Molecular signatures of long-term hepatocellular carcinoma risk in non-alcoholic fatty liver disease

Abstract

Disclosures herein are directed to methods and compositions for predicting high- and low-risk for hepatocellular carcinoma in patients with non-alcoholic fatty liver disease (NAFLD). Provided herein are methods of identifying the NAFLD as indolent-, progressive, or advanced-NAFLD. Based on the results achieved from the methods and compositions disclosed herein, non-alcoholic fatty liver disease patients can be classified into a prognostic risk group, which enables early diagnosis and prevention of hepatocellular carcinoma and other lethal complications. Methods and compositions disclosed herein substantially improve the poor prognosis of subjects having or at risk for hepatocellular carcinoma.

Description

MOLECULAR SIGNATURES OF LONG-TERM HEPATOCELLULAR CARCINOMA RISK IN NON-ALCOHOLIC FATTY LIVER DISEASECROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 509,362, filed June 21 , 2023, and titled “MOLECULAR SIGNATURES OF LONG-TERM HEPATOCELLULAR CARCINOMA RISK IN NON-ALCOHOLIC FATTY LIVER DISEASE,” which is incorporated by reference herein in its entirety.ACKNOWLEDGEMENT OF GOVERNMENT SUPPORT

[0002] This invention was made with government support under Grant No. CA233749 awarded by the National Institutes of Health. The government has certain rights in this invention.BACKGROUNDField

[0003] The present disclosure is directed to methods of determining a prognostic liver signature (PLS) and prognostic liver secretome signature (PLSec) specific for non-alcoholic fatty liver patients (e.g., PLS-NAFLD and PLSec-NAFLD) for use in prediction of risk for developing liver cancer (e.g., hepatocellular carcinoma (HCC)) and lethal liver disease complications (e.g., hepatic decompensation, fibrosis progression) in a subject, as well as identifying the NAFLD as indolent-, progressive, or advanced-NAFLD.Discussion of Related Art

[0004] Accurate prediction of risk for developing cancers (especially HCC) and lethal complications is a critical step in the management of subjects having chronic liver diseases. The prediction of HCC and other prognostic risk and / or outcome of treatment aiming at reducing the risk will provide critical information to subjects and treating physicians, both at the time of selection of the caring / treatment strategy and after the application of therapy. For subjects diagnosed with chronic liver diseases, identification of a subset of patients at elevated risk of HCC and other lethal complications is critical for diagnosis of these life-threatening medical problems at early stage where curative treatment options are still available. This is important especially because of the vast size of the liver disease patient population. For example, one of several causes of chronic liver diseases, non-alcoholic fatty liver disease (NAFLD) affects 60-80 million Americans, which is already far beyond the capability of existing health care facilities across the nation. Indeed, only <25% of HCC patients are diagnosed at early stages, and consequently, HCC prognosis is extremely poor (5-year survival rate <15%) compared to other cancer types such as colon (5-year survival rate 58%), breast (5-yearsurvival rate 86%), and prostate (5-year survival rate 88%) cancers. Thus, identification at- risk patients will significantly improve efficiency of diagnosing early-stage disease followed by curative treatment. In addition, the high-risk patients can be subjected to therapies to reduce the risk level to prevent future development of HCC and other lethal complications such as hepatic decompensation. Unfortunately, predictive algorithms for risk of disease progression based on typical clinical risk factors, such as age, sex, and degree of liver dysfunction, have suboptimal performance when used in the clinic. Further, there are no available algorithms tailored to the non-alcoholic fatty liver disease population, which may have different risk factors and etiology than other groups at risk for liver cancer and liver complications. As such, there is a need in the art for improved methods that accurately determine the risk of liver disease progression toward HCC and the potential for lethal complications in non-alcoholic fatty liver disease patients.SUMMARY

[0005] The present disclosure is based, in part, on the novel finding that determining the abundance of proteins in a biological sample obtained from a subject can be used to generate a PLSec-NAFLD score for use in prediction of cancer risk, detection, and treatment of liver cancer (e.g., HCC) in a subject having or suspected of having non-alcoholic fatty liver disease (NAFLD). Accordingly, provided herein are methods and kits for measuring protein abundance of a panel of circulating proteins, determining a PLSec-NAFLD score, and treating high- and low-risk liver cancer subjects according to their PLSec-NAFLD score. Additional aspects of the present disclosure are based, in part, on the novel finding that evaluating a gene expression profile of a biological sample obtained from a sample can be used to generate a PLS-NAFLD score for use in prediction of liver cancer risk. Accordingly, provided herein are methods and kits for evaluating gene expression in a patient sample, determining a PLS- NAFLD score, and treating high- and low-risk liver cancer subjects according to their PLS- NAFLD score.

[0006] In some embodiments, methods of predicting a risk for developing hepatocellular carcinoma (HCC) in a subject comprise determining a non-alcoholic fatty liver disease associated prognostic liver secretome signature (PLSec-NAFLD) score for the subject, wherein the method of obtaining the PLSec-NAFLD score comprises: (a) obtaining a blood sample from the subject; (b) subjecting the sample to a multi-analyte profiling assay for protein quantification of lymphotactin, progranulin, angiopoietin 2, and / or hepatocyte growth factor receptor; (c) normalizing protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor to median fluorescent intensity; and (d) converting the normalized protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and and / or hepatocyte growth factor receptor into an aggregatedscore wherein the aggregated score is the PLSec-NAFLD score. In some aspects, a subject disclosed herein may be predicted to be at low risk of developing HCC if the PLSec-NAFLD score is less than or equal to 1. In some embodiments, the subject may be predicted to be at high risk for developing HCC if the PLSec-NAFLD score is greater than 1.

[0007] Other aspects of the present disclosure provide methods of determining a nonalcoholic fatty liver disease associated prognostic liver secretome signature (PLSec-NAFLD) score in a subject, the method comprising (a) obtaining a blood sample from the subject; (b) subjecting the sample to a multi-analyte profiling assay for protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor to median fluorescent intensity; (c) normalizing the protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor to median fluorescent intensity; and (d) converting the normalized protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor into an aggregated score, wherein the aggregated score is the PLSec- NAFLD score. In various aspects, the subject has or is suspected of having non-alcoholic fatty liver disease (NAFLD). In various aspects, the subject is predicted to be at low risk for developing HCC if the PLSec-NAFLD score is less than or equal to 1. In some aspects, the subject is predicted to be at high risk for developing HCC if the PLSec-NAFLD score is greater than 1.

[0008] In various embodiments, the methods herein may further comprise deriving a PLSec- AFP score in the subject based on circulating levels of alpha-fetoprotein (AFP) and at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S in the subject, wherein the PLSec- AFP score is either a “high-risk” PLSec-AFP score or a “low-risk” PLSec-AFP score. In various embodiments, a subject disclosed herein may be (a) predicted to be at high risk for developing HCC if a PLSec-NAFLD score determined as provided herein is greater than 1 and the PLSec- AFP score is “high risk”; (b) predicted to be at intermediate risk for developing HCC if a PLSec- NAFLD score determined herein is greater than 1 and the PLSec-AFP score is “low risk”; (c) predicted to be at intermediate risk for developing HCC if the PLSec-NAFLD score as determined herein is less than 1 and the PLSec-AFP score is “high risk”; or (d) predicted to be at low risk for developing HCC if the PLSec-NAFLD score as determined herein is less than 1 and the PLSec-AFP score is “low risk”.

[0009] Further aspects of the present disclosure are directed to methods of predicting a risk for developing hepatocellular carcinoma (HCC) in a subject having or suspected of having non-alcoholic fatty liver disease by determining a PLS-NAFLD score in the subject. In variousaspects, the PLS-NAFLD score is determined by a method comprising: (a) obtaining a tissue sample from the subject; (b) subjecting the tissue sample to a multi-analyte profiling assay for gene expression of one or more genes selected from ACRBP, LISB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof, to obtain a gene expression measurement for each of the one or more genes; (c) normalizing the gene expression measurements of ACRBP, LISB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a control set of genes to obtain a gene expression profile of the subject; and (d) converting the gene expression profile of the subject into a PLS-NAFLD score, wherein the PLS-NAFLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low- risk reference gene expression profile. In some aspects, the subject is predicted to be at low risk for hepatocellular carcinoma if the PLS-NAFLD score is less than -1.3013. In someaspects, the subject is predicted to be at high or intermediate risk for HCC if the PLS-NAFLD score is greater than or equal to -1.3013.

[0010] Further aspects of the present disclosure provide a method of determining a nonalcoholic fatty liver disease associated prognostic liver signature (PLS-NAFLD) score for a subject comprising: (a) obtaining a liver biopsy sample from the subject; (b) subjecting the sample to a multi-analyte profiling assay for gene expression of one or more genes selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof, to obtain a gene expression measurement for each of the one or more genes; (c) normalizing the gene expression measurements of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a control set of genes to obtain a gene expression profile of the subject;and (d) converting the gene expression profile of the subject into a PLS-NAFLD score, wherein the PLS-NAFLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile. In some aspects, the subject is predicted to be at low risk for hepatocellular carcinoma if the PLS-NAFLD score is less than -1.3013. In various aspects, the subject is predicted to be at high or intermediate risk for hepatocellular carcinoma if the PLS-NAFLD score is greater than or equal to -1.3013. In various aspects, the subject has or is suspected of having non-alcohol fatty liver disease (NAFLD).

[0011] In any of the preceding or foregoing embodiments, the methods may further comprise detecting and / or diagnosing HCC in the subject predicted to be at a high risk for HCC. In some embodiments, the subject is diagnosed with HCC and / or HCC is detected at an earlier stage compared to a diagnosis made via semi-annual HCC screening. In various aspects, the method of detecting and / or diagnosing HCC in the subject comprises performing a liver biopsy, one or more blood tests to assess liver function, measuring levels of alpha-fetoprotein in blood, computed tomography, magnetic resonance imaging, or a combination thereof. In some aspects, the one or more blood tests performed to assess liver function comprises a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof.

[0012] In any of the preceding or foregoing embodiments, the methods may further comprise administering one or more prophylactic therapies and / or treatments of HCC to the subject predicted to be at a high risk for HCC.

[0013] Further aspects of the present disclosure are directed to methods of preventing and / or treating hepatocellular carcinoma (HCC) in a subject at high risk for developing HCC, the method comprising: (a) determining if the subject is at high risk for developing HCC by a method comprising: (i) obtaining a blood sample from the subject; (ii) determining protein levels of at least two liver disease biomarkers in the sample wherein, one of the at least two liver disease biomarkers is selected from lymphotactin or progranulin; and the other one of the at least two liver disease biomarkers is selected from angiopoietin 2 or hepatocyte growth factor receptor; (iii) converting the protein levels of the at least two disease biomarkers to an aggregated PLSec-NAFLD score based on levels of the at least two disease biomarkers in a control sample, wherein the control is a blood sample from a subject known to not have any liver disease; (iv) determining that the subject is at high risk for developing HCC if the aggregated PLSec-NAFLD score is greater than 1 ; and (b) administering one or more prophylactic therapies and / or treatments of HCC to the subject determined to be at high risk for developing HCC.

[0014] In various aspects, the subject can be at high risk for developing HCC if any one of lymphotactin and progranulin has a higher protein expression compared to the control and / or any one of angiopoietin 2 and hepatocyte growth factor receptor has equivalent or lower protein expression compared to the control. For example, the subject can be at high risk for developing HCC if lymphotactin and progranulin have a higher protein expression compared to the control and / or angiopoietin 2 and hepatocyte growth factor receptor have equivalent or lower protein expression compared to the control.

[0015] In various aspects, the method of preventing and / or treating hepatocellular carcinoma (HCC) may further comprise deriving a PLSec-AFP score in the subject based on circulating levels of alpha-fetoprotein (AFP) and at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S in the subject, wherein the PLSec-AFP score is either a “high-risk” PLSec-AFP score or a “low-risk” PLSec-AFP score, and wherein the subject is at high risk for developing HCC if the aggregated PLSec-NAFLD score is greater than 1 and the PLSec-AFP score is a “high-risk” PLSec-AFP score. In an embodiment, the protein levels of the at least two liver disease biomarkers, alpha-fetoprotein (AFP), and / or at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S can be determined by one or more methods selected the group consisting of Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling assay, mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two-dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), and combinations thereof. In an embodiment, the protein level of the at least two liver disease biomarkers, alpha-fetoprotein (AFP), and / or at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S are determined by ELISA or multi-analyte profiling assay.

[0016] Additional methods of preventing and / or treating hepatocellular carcinoma (HCC) in a subject with non-alcoholic fatty liver disease at high risk for HCC are provided, the methods comprising: (a) determining if the subject with non-alcoholic fatty liver disease is at high risk for hepatocellular carcinoma by a method comprising: (i) obtaining a liver biopsy sample from the subject; (ii) subjecting the liver biopsy sample to a multi-analyte profiling assay forgene expression of one or more genes selected from ACRBP, LISB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof to obtain a gene expression measurement of each of the one or more genes; (iii) normalizing the gene expression measurements of ACRBP, LISB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a control set of genes to obtain a gene expression profile of the subject; (iv) converting the gene expression profile of the subject into a PLS- NAFLD score, wherein the PLS-NAFLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile; and (v) determining that the subject is at high or intermediate risk for hepatocellular carcinoma if the PLS-NAFLD score is greater than or equal to -1.3013, and (b) administering one or more prophylactic therapiesand / or treatments of HCC to the subject determined to be at high or intermediate risk for developing hepatocellular carcinoma.

[0017] Further aspects of the present disclosure are directed to methods of monitoring the effectiveness of a prophylactic therapy or treatment for HCC by monitoring PLSec-NAFLD and / or PLS-NAFLD scores in a subject before and after the treatment.

[0018] Accordingly, in some aspects, a method of monitoring outcome of one or more prophylactic therapies and / or treatments of HCC in a subject having or suspected of having non-alcoholic fatty liver disease, the method comprising: (a) determining a first PLSec-NAFLD score for a subject having or suspected of having non-alcoholic fatty liver disease by a method comprising: (i) obtaining a blood sample from the subject; (ii) subjecting the sample to a multianalyte profiling assay for protein quantification measurements of lymphotactin, progranulin, angiopoietin 2, and / or hepatocyte growth factor receptor to median fluorescent intensity; (iii) normalizing the protein quantification measurements of lymphotactin, progranulin, angiopoietin 2, and / or hepatocyte growth factor receptor to median fluorescent intensity; and (iv) converting the normalized protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor into an aggregated score, wherein the aggregated score is the PLSec-NAFLD score; (b) administering one or more prophylactic therapies and / or treatments of HCC to the subject; (c) repeating step (a) to obtain a second PLSec-NAFLD score; and (d) monitoring an outcome of the one or more prophylactic therapies and / or treatments of HCC administered in (b) based on differences between the second PLSec-NAFLD score in (c) and the first PLSec-NAFLD score in (a). In various aspects, the one or more prophylactic therapies and / or treatments of HCC administered in (b) has a positive outcome when the first PLSec-NAFLD score is greater than the second PLSec- NAFLD score. In some aspects, the one or more prophylactic therapies and / or treatments of HCC administered in (b) has a negative outcome when the first PLSec-NAFLD score in less than or equal to the second PLSec-NAFLD score.

[0019] In further embodiments, step (a) further comprises obtaining a first PLSec-AFP score and comparing to the first PLS-NAFLD score to derive a first etPLSec-NAFLD score and (c) further comprises obtaining a second PLSec-AFP score and comparing to the second PLSec- NAFLD score to derive a second etPLSec-NAFLD score; wherein the first and second PLSec- AFP score are derived from circulating levels of alpha-fetoprotein (AFP) and at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factorbinding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S in the subject, wherein the effectiveness of the one or more prophylactic therapies and / or treatments of HCC is evaluated by comparing the first and second etPLSec-NAFLD scores.

[0020] In some other aspects, a method of monitoring outcome of a prophylactic therapy and / or treatment for HCC in a subject having or suspected of having non-alcoholic fatty liver disease, can comprise: (a) determining a first PLS-NAFLD score for a subject having or suspected of having non-alcoholic fatty liver disease by a method comprising: (i) obtaining a liver biopsy sample from the subject; (ii) subjecting the liver biopsy sample to a multi-analyte profiling assay for gene expression of one or more genes selected from ACRBP, LISB1 , HLA- A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof to obtain a gene expression measurement of each of the one or more genes; (iii) normalizing the gene expression measurements of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ER11 , SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a control set of genes to obtain a gene expression profile of the subject; and (iv) converting the gene expression profile of the subject into a PLS-NAFLD score, wherein the PLS-NAFLD score is a numerical valuecorresponding to a similarity between the gene expression profile of the subject and a high- risk reference gene expression profile or a low-risk reference gene expression profile; (b) administering one or more prophylactic therapies and / or treatments of HCC to the subject; and (c) repeating step (a) to determine a second PLS-NAFLD score; and (d) monitoring an outcome of the one or more prophylactic therapies and / or treatments of HCC administered in (b) based on differences between the second PLS-NAFLD score in (c) and the first PLS- NAFLD score in (a). In some aspects, the one or more treatments of HCC administered in (b) has a positive outcome when the first PLS-NAFLD score in greater than the second PLS- NAFLD score. In various aspects, the one or more treatments of HCC administered in (b) has a negative outcome when the first PLS-NAFLD score in less than or equal to the second PLS- NAFLD score.

[0021] In any of the foregoing or related aspects, the one or more prophylactic therapies and / or treatments of HCC can comprise surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof. In some aspects, the drug therapy comprises administration of one or more drugs to the subject, wherein the drugs are comprised of sorafenib, regorafenib, nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof. In some aspects, the chemopreventative agent comprises an antiviral, a statin, an anti-diabetic, a dietary and / or nutritional agent, an anti-inflammatory, an immunomodulatory, or a combination thereof. In further aspects, the chemopreventative agent is selected from tenofovir disoproxil fumarate, simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin, metformin, polyunsaturated fatty acids (PUFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), Branched-chain amino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe), celecoxib, aspirin thalidomide, thymalfasin, or a combination thereof.

[0022] Further aspects of the present disclosure provide methods of excluding a subject from a prophylactic therapy for hepatocellular carcinoma, the method comprising determining a PLS-NAFLD score as described herein and excluding the subject from the prophylactic therapy when the PLS-NAFLD score is less than -1.3013. In some aspects, the prophylactic therapy comprises a chemopreventative agent selected from an antiviral, a statin, an antidiabetic, a dietary and / or nutritional agent, an anti-inflammatory, an immunomodulatory, or a combination thereof. In some aspects, the chemopreventative agent is selected from tenofovir disoproxil fumarate, simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin, metformin, polyunsaturated fatty acids (PUFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), Branched-chain amino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe), celecoxib, aspirin thalidomide, thymalfasin, or a combination thereof.

[0023] Further aspects of the present disclosure are directed to diagnostic kits for determining either non-alcoholic fatty liver disease associated prognostic liver secretome signature (PLSec-NAFLD) score or a non-alcoholic fatty liver disease associated prognostic liver signature (PLS-NAFLD) score, where the kit comprise one or more reagents for use in a multianalyze profiling assay.

[0024] In various aspects, in diagnostic kits for determining a PLSec-NAFLD score, the one or more reagents may comprise beads labeled with antibodies to lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor. In some aspects, the kits may further comprise one or more reagents to measure levels of alpha-fetoprotein (AFP), vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, and / or protein S in a sample to determine a PLSec-AFP score of the subject.

[0025] In various aspects, in diagnostic kits for determining a PLS-NAFLD score, the one or more reagents may comprise one or more nucleic acid probes labeled with color-coded microbeads to mRNA transcribed from one or more genes selected from ACRBP, USB1 , HLA- A, TPH1 , ADAMTSL1 , B3GNTL1 , PHGR1 , DYM, PIK3IP1 , DTD1 , ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1 , MPV17L2, ABCC5, VAV1 , KIFC2, ATP6V0B, RNF166, HIST1 H2BI, MFSD11 , PIM1 , SLA2, EHD2, ERCC4, SKAP1 , EP400, ABI3, NAT10, STX6, BCL2L1 , LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1 , SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1 , CD109, BTN3A1 , USP5, RAB4B- EGLN2, MLX, SLC4A10, TAF5L, IPCEF1 , MIA-RAB4B, RAB40C, TCIRG1 , PRF1 , COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1 , HDC, ARF3, WBSCR16, PRR5L, HIST1 H2BK, PNKD, CCDC88C, PPP1 R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1 , NPPA, TLE2, MKLN1 , DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1 , BDH2, ZFAND5, PPP1 R1C, NSUN6, CSNK2A2, DDX59, PGAP1 , BAZ2B, ETNK1 , TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111 , PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1 , PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1 , CTBP2, C16orf87, TMEM184C, PPFIBP1 , STAU2, PLD1 , RSBN1 , FAF1 , KIAA1217, or a combination thereof.BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure, which can be better understood byreference to the drawing in combination with the detailed description of specific embodiments presented herein.

[0027] FIG. 1 depicts and overview of the study design to derive and validate HCC risk signature. HCC, hepatocellular carcinoma; IQR, interquartile range; PLS, prognostic liver signature; NAFLD, non-alcoholic fatty liver disease; PLSec, prognostic liver secretome signature.

[0028] FIG. 2A depicts expression pattern of PLS-NAFLD genes and clinico-histological and genetic features in a derivation set in accordance with various aspects of the present disclosure.

[0029] FIG. 2B is a plot depicting prognostic association of PLS-NAFLD.

[0030] FIG. 2C depicts clusters of human-liver-derived single cells from meta-analysis of four scRNA-seq datasets, representing healthy to NAFLD-affected livers.

[0031] FIG. 2D depicts induction of high- and low-risk PLS-NAFLD genes measured by the average relative expression (as “score”) across the human hepatic single cell clusters.

[0032] FIG. 2E depicts H&E staining (upper panel) and histological architecture determination over the grid-like “spots” for the spatial transcriptome profiling (lower panel) of liver tissue from a NAFLD patient.

[0033] FIG. 2F is a plot of Induction of high- and low-risk PLS-NAFLD genes measured by the “score” across the four histological architectures.

[0034] FIG. 2G depicts the correlation of the high- and low-risk PLS-NAFLD scores with relative abundance of the hepatic single cell clusters across the four histological architectures.

[0035] FIG. 2H depicts the number of inferred cell-cell interactions between the five hepatic single cell clusters co-presenting in the portal tracts and contributing to the high-risk PLS- NAFLD induction.

[0036] FIG. 2I depicts a representative portal tract (outlined by dotted line) with the high-risk PLS-NAFLD induction determined by the spatial transcriptome profiling (right upper panel), where IDO1+ eDCs (left lower panel) and PD-1+ CD8 T cells (right lower panel) are colocalized in close proximity.

[0037] FIG. 3A depicts an overview of tissue sampling and clinical follow-up in the tissue validation set 1.

[0038] FIG. 3B depicts the expression pattern of the PLS-NAFLD genes and clinico- histological and genetic features in the tissue validation set 1. For the SNPs, homozygous orheterozygous presence of risk alleles is shown for PNPLA3, TM6SF2, and GCKR, and presence of protective allele is shown for HSD17B13 and MTARC1.

[0039] FIG. 3C depicts prognostic association of PLS-NAFLD with incident HOC in the tissue validation set 1.

[0040] FIG. 3D depicts longitudinal changes in the PLS-NAFLD-based HOC risk level measured by combined enrichment score (CES) and clinico-histological features and laboratory tests between the serial biopsies.

[0041] FIG. 3E depicts prognostic association of improved PLS-NAFLD.

[0042] FIG. 3F depicts time-dependent AUROC of improved PLS-NAFLD and regressed fibrosis (i.e. , decrease of F-stage).

[0043] FIG. 3G depicts the expression pattern of the PLS-NAFLD genes with clinico- histological and genetic features in the tissue validation set 2.

[0044] FIG. 3H depicts the prognostic association of PLS-NAFLD in the tissue validation set 2.

[0045] FIG. 4A depicts the pattern of PLSec-NAFLD protein abundance in HCC-naTve patients with NAFLD cirrhosis.

[0046] FIG. 4B depicts the prognostic association of high-risk PLSec-NAFLD in the serum validation set.

[0047] FIG. 4C is a calibration plot of PLSec-NAFLD at 5, 10, and 15 years. The diagonal dotted line indicates ideal calibration.

[0048] FIG. 4D depicts the prognostic association of etPLSec-NAFLD.

[0049] FIG. 4E is a plot of time-dependent AUROC of etPLSec-NAFLD, PLSec-AFP, and PLSec-NAFLD.

[0050] FIG. 5A depicts a study design to assess PLS-NAFLD modulation by bariatric surgery in NAFLD patients under lifestyle intervention.

[0051] FIG. 5B depicts a proportion of patients with significantly improved PLS-NAFLD with bariatric surgery in NAFLD patients under lifestyle intervention.

[0052] FIG. 5C depicts a study design to assess association of PLS-NAFLD status with statin use in patients who underwent bariatric surgery.

[0053] FIG. 5D depicts an association of PLS-NAFLD-based HCC risk prediction with lipophilic or hydrophilic statins use.

[0054] FIG. 5E depicts an experimental design to assess the effect of an IDO1 inhibitor, epacadostat, in our PLS-inducible cell culture model (cPLS system).

[0055] FIG. 5F depicts the modulation of free-fatty-acid-induced high-risk pattern of PLS- NAFLD with epacadostat in the cPLS system.

[0056] FIG. 6A depicts the required sample size to detect expected hazard ratios (2.0, 2.5, and 3.0) in Cox regression as statistically significant according to proportion of patients who develop an event during follow-up (at statistical power > 0.80; type I error rate = 0.05).

[0057] FIG. 6B depicts a hazard of HCC recurrence over time after curative HCC treatment in the PLS-NAFLD derivation set (left) and a publicly available HCC cohort (GSE14520) (right).

[0058] FIG. 6C depicts a prognostic association of PLS in a derivation set.

[0059] FIG. 6D depicts a prognostic association of PLS in a first the tissue validation set.

[0060] FIG. 6E depicts a prognostic association of PLS in a second tissue validation set.

[0061] FIG. 7A depicts a workflow of PLS-NAFLD identification.

[0062] FIG. 7B depicts a prognostic association of PLS-NAFLD based on three-class risk stratification.

[0063] FIG. 7C depicts association of PLS-NAFLD with clinical biochemical tests, platelet count, and liver stiffness measurement by transient elastography.

[0064] FIG. 7D depicts association of PLS-NAFLD with NAFLD-related histological features.

[0065] FIG. 7E depicts association of PLS-NAFLD with NAFLD-related SNPs. Red and blue vertical bars indicate NAFLD-promoting and -protective SNPs, respectively.

[0066] FIG. 7F depicts prognostic associations of PLS-NAFLD in cohorts from viral etiologies.

[0067] FIG. 7G depicts prognostic associations of PLS-NAFLD in cohorts from viral etiologies.

[0068] FIG. 7H depicts association between presence of cirrhosis and high-risk PLS-NAFLD in patients with alcohol-related liver disease.

[0069] FIG. 7I depicts molecular pathways associated with induced or suppressed PLS- NAFLD.

[0070] FIG. 8A depicts cell type annotation for the single-cell clusters (SC01 to SC31) derived from the meta-analysis of hepatic single-cell transcriptome datasets.

[0071] FIG. 8B depicts mapping of the datasets used to construct a hepatic single-cell atlas.

[0072] FIG. 8C depicts distribution of cell types associated with NAFLD pathogenesis in literature.

[0073] FIG. 8D depicts induction of high- and low-risk PLS-NAFLD scores in the mononuclear phagocyte clusters.

[0074] FIG. 8E depicts Mapping of mono-nuclear phagocyte subtypes from (33).

[0075] FIG. 8F depicts distribution of eDC markers in the mono-nuclear phagocyte clusters.

[0076] FIG. 8G depicts Distribution of / DO7-expressing cells.

[0077] FIG. 9A depicts quality control and preprocessing of genome-wide spatial transcriptome profiling on NAFLD-affected liver.

[0078] FIG. 9B depicts an algorithm to determine histological annotation for each of the gridlike regions (spots) in the spatial transcriptome profiling assay based on spatial proximity to the landmark vascular structures (portal tract and central vein) in the liver.

[0079] FIG. 9C depicts an induction of molecular signatures and marker genes of metabolic zonation in the liver by the four histological architectures, namely portal tract, peri-portal (zone 1), mid-lobular (zone 2), and peri-central (zone 3) regions.

[0080] FIG. 9D depicts a proportion of the portal-tract- and high-risk-PLS-NAFLD- associated single-cell clusters in each of the four histological architectures.

[0081] FIG. 9E depicts a proportion of the spots by co-presence of the portal-tract-associated single-cell clusters in each of the four histological architectures.

[0082] FIG. 9F depicts inferred inter-cell-type interactions between the five portaltract-and high-risk-PLS-NAFLD-associated single-cell clusters.

[0083] FIG. 10A depicts a workflow of derivation and optimization of PLSec-NAFLD. Red and blue genes / proteins indicate high- and low-risk-associated genes / proteins, respectively.

[0084] FIG. 10B depicts determination of cut-off value to define high-risk PLSec-NAFLD.

[0085] FIG. 10C depicts prognostic association of the PLSec-AFP in the serum validation set.

[0086] FIG. 10D depicts association between PLSec-AFP- and PLSec-NAFLD-based prognostic prediction in the serum validation set.

[0087] FIG. 11A depicts the strategy design for an integrative hepatic transcriptome metaanalysis of five multi-regional / racial / ethnic derivation cohorts.

[0088] FIG. 11B depicts reproducible subtypes 1 , 2, and 3 across the cohorts of the integrative hepatic transcriptome meta-analysis.

[0089] FIG. 11C depicts an analysis of progression and regression of histological fibrosis for a-MASLD and p-MASLD versus i-MASLD.

[0090] FIG. 11D depicts HCC incidence rate across i-, p-, and a-MASLD subtypes.

[0091] FIG. 11E depicts heatmaps showing MASLD subtyping using nCounter-based and RNA-seq-based gene panels for i-, p-, and a-MASLD.

[0092] FIG. 11 F depicts RNA-seq-based MASLD subtyping of a subset of the derivation set using the nCounter Sprint assay. A 30-gene signature (e.g., TABLE 16) reproduced the original MASLD subtyping in the validation set (consistency, 79%; p <0.001).

[0093] FIG. 12A depicts the strategy design for validation of MASLD subtype.

[0094] FIG. 12B depicts principal component analysis of the derivation and validation cohorts before and after batch correction.

[0095] FIG. 12C depicts cumulative distribution function curve for consensus clustering for MASLD subtyping.

[0096] FIG. 12D depicts A area under the cumulative distribution function curve for the consensus clustering for MASLD subtyping.

[0097] FIG. 12E depicts the consensus matrix for MASLD subtyping.

[0098] FIG. 13A depicts external validation of clinico-histological associations in an integrative hepatic transcriptome meta-analysis of 5 independent cohorts of 556 MASLD patients using publicly available RNA-seq data.

[0099] FIG. 13B depicts strategy for reducing the number of signature genes for implementation in a clinical diagnostic platform.

[0100] FIG. 13C depicts subtyping consistency with the full signature set.

[0101] FIG. 14A depicts strategy for developing a non-invasive assay for performing MASLD subtyping, using blood based MASLD secretome signatures developed using the TexSEC algorithm. Secretome signature protein optimization included correlations with RNA-based subtyping, and a 1,000 iterative feature selection.

[0102] FIG. 14B shows the reliability of secretome proteins (8 a-MASLD proteins, 3 p-MASLD proteins, and 4 i-MASLD signature proteins) reflecting tissue-based subtyping.

[0103] FIG. 15A depicts sensitivity and specificity of blood-based MASLD subtyping with RNA-based MASLD subtyping for a-MASLD and rest.

[0104] FIG. 15B depicts sensitivity and specificity of blood-based MASLD subtyping with RNA-based MASLD subtyping for p-MASLD and i-MASLD.

[0105] FIG. 16A depicts validation of paired NanoString-based MASLD subtyping with a set of 44 patients for a-MASLD identification. Overall consistency of serum-based subtyping was 74% (first-step, 95%; second-step, 71%).

[0106] FIG. 16B depicts validation of paired NanoString-based MASLD subtyping with a set of 44 patients for p- and i-MASLD separation. Overall consistency of serum-based subtyping was 74% (first-step, 95%; second-step, 71 %).

[0107] FIG. 17A depicts prognostic associations of serum based MASLD subtyping. Relative fibrosis-4 (FIB-4) index change is plotted as a function of years from the index of biopsy.

[0108] FIG. 17B depicts HOC development observed in a-MASLD, i-MASLD, and p-MASLD subjects.

[0109] The drawing figures do not limit the present disclosure to the specific embodiments disclosed and described herein. The drawings are not necessarily to scale, emphasis instead being placed on clearly illustrating principles of certain embodiments of the present disclosure.DETAILED DESCRIPTION

[0110] The following detailed description references the accompanying drawings that illustrate various embodiments of the present disclosure. The drawings and description are intended to describe aspects and embodiments of the present disclosure in sufficient detail to enable those skilled in the art to practice the present disclosure. Other components can be utilized, and changes can be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0111] The present disclosure is based, in part, on the novel finding that determining a gene expression profile or protein abundance levels in in a biological sample obtained from a subject having or suspected of having non-alcoholic fatty liver disease can be used to generate a PLS- NAFLD score and / or a PLSec-NAFLD score for use in a targeted risk prediction of hepatocellular carcinoma in the subject. Accordingly, provided herein are methods for determining gene expression in a tissue and / or measuring protein abundance of a panel of circulating proteins, determining a PLS-NAFLD and / or PLSec-NAFLD score, and treating patients at high or low risk of developing hepatocellular carcinoma according to their PLS- NAFLD and / or PLSec-NAFLD score. Kits used in practicing the methods disclosed herein are also provided in the present disclosure.I. Terminology

[0112] The phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting. For example, the use of a singular term, such as, “a” is not intended as limiting of the number of items. Also, the use of relational terms such as,but not limited to, “top,” “bottom,” “left,” “right,” “upper,” “lower,” “down,” “up,” and “side,” are used in the description for clarity in specific reference to the figures and are not intended to limit the scope of the present disclosure or the appended claims.

[0113] Further, as the present disclosure is susceptible to embodiments of many different forms, it is intended that the present disclosure be considered as an example of the principles of the present disclosure and not intended to limit the present disclosure to the specific embodiments shown and described. Any one of the features of the present disclosure may be used separately or in combination with any other feature. References to the terms “embodiment,” “embodiments,” and / or the like in the description mean that the feature and / or features being referred to are included in, at least, one aspect of the description. Separate references to the terms “embodiment,” “embodiments,” and / or the like in the description do not necessarily refer to the same embodiment and are also not mutually exclusive unless so stated and / or except as will be readily apparent to those skilled in the art from the description. For example, a feature, structure, process, step, action, or the like described in one embodiment may also be included in other embodiments but is not necessarily included. Thus, the present disclosure may include a variety of combinations and / or integrations of the embodiments described herein. Additionally, all aspects of the present disclosure, as described herein, are not essential for its practice. Likewise, other systems, methods, features, and advantages of the present disclosure will be, or become, apparent to one with skill in the art upon examination of the figures and the description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the present disclosure, and be encompassed by the claims.

[0114] As used herein, the term “about,” can mean relative to the recited value, e.g., amount, dose, temperature, time, percentage, etc., ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, or ±1%.

[0115] The terms "comprising," "including," “encompassing” and "having" are used interchangeably in this disclosure. The terms "comprising," "including," “encompassing” and "having" mean to include, but not necessarily be limited to the things so described.

[0116] The terms “or” and “and / or,” as used herein, are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and / or C” mean any of the following: “A,” “B” or “C”; “A and B”; “A and C”; “B and C”; “A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

[0117] "Biomarker" as used herein refers to any biological molecules (e.g., nucleic acids, genes, peptides, proteins, lipids, hormones, metabolites, and the like) that, singularly orcollectively, reflect the current or predict future state of a biological system. Thus, as used herein, the presence or concentration of one or more biomarkers can be detected and correlated with a known condition, such as a disease state. In some aspects, detecting the presence and / or concentration of one or more biomarkers herein may be an indication of a liver cancer risk in a subject. In some other aspects, detecting the presence and / or concentration of one or more biomarkers herein may be used in treating and / or preventing a liver cancer in a subject.

[0118] As used herein, the terms “treat”, “treating”, “treatment” and the like, unless otherwise indicated, can refer to reversing, alleviating, inhibiting the process of, or preventing the disease, disorder or condition to which such term applies, or one or more symptoms of such disease, disorder or condition and includes the administration of any of the compositions, pharmaceutical compositions, or dosage forms described herein, to prevent the onset of the symptoms or the complications, or alleviating the symptoms or the complications, or eliminating the condition, or disorder.

[0119] The term “biomolecule” as used herein refers to, but is not limited to, proteins, enzymes, antibodies, DNA, siRNA, and small molecules. “Small molecules” as used herein can refer to chemicals, compounds, drugs, and the like.

[0120] The term “nucleic acid” or “polynucleotide” refers to deoxyribonucleic acids (DNA) or ribonucleic acids (RNA) and polymers thereof in either single- or double-stranded form. Unless specifically limited, the term encompasses nucleic acids containing known analogues of natural nucleotides that have similar binding properties as the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise indicated, a particular nucleic acid sequence also implicitly encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions), alleles, orthologs, SNPs, and complementary sequences as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions may be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and / or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); and Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)).

[0121] The terms “peptide,” “polypeptide,” and “protein” are used interchangeably, and refer to a compound comprised of amino acid residues covalently linked by peptide bonds. A protein or peptide must contain at least two amino acids, and no limitation is placed on the maximum number of amino acids that can comprise a protein's or peptide's sequence. Polypeptides include any peptide or protein comprising two or more amino acids joined to each other by peptide bonds. As used herein, the term refers to both short chains, which also commonlyare referred to in the art as peptides, oligopeptides and oligomers, for example, and to longer chains, which generally are referred to in the art as proteins, of which there are many types. “Polypeptides” include, for example, biologically active fragments, substantially homologous polypeptides, oligopeptides, homodimers, heterodimers, variants of polypeptides, modified polypeptides, derivatives, analogs, fusion proteins, among others. A polypeptide includes a natural peptide, a recombinant peptide, or a combination thereof.

[0122] As used herein, the term “non-alcoholic fatty liver disease” or “NAFLD” are used interchangeably to refer to a collection of medical conditions where fat accumulates in the liver without corresponding heavy alcohol use. NAFLD are sub-categorized into non-alcoholic fatty liver (NAFL), which is characterized by the fat accumulation without liver cell damage, and non-alcoholic steatohepatitis (NASH), which is characterized by the fat accumulation and liver cell damage. As used herein, the term “non-alcoholic fatty liver disease” or “NAFLD” encompass all stages of NAFLD and therefore include NAFL and NASH. NAFLD may be the presence of fatty liver disease (FLD) in the absence of known causes of steatosis, with an emphasis on alcohol, and has been recognized as the second cause of FLD. Notably, NAFLD has more recently been referred to as metabolic dysfunction-associated steatotic liver disease (MASLD) (see e.g., Lee et al. Hepatology, 2024, 79(3):666-673). MASLD may be the presence of FLD concomitantly with the presence of overweight or obesity and / or type 2 diabetes mellitus (T2DM) (D. Garcia-Compean and A.R. Jimenez-Rodriguez, Annals of Hepatology, 27, 100765 (2022); S. Pouwels et al., BMC Endocr Disord 22, 63 (2022)). For the purposes of this disclosure, the terms NAFLD and MASLD are considered equally as the same indication and the terms may be used interchangeably in any embodiment of an invention described herein (e.g., such as in any method, kit, or other invention as described herein), and also when characterizing a subject as having or being suspected of having NAFLD / MASLD. For instance, a PLS-NAFLD may be PLS-MASLD, or a PLSec-NAFLD may be a PLSeq-MASLD, and vice versa.

[0123] As a skilled artisan would recognize, the complete names of the genes or proteins recited herein are readily available one or more of the public protein database (e.g., National Center for Biotechnology Information (NCBI), The UniProt Consortium, or other similar database). The corresponding names for certain abbreviations utilized herein may also be found herein. ABCC5 may be ATP Binding Cassette Subfamily C Member 5. ABHD17B may be abhydrolase domain containing 17B, depalmitoylase. ABI3 may be ABI family member 3. ACRBP may be acrosin binding protein. ADAMTSL1 may be ADAMTS-like protein 1. abi3 may be ABI family member 3. AFTPH may be Aftiphilin protein. AKNA may be AT-Hook Transcriton Factor. ALB may be Albumin. ANAPC10 may be Anaphase Promoting Complex Subunit 10. ANKRD44 may be Ankyrin Repeat Domain 44. APOH may be ApolipoproterinH. ARF3 may be ADP Ribosylation Factor 3. ARFGEF2 may be BIG2 Protein. ARNT may be Aryl Hydrocarbon Recepter Nuclear Translocator. ATP5C1 may be ATP Synthase F1 Subunit Gamma. ATP6V0A2 may be ATPase H+ Transporting VO Subunit A2. ATP6V0B may be ATPase H+ Transporting VO Subunit B. ALIRKA may be Aurora Kinase A. B3GNTL1 may be UDP-GIcNAc: BetaGai Beta-1 , 3-N-Acetylglucosaminyltransferase Like 1. BAZ2B may be Bromodomain Adjacent To Zinc Finger Domain 2B. BCL2L1 may be BCL2 Like 1. BDH2 may be 3-Hydroxybutyrate Dehydrogenase 2. BTN3A1 may be Butyrophilin Subfamily 3 Member A1. C16orf87 may be Chromosome 16 Open Reading Frame 87. C1orf111 may be Chromosome 1 Open Reading Frame 111. C9orf72 may be C9orf72-SMCR8 Complex Subunit. CACNB2 may be Calcium Voltage-Gated Channel Auxiliary Subunit Beta 2. CCDC88C may be Coiled-Coil Domain Containing 88C. CD109 may be CD109 Molecule. CD48 may be CD48 Molecule. CD6 may be CD6 Molecule. cd69 may be CD69 Molecule. CFHR2 may be Complement Factor H Related 2. CHD1 may be Chromodomain Helicase DNA Binding Protein 1. COMMD2 may be COMM Domain Containing 2. CSNK2A2 may be Casein Kinase 2 Alpha 2. CTBP2 may be C-Terminal Binding Protein 2. CXCR6 may be C- X-C Motif Chemokine Receptor 6. CYP1 A2 may be Cytochrome P450 Family 1 Subfamily A Member 2. CYP2E1 may be Cytochrome P450 Family 2 Subfamily E Member 1. D2HGDH may be D-2-Hydroxyglutarate Dehydrogenase. DAGLA may be Diacylglycerol Lipase Alpha. DCAF4 may be DDB1 And CUL4 Associated Factor 4. DDX59 may be DEAD-Box Helicase 59. DGKA may be Diacylglycerol Kinase Alpha. DLG2 may be Discs Large MAGUK Scaffold Protein 2. DTD1 may be D-Aminoacyl-TRNA Deacylase 1. DYM may be Dymeclin. EHD2 may be EH Domain Containing 2. ENTPD1 may be Ectonucleoside Triphosphate Diphosphohydrolase 1. EP400 may be E1A Binding Protein P400. ERCC4 may be ERCC Excision Repair 4, Endonuclease Catalytic Subunit. ERI1 may be Exoribonuclease 1. ESD may be Esterase D. ETNK1 may be Ethanolamine Kinase 1. FAF1 may be Fas Associated Factor 1. GAREM may be GRB2 Associated Regulator Of MAPK1 Subtype. GCKR may be Glucokinase Regulator. GLS may be Glutaminase. GOLPH3 may be Golgi Phosphoprotein 3. HDC may be Histidine Decarboxylase. HIST1 H2BI may be Histone H2B type 1-C / E / F / G / lHIST1 H2BK may be Histone H2B type 1-K. HLA-A may be Major Histocompatibility Complex, Class I, A. HLA-DPA1 may be Major Histocompatibility Complex, Class II, DP AlphaI . HLA-G may be Major Histocompatibility Complex, Class I, G. HNF1A may be HNF1 Homeobox A. HNF4A may be Hepatocyte Nuclear Factor 4 Alpha. HPN may be Hepsin. HSD17B12 may be Hydroxysteroid 17-Beta Dehydrogenase 12. HSD17B13 may be Hydroxysteroid 17-Beta Dehydrogenase 13. HTT may be Huntingtin. ICOS may be Inducible T Cell Costimulator. id01 may be Indoleamine 2,3-Dioxygenase 1. IKZF5 may be IKAROS Family Zinc Finger 5. IL17RC may be Interleukin 17 Receptor C. IPCEF1 may be Interaction Protein For Cytohesin Exchange Factors 1. ITGA4 may be Integrin Subunit Alpha 4. ITGALmay be I ntegrin Subunit Alpha L. ITGAX may be Integrin Subunit Alpha X. KIAA1217 may be KIAA1217. KIFC2 may be Kinesin Family Member C2. L3MBTL4 may be L3MBTL Histone Methyl-Lysine Binding Protein 4. LATS2 may be Large Tumor Suppressor Kinase 2. LCK may be LCK Proto-Oncogene, Src Family Tyrosine Kinase. LLGL2 may be LLGL Scribble Cell Polarity Complex Component 2. LRRC27 may be Leucine Rich Repeat Containing 27. MASTL may be Microtubule Associated Serine / Threonine Kinase Like. MFSD11 may be Major Facilitator Superfamily Domain Containing 11. MIA-RAB4B may be MIA (melanoma inhibitory activity) and RAB4B (RAB4B, member RAS oncogene family). MIDN may be Midnolin. MKLN1 may be Muskelin 1. MLX may be MAX Dimerization Protein MLX. MPC2 may be Mitochondrial Pyruvate Carrier 2. MPV17L2 may be MPV17 Mitochondrial Inner Membrane Protein Like 2. MRPS15 may be Mitochondrial Ribosomal Protein S15. MTARC1 may be Mitochondrial Amidoxime Reducing Component 1. NAT10 may be N- Acetyltransferase 10. NAT9 may be N-Acetyltransferase 9 (Putative). NDUFB1 may be NADH:Ubiquinone Oxidoreductase Subunit B1. NEIL1 may be Nei Like DNA Glycosylase 1. NPPA may be Natriuretic Peptide A. NR5A2 may be Nuclear Receptor Subfamily 5 Group A Member 2. NSUN6 may be NOP2 / Sun RNA Methyltransferase 6. P2RY13 may be Purinergic Receptor P2Y13. PARP8 may be Poly(ADP-Ribose) Polymerase Family Member 8. pd-11 may be Programmed Cell Death Ligand 1 . PGAP1 may be Post-GPI Attachment To Proteins Inositol Deacylase 1. PHF10 may be PHD Finger Protein 10. PHGR1 may be Proline, Histidine And Glycine Rich 1. PIK3IP1 may be Phosphoinositide-3-Kinase Interacting Protein 1. PIM1 may be Pim-1 Proto-Oncogene, Serine / Threonine Kinase. PITPNB may be Phosphatidylinositol Transfer Protein Beta. PLD1 may be Phospholipase D1. PLIN1 may be Perilipin 1. PLP2 may be Proteolipid Protein 2. PNKD may be PNKD Metallo-Beta-Lactamase Domain Containing. PNPLA3 may be Patatin Like Phospholipase Domain Containing 3. PPFIBP1 may be PPFIA Binding Protein 1. PPP1 R1C may be Protein Phosphatase 1 Regulatory Inhibitor Subunit 1C. PPP1 R37 may be Protein Phosphatase 1 Regulatory Subunit 37. PRF1 may be Perforin 1. PRR14L may be Proline Rich 14 Like. PRR5L may be Proline Rich 5 Like. PSME3 may be Proteasome Activator Subunit 3. RAB3GAP2 may be RAB3 GTPase Activating Non-Catalytic Protein Subunit 2. RAB40C may be RAB40C, Member RAS Oncogene Family is a protein coding gene.. RAB4B may be RAB4B, Member RAS Oncogene Family is a protein coding gene. RAB4B-EGLN2 may be RAB4B (RAB4B, member RAS oncogene family) and EGLN2 (egl nine homolog 2) genes on chromosome 19. RCAN1 may be Regulator Of Calcineurin 1. RGS10 may be Regulator Of G Protein Signaling 10. RGS3 may be Regulator Of G Protein Signaling 3. RNF166 may be Ring Finger Protein 166. RSBN1 may be Round Spermatid Basic Protein 1. RTN4R may be Reticulon 4 Receptor. RXRA may be Retinoid X Receptor Alpha. SACS may be Sacsin Molecular Chaperone. SH3RF1 may be SH3 Domain Containing Ring Finger 1. SKAP1 may be Src Kinase AssociatedPhosphoprotein 1. SLA2 may be Src Like Adaptor 2. SLC15A3 may be Solute Carrier Family 15 Member 3. SLC4A10 may be Solute Carrier Family 4 Member 10. SMARCC1 may be SWI / SNF Related, Matrix Associated, Actin Dependent Regulator Of Chromatin Subfamily C Member 1. SMO may be Smoothened, Frizzled Class Receptor. SMPD2 may be Sphingomyelin Phosphodiesterase 2. SORBS2 may be Sorbin And SH3 Domain Containing 2. STALI2 may be Staufen Double-Stranded RNA Binding Protein 2. STX6 may be Syntaxin 6. SYTL1 may be Synaptotagmin Like 1. TAF5L may be TATA-Box Binding Protein Associated Factor 5 Like. TCF4 may be Transcription Factor 4. TCIRG1 may be T Cell Immune Regulator 1 , ATPase H+ Transporting VO Subunit A3. TECPR1 may be Tectonin Beta-Propeller Repeat Containing 1 . THOP1 may be Thimet Oligopeptidase 1 . THSD7A may be Thrombospondin Type 1 Domain Containing 7A. TLE2 may be TLE Family Member 2, Transcriptional Corepressor. tlr4 may be Toll Like Receptor 4. TM6SF2 may be Transmembrane 6 Superfamily Member 2. TM7SF2 may be Transmembrane 7 Superfamily Member 2. TM9SF2 may be Transmembrane 9 Superfamily Member 2. TMEM184C may be Transmembrane Protein 184C. TP53BP2 may be Tumor Protein P53 Binding Protein 2. TPH1 may be Tryptophan Hydroxylase 1. TTC27 may be Tetratricopeptide Repeat Domain 27. TTC6 may be Tetratricopeptide Repeat Domain 6. TUBA1C may be Tubulin Alpha 1c. UBXN2A may be UBX Domain Protein 2A. UGT2B7 may be UDP Glucuronosyltransferase Family 2 Member B7. USB1 may be U6 SnRNA Biogenesis Phosphodiesterase 1. USP5 may be Ubiquitin Specific Peptidase 5. VAV1 may be Vav Guanine Nucleotide Exchange Factor 1. WBSCR16 may be Mitochondrial Protein Important to Fusion. Protein in the Regulator of Chromosome Condensation 1. WDR43 may be WD Repeat Domain 43. WSB1 may be WD Repeat And SOCS Box Containing 1. ZEB2 may be Zinc Finger E-Box Binding Homeobox 2. ZFAND5 may be Zinc Finger AN1-Type Containing 5.

[0124] It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.II. Methods of Determining a Non-Alcoholic Fatty Liver Disease Prognostic Liver Signature (PLS-NAFLD) score and / or a Non-Alcoholic Fatty Liver Disease Prognostic Liver Secretome Signature (PLSec-NAFLD) score

[0125] In general, methods disclosed herein include determining a PLS-NAFLD and / or a PLSec-NAFLD score for a subject having or suspected of having non-alcoholic fatty liver disease, wherein the determined PLS-NAFLD score and / or PLSec-NAFLD score can be used to predict the risk for developing liver cancer (e.g., hepatocellular carcinoma, HCC) in the subject, the prognostic outcome for a subject with NAFLD having or suspected of having HCC,and / or providing a suitable treatment regimen to the subject. Standard procedures used to classify a variety of cancers (e.g., tumor staging) do not take into account extenuating factors that have an impact of HCC risk, outcome, and treatment regimens. Moreover, individuals with NAFLD have additional risk factors for hepatocellular carcinoma that make early and accurate diagnosis critical. Further, identifying individuals with NAFLD with low risk for HCC is also important to avoid unnecessary therapeutic intervention in individuals already subject to multiple therapies. To account for such extenuating factors, the present disclosure provides novel methods of classifying risk for developing HCC in a subject having or suspected of having NAFLD by determining a PLS-NAFLD or PLSec-NAFLD score of the subject.

[0126] As used herein, a suitable subject includes a mammal, a human, a livestock animal, a companion animal, a lab animal, or a zoological animal. In some embodiments, a subject may be a rodent, e.g., a mouse, a rat, a guinea pig, etc. In other embodiments, a subject may be a livestock animal. Non-limiting examples of suitable livestock animals may include pigs, cows, horses, goats, sheep, llamas and alpacas. In yet other embodiments, a subject may be a companion animal. Non-limiting examples of companion animals may include pets such as dogs, cats, rabbits, and birds. In yet other embodiments, a subject may be a zoological animal. As used herein, a “zoological animal” refers to an animal that may be found in a zoo. Such animals may include non-human primates, large cats, wolves, and bears. In other embodiments, the animal is a laboratory animal. Non-limiting examples of a laboratory animal may include rodents, canines, felines, and non-human primates. In some embodiments, the animal is a rodent. Non-limiting examples of rodents may include mice, rats, guinea pigs, etc. In preferred embodiments, the subject is a human.

[0127] In some embodiments, a suitable subject for the methods herein may have or may be suspected of having non-alcoholic fatty liver disease (NAFLD). In some embodiments, the subject may have one or more injuries to the liver that may predispose the subject to nonalcoholic fatty liver disease, hepatocellular carcinoma, or any other liver cancer. Non-limiting examples of one or more injuries to the liver that may predispose a subject to non-alcoholic fatty liver disease may include liver cell damage caused by excessive intake of fatty and / or high-calory diet as well as metabolic disorders such as type 2 diabetes, hyperlipidemia, and obesity. In some embodiments, a suitable subject for the methods herein may have or be suspected of having one or more injuries to the liver that may predispose a subject to NAFLD.

[0128] In some embodiments, a suitable subject for the methods herein may present with at least one clinical symptom or sign associated with non-alcoholic fatty liver disease (NAFLD). Non-limiting examples of clinical symptoms and signs associated with non-alcoholic fatty liver disease (NAFLD) may include elevated blood liver enzyme tests, impaired blood clottingfunction, ascites, gastro-esophageal varices, bacterial peritonitis, encephalopathy or a combination thereof.

[0129] In some embodiments, a suitable subject for the methods herein may have or be suspected of having a liver cancer. In some embodiments, a suitable subject for the methods herein may have or be suspected of having a secondary liver cancer. A secondary liver cancer, also known as a liver metastasis, develops when primary cancer from another part of the body spreads to the liver. In some embodiments, a subject to be subjected to the methods herein may have or be suspected of having a primary liver cancer. A primary liver cancer is a cancer that originates in the liver. Non-limiting examples of primary liver cancers include hepatocellular carcinoma (HCC) (also called hepatoma); fibrolamellar HCC; cholangiocarcinoma (e.g., bile duct cancer); angiosarcoma (also called hemangiosarcoma), and the like. HCC is the most common type of liver cancer, accounting for approximately 75 percent of all liver cancers. HCC starts in the main type of liver cells, called hepatocellular cells. HCC can result from one or more injuries to the liver that may predispose a subject to HCC. NAFLD and NASH, discussed above, are examples of injuries to the liver that can predispose a subject to HCC, but others exist. Non-limiting examples of one or more injuries to the liver that may predispose a subject to HCC other than NAFLD or NASH can include cirrhosis, chronic infection of hepatitis B virus (HBV), chronic infection of hepatitis C virus (HCV), primary biliary cirrhosis (PBC), hereditary hemochromatosis, type 2 diabetes, obesity, tobacco use, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alphal -antitrypsin deficiency, porphyria cutanea tarda, glycogen storage diseases, Wilson disease, or a combination thereof. In some embodiments, a suitable subject for the methods herein may have or be suspected of having one or more injuries to the liver (e.g., including and in addition to NAFLD or NASH) that may predispose a subject to HCC.

[0130] In some embodiments, a suitable subject for the methods herein may present with at least one clinical symptom associated with liver cancer (e.g., HCC). Non-limiting examples of clinical symptoms associated with liver cancer (e.g., HCC) may include mild to moderate upper abdominal pain, weight loss, early satiety, or a palpable mass in the upper abdomen, paraneoplastic syndrome, hypoglycemia, erythrocytosis, hypercalcemia, intractable diarrhea and associated electrolyte disturbances (e.g., hyponatremia, hypokalemia, metabolic alkalosis), cutaneous manifestations (e.g., dermatomyositis, pemphigus foliaceus, seborrheic keratosis, pityriasis rotunda), intraperitoneal bleeding, jaundice, fever, pyogenic liver abscess, and the like. In some embodiments, a suitable subject for the methods herein may have one or more serum markers indicative of liver cancer (e.g., HCC). Non-limiting examples of serum markers indicative of liver cancer (e.g., HCC) mayinclude alpha-fetoprotein (AFP) (e.g., an AFP level of 20 ng / mL or higher), des-gamma- carboxy prothrombin, lens culinaris agglutinin- reactive AFP (AFP-L3), and the like.

[0131] In some embodiments, a PLS-NAFLD score and / or a PLSec-NAFLD score may be determined as disclosed herein from at least one sample collected from a subject. In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with a liver disease (e.g., non-alcoholic fatty liver disease or hepatocellular carcinoma). In some aspects, at least one sample can be obtained from a subject who has not been nonalcoholic fatty liver disease. In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with hepatocellular carcinoma. In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with a liver disease (e.g., non-alcoholic fatty liver disease or hepatocellular carcinoma) but is suspected of having the liver disease or condition. In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with non-alcoholic fatty liver disease (NAFLD) but is suspected of having non-alcoholic fatty liver disease (NAFLD). In some other aspects, at least one sample can be obtained from a subject who has been diagnosed with a liver disease (e.g., non-alcoholic fatty liver disease or hepatocellular carcinoma). In some other aspects, at least one sample can be obtained from a subject who has been diagnosed with a non-alcoholic fatty liver disease. In some aspects, at least one sample can be obtained from a subject who may have or be suspected of having one or more injuries to the liver that may predispose a subject to non-alcoholic fatty liver disease. In some aspects at least one sample can be obtained from a subject who may have or be suspected of having one or more injuries to the liver that may predispose a subject to hepatocellular carcinoma.

[0132] In some embodiments, a PLS-NAFLD score may be determined by obtaining a gene expression profile from a sample collected from a subject. As used herein, the term “gene expression profile” refers to a pattern of genes expressed in a sample at the transcription level. Non-limiting examples of methods of measuring gene expression in a sample suitable for use herein include digital transcript counting, high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, RNA Seq, tiling array, northern blotting, hybridization microarray, in situ hybridization, or a combination thereof. In some aspects, a gene expression profile as disclosed herein can be obtained by any known or future method suitable to assess gene expression.

[0133] In some embodiments, a PLSec-NAFLD score may be determined by obtaining a protein expression profile from a sample collected from a subject. As used herein, the term “protein expression profile” refers to a pattern of proteins expressed in a sample collected fromthe subject. Non-limiting examples of methods of measuring protein expression in a sample suitable for use herein include Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two- dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), or a combination thereof. In some aspects, a protein expression profile as disclosed herein can be obtained by any known or future method suitable to assess protein expression.

[0134] In some embodiments, a sample obtained from a subject for determination of a PLS- NAFLD score and / or a PLSec-NAFLD score as disclosed in the methods herein may be a tissue sample, a blood sample, a plasma sample, a hair sample, venous tissues, cartilage, a sperm sample, a skin sample, an amniotic fluid sample, a buccal sample, saliva, urine, serum, sputum, bone marrow or a combination thereof. In some aspects, a sample obtained from a subject for determination of a PLS-NAFLD score and / or a PLSec-NAFLD score as disclosed herein may be a liver tissue sample (e.g., a biopsy).

[0135] In some embodiments, a sample obtained from a subject for determination of a PLSec- NAFLD score as disclosed herein may be a blood, serum and / or plasma sample. In some aspects, a liver sample for use in the methods herein can be liver proteins isolated from a blood sample collected from any of the subjects disclosed herein. In some aspects, a sample obtained from a subject for determination of a PLSec-NAFLD score as disclosed herein may be serum.

[0136] In some embodiments, a sample obtained from a subject for determination of a PLS- NAFLD score as disclosed herein may be a liver tissue sample (e.g., a biopsy). Non-limiting methods suitable for use herein to collect liver tissue include collection by fine needle aspirate, by removal of pleural or peritoneal fluid, and by excisional biopsy. In some aspects, a liver sample can include a biopsy from a single site in the liver, a biopsy from at least one tissue in liver and / or at least one tissue in contact with the liver can be from about 10 mg about 50 mg (e.g., about 10 mg, 15 mg, 20 mg, 25 mg, 30 mg, 35 mg, 40 mg, 45 mg, 50 mg) of tissue per sample.

[0137] In some aspects, a sample obtained from for determination of a PLS-NAFLD score or PLSec-NAFLD score as disclosed herein may be stored at about 25°C to about -80°C for up to about 1 day to about 2 years, about 1 week to about 1 year, or about 1 month to about 6 months. In other aspects, a sample obtained from a subject may be immediately processedto obtain a protein expression profile as disclosed herein. In some other aspects, a sample obtained from a subject may be processed to obtain a protein expression profile as disclosed herein. Non-limiting examples of sample preparation methods can be found in art, for example in Gallagher & Wiley, (2012) CURRENT PROTOCOLS ESSENTIAL LABORATORY TECHNIQUES, Hoboken, N.J: Wiley-Blackwell, the disclosures of which are incorporated herein.(a) Non-Alcoholic Fatty Liver Disease Prognostic Liver Signature (PLS-NAFLD)

[0138] In some embodiments, a sample obtained from a subject for determination of a PLS- NAFLD score as disclosed herein consists of a gene expression profile. As used herein, a gene expression profile comprises a pattern of genes expressed in a sample at the transcription level. Non-limiting examples of methods of measuring gene expression in a sample suitable for use herein include high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, RNA Seq, tiling array, northern blotting, hybridization microarray, in situ hybridization, digital transcript counting, or a combination thereof. In some aspects, a gene expression profile as disclosed herein can be obtained by any known or future method suitable to assess gene expression. In some embodiments a PLS-NAFLD score as disclosed herein can be determined from a gene expression profile of a liver sample. In some embodiments, a PLS- NAFLD score as disclosed herein can be determined from a gene expression profile expressed by the liver wherein the gene expression profile is comprised of a panel of genes associated with the risk of developing hepatocellular carcinoma, particularly in subjects having or suspected of having non-alcoholic fatty liver disease (NAFLD). In some aspects, a computational biology approach may be applied to identify a gene expression profile associated with the risk of developing hepatocellular carcinoma, particularly in subjects having or suspected of having non-alcoholic fatty liver disease (NAFLD). For example, ranked prioritized genes can be tested for their association with liver fibrosis against a plurality of matched control gene set. Covariates can be adjusted to identify a set of circulating proteins enriched for liver fibrosis (e.g., p<0.001). One or more regression models may be applied to a set of differentially expressed genes to further select for genes that are relevant to hepatocellular carcinoma, or its progression and the risk associated thereof, particularly in subjects having or suspected of having non-alcoholic fatty liver disease (NAFLD). In some embodiments, an enriched group of genes for assessing hepatocellular carcinoma risk may be a panel of genes that make up a gene expression profile as disclosed herein. In some embodiments, computational approaches exemplified herein can identify panel of genes for prognostic prediction of hepatocellular carcinoma risk. As used herein, a “panel of genes”refers to one or more genes whose differential expression (i.e., over-expression or underexpression) is predictive of the risk for developing a pathological condition and / or having a pathological condition. In some embodiments, computational approaches exemplified herein can identify a panel of genes for prognostic prediction of hepatocellular risk in subjects having or suspected of having NAFLD, wherein the panel of genes can be referred to as a PLS- NAFLD.

[0139] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA 1217, or a combination thereof. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MI , SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, or a combination thereof and one or more genes selected from AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2,C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA 1217, or a combination thereof.

[0140] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 5 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 5 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MI , SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217.

[0141] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 10 or more genes selected from: ACRBP, USB1, HLA-A,TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 10 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, S7X6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more genes selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217.

[0142] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 20 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G,ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B- EGLN2, MUX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 20 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MUX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217.

[0143] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 30 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1,DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 30 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217.

[0144] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 40 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MI , SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72,CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 40 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217.

[0145] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 50 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 50 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA,RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217.

[0146] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 60 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 60 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, S7X6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217.

[0147] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 70 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 70 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, S7X6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1,NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217.

[0148] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 80 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MIX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 80 genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B- EGLN2, MIX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a combination of 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4,LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217.

[0149] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 90 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MIX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217.

[0150] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of 100 or more genes selected from: ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B- EGLN2, MIX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA 1217.

[0151] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of the following genes: ACRBP, USB1, HLA-A, TPH1,ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and KIAA1217.

[0152] In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more genes wherein at least one of the genes is a high- risk-associated gene. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment in NAFLD patients may comprise a combination of one or more (e.g., 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, or 80 or more) high-risk-associated genes selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, ora combination thereof. In some embodiments, a PLS- NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more genes wherein at least one of the genes is a low-risk-associated gene. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more (e.g., 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more) low-risk associated genes selected from: AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2,C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA 1217, or a combination thereof. In some embodiments, a PLS-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more (e.g., 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, or 80 or more) high-risk-associated genes of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, or a combination thereof and one or more (e.g., 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more) low-risk-associated genes of AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof.

[0153] In some embodiments, a PLS-NAFLD or PLS-MASLD for subtype identification may comprise measuring gene expression of one or more of HPN, LLGL2, D2HGDH, HNF4A, HNF1A, SELO, NEIL1, IL17RC, SMO, RXRA, APOH, TM9SF2, NDUFB1, MPC2, ATP5C1, ESD, CFHR2, UGT2B7, HSD17B12, MRPS15, ANKRD44, GLS, ENTPD1, PARP8, SACS, DGKA, TCF4, WSB 1, ZEB2, ITGA4, or a combination thereof within a PLS-MASLD for M ASLD subtype identification. In some embodiments, a PLS-MASLD comprises one or more or HPN, LLGL2, D2HGDH, HNF4A, HNF1A, SELO, NEIL1, IL17RC, SMO, RXRA, or a combination thereof. In some embodiments, In some embodiments, a PLS-MASLD comprises one or more of RXRA, APOH, TM9SF2, NDUFB1, MPC2, ATP5C1, ESD, CFHR2, UGT2B7, HSD17B12, MRPS15 or a combination thereof. In some embodiments, a PLS-MASLD comprises one or more of ANKRD44, GLS, ENTPD1, PARP8, SACS, DGKA, TCF4, WSB1, ZEB2, ITGA4, or a combination thereof.

[0154] Provided herein is a method of identifying a MASLD subtype in a subject, comprising determining a MASLD-associated prognostic liver (PLS-MASLD) score for the subject and identifying the MASLD subtype as: an indolent-MASLD (i-MASLD) subtype; a progressive- MASLD (p-MASLD) subtype; or an advanced-MASLD (a-MASLD) subtype. Identification maybe based on the PLS-MASLD score. Also provided is method of obtaining the PLS-MASLD score for the subject, wherein the method of obtaining the PLS-MASLD score comprises: having obtained or obtaining a tissue sample from the subject; subjecting the tissue sample to a multi-analyte profiling assay for quantification of one or more signature genes or nucleotide sequences encoding a signature protein to obtain one or more quantification measurements; normalizing the one or more quantification measurements; converting the quantification measurements into a PLS- MASLD score, wherein the PLS- MASLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a reference gene expression profile for an indolent-MASLD (i-MASLD) subtype; a progressive- MASLD (p-MASLD) subtypes; and / or an advanced-MASLD (a-MASLD) subtype.

[0155] A signature genes or nucleotide sequences encoding a signature protein may be selected from an i-MASLD subtype signature gene panel, optionally wherein the i-MASLD subtype signature gene panel comprises one or more genes selected from TABLE 12 or TABLE 16; a p-MASLD subtype signature gene panel, optionally wherein the p-MASLD subtype sig-nature gene panel comprises one or more gene selected from TABLE 12 or TABLE 16; and / or an a-MASLD subtype signature gene panel, optionally wherein the a- MASLD subtype signature protein panel comprises one or more proteins selected from TABLE 12 or TABLE 16.

[0156] The one or more signature genes or nucleotide sequences encoding a signature protein is selected from an i-MASLD subtype signature gene selected from APOH, CFHR2, MPC2, TM9SF2, UGT2B7, ATP5C1 , MRPS15, ESD, HSD17B12, NDUFB1 , CXCR6, or a combination thereof; a p-MASLD subtype signature gene selected from SELO, HNF4A, D2HGDH, SMO, IL17RC, NEIL1 , LLGL2, RXRA, HNF1A, HPN, or a combination thereof; and / or an a-MASLD subtype signature gene selected from DGKA, ZEB2, PARP8, ITGA4, ANKRD44, ENTPD1 , SACS, TCF4, WSB1 , GLS, or a combination thereof.

[0157] A method of identifying a MASLD subtype in the subject may further comprise performing a liver biopsy, one or more blood tests to assess liver function, measuring levels of alpha-fetoprotein in blood, computed tomography, magnetic resonance imaging, or a combination thereof. The one or more blood tests may be performed to assess liver function comprises a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof. A method of identifying a MASLD subtype may further comprise administering one or more prophylactic therapies and / or treatments of MASLD or hepatocellular carcinoma (HCC) to the subject as described herein. A drug may be selected from an oral CCR2 / 5 antagonist optionally selected from cenicriviroc, or a discoidin domain receptor tyrosine kinase 1 (DDR1) inhibitor.(b) Non-Alcoholic Fatty Liver Disease Prognostic Liver Secretome Signature (PLSec- NAFLD)

[0158] In some embodiments, a sample obtained from a subject for determination of a PLSec- NAFLD score as disclosed herein consists of a secretome. As used herein, a “secretome” refers to a panel of proteins expressed by an organism and secreted into the extracellular space. In some embodiments a PLSec-NAFLD score as disclosed herein can be determined from a secretome expressed by the liver and secreted into the extracellular space. In some embodiments, a PLSec-NAFLD score as disclosed herein can be determined from a secretome expressed by the liver wherein the secretome is comprised of a panel of proteins associated with the risk of developing a liver cancer. In some aspects, a computational biology approach may be applied to identify a secretome associated with the risk of developing hepatocellular carcinoma. For example, ranked prioritized circulating proteins can be tested for their association with hepatocellular carcinoma in subjects with non-alcoholic fatty liver disease against a plurality of matched control gene set. Covariates can be adjusted to identify a set of circulating proteins enriched for hepatocellular carcinoma (e.g., p<0.001). One or more regression models may be applied to the enriched circulating proteins set to further select for proteins that are relevant to hepatocellular carcinoma and the risk associated thereof. In some embodiments, an enriched group of circulating proteins for assessing hepatocellular carcinoma risk may be a panel of proteins that make up a secretome as disclosed herein. In some embodiments, computational approaches exemplified herein can identify panel of proteins for prognostic prediction of hepatocellular carcinoma risk. As used herein, a “panel of proteins” refers to one or more proteins that are predictive of the risk for developing a pathological condition and / or having a pathological condition. In some embodiments, computational approaches exemplified herein can identify a panel of circulating proteins for prognostic prediction of hepatocellular carcinoma risk, wherein the panel of circulating proteins can be referred to as a serum-protein-based PLSec-NAFLD.

[0159] In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more circulating proteins selected from lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor. In some embodiments, a PLSec- NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more circulating proteins encoded by XCL1, GRN, ANGPT2, and MET genes, respectively.

[0160] In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more circulating proteins selected from lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor or a combination thereof. In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of three or more circulating proteins of lymphotactin, progranulin, angiopoietin2 and / or hepatocyte growth factor or a combination thereof. In some embodiments, a PLSec- NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of the four circulating proteins of lymphotactin, progranulin, angiopoietin 2 and hepatocyte growth factor.

[0161] In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more circulating proteins wherein at least one of the proteins is a high-risk-associated protein. In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more high- risk-associated circulating proteins of lymphotactin, progranulin, or a combination thereof. In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more circulating proteins wherein at least one of the proteins is a low-risk-associated protein. In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more low-risk-associated circulating proteins of angiopoietin 2, hepatocyte growth factor or a combination thereof. In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more high-risk-associated circulating proteins and one or more low-risk-associated circulating proteins. In some embodiments, a PLSec-NAFLD for hepatocellular carcinoma risk assessment may comprise a combination of one or more high- risk-associated circulating proteins of lymphotactin, progranulin or a combination thereof and one or more low-risk-associated circulating proteins of angiopoietin 2, hepatocyte growth factor or a combination thereof.

[0162] In some embodiments, a PLSec-NAFLD or PLSec-MASLD for subtype identification may comprise measuring a signature protein selected from Parkinson disease protein 7 (PARK7), soluble HGF receptor, N-cadherin, angiopoietin-like protein 3, furin, inter-alpha- trypsin inhibitor heavy chain H4 (ITIH4), angiopoietin-related protein 6, cathepsin S, CCL-20, collage IV alpha 1 , autotaxin, a-FABP, osteopontin, pro-collagen I alpha I, IGFBP-7, or a combination thereof.

[0163] In some embodiments, an PLSec-NAFLD or PLSec-MASLD for subtype identification may comprise measuring a MASLD-associated prognostic liver secretome signature (PLSec- MASLD) score for the subject and identifying the MASLD subtype as: an indolent-MASLD (i- MASLD) subtype; a progressive-MASLD (p-MASLD) subtype; or an advanced-MASLD (a- MASLD) subtype. The identification may be based on the PLSec-MASLD score. Provided herein is a method for obtaining the PLSec-MASLD score for the subject, wherein the method of obtaining the PLSec-MASLD score comprises: having obtained or obtaining a blood sample from the subject; subjecting the blood sample to a multi-analyte profiling assay for protein quantification of one or more signature proteins to obtain one or more quantification measurements; normalizing the one or more quantification measurements; and converting thequantification measurements into an aggregated score wherein the aggregated score is the PLSec-MASLD score. The one or more signature proteins may be selected from an i-MASLD subtype signature protein panel; a p-MASLD subtype signature protein panel; and / or an a- MASLD subtype signature protein panel.

[0164] The one or more signature proteins may be selected from Parkinson disease protein 7 (PARK7), soluble HGF receptor, N-cadherin, angiopoietin-like protein 3, or a combination thereof; furin, inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), angiopoietin-related protein 6, or a combination thereof; and / or cathepsin S, CCL-20, collage IV alpha 1 , autotaxin, a- FABP, osteopontin, pro-collagen I alpha I, IGFBP-7, or a combination thereof.

[0165] A method of identifying a MASLD by PLSec-MASLD subtype may further comprise performing a liver biopsy, one or more blood tests to assess liver function, measuring levels of alpha-fetoprotein in blood, computed tomography, magnetic resonance imaging, or a combination thereof. The one or more blood tests performed to assess liver function comprises a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof. A method of identifying a MASLD by PLSec-MASLD may further comprise administering one or more prophylactic therapies and / or treatments of MASLD or hepatocellular carcinoma (HCC) to the subject, as described herein The one or more prophylactic therapies and / or treatments of MASLD or HCC comprise surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof. The drug may be selected from an oral CCR2 / 5 antagonist optionally selected from cenicriviroc, or a discoidin domain receptor tyrosine kinase 1 (DDR1).(c) PLS-NAFLD and PLSec-NAFLD assays and PLS-NAFLD and PLSec-NAFLD scores

[0166] In some embodiments, a PLS-NAFLD as disclosed herein may be used to determine a PLS-NAFLD score. In some embodiments, a PLSec-NAFLD as disclosed herein may be used to determine a PLSec-NAFLD score. In some embodiments, a PLS-NAFLD score and / or a PLSec-NAFLD score can be determined from one or more samples collected from a subject as described herein. In some embodiments, a PLSec-NAFLD score can be determined from the results of a PLSec-NAFLD assay. In some embodiments, a PLS-NAFLD score can be determined from the results of a PLS-NAFLD assay.PLS-NAFLD Assay and PLS-NAFLD Score

[0167] In some embodiments, a sample collected from a subject as disclosed herein can be processed and used in a PLS-NAFLD assay. As used herein, an “PLS-NAFLD assay” refers to subjecting a sample to any method suitable for determining the level of gene expression ofany one of the genes comprising a PLS-NAFLD for hepatocellular carcinoma risk assessment as disclosed herein. In some embodiments, a PLS-NAFLD assay may be a method of measuring gene expression of one or more genes within a PLS-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments, a PLS-NAFLD assay may be a method of measuring gene expression of one or more of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1 , B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MIX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof within a PLS-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments, a PLS-NAFLD assay may be a method of measuring gene expression of one or more of AEBP1, ANXA1, IER3, CXCR4, FILIP1L, L0XL2, KRT7, DDR1, SLC7A1, BCL2, NTS, FBN1, IGFBP6, ASAHUNAAA, TTR, PMM1, P0N3, F9, HAAO, or a combination thereof within a PLSec-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments, a PLS-NAFLD assay may be a method of measuring gene expression of one or more of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MI , SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, or a combination thereof within a PLS-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments, a PLSec-NAFLD assay may be a method of measuring gene expression of one or more of AL / F? A, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L,WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA 1217 or a combination thereof within a PLS-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments, a PLS-NAFLD assay may be a method of measuring gene expression of (a) one or more (e.g., 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, or 80 or more) of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA- DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA- RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9 or a combination thereof and (b) one or more (e.g., 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, or 50 or more) of AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217 or a combination thereof within a PLS-NAFLD for hepatocellular carcinoma risk assessment.

[0168] In some embodiments, a PLS-NAFLD assay described herein may entail subjecting a sample from a subject herein to a gene expression profiling assay of one or more genes within a PLS-NAFLD for hepatocellular carcinoma risk assessment. A gene expression profiling assay is a type of assay that uses labeled oligonucleotide probes to simultaneously measure multiple RNA transcripts in a sample in a single experiment. Non-limiting examples of gene expression profiling assays suitable for use herein may include digital transcript counting assays like the nCounter Analysis System (NanoString). Alternatively, the gene expression profiling assay may include a DNA microarray or RNA-Seq assay. In some embodiments, a PLS-NAFLD assay may entail subjecting a sample collected from a subject herein to an FDA- approved clinical diagnostic digital transcript counting technology, nCounter platform.

[0169] In some embodiments, a PLS-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 ,52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99, 100, 101 , 102, 103, 104, 105, 106, 107, 108, 109, 110, 111 , 112, 113, 114, 115, 116, 117, 118, 119, 120, 121 , 122, 123, 124, 125, 126, 127, 128, 129, 130, 131 , 132, or 133) within a gene panel comprising one or more of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof. In some embodiments, a PLS-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23,24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48,49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, 73,74, 75, 76, 77, 78, 79, or 80) within a gene panel comprising one or more of ACRBP, USB1,HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, S7X6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA- G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B- EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, or a combination thereof. In some embodiments, a PLS-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53) within a gene panel comprising one or more of AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM,MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof. In some embodiments, a PLS-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of a gene selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof.

[0170] In some embodiments, a PLS-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99, 100, 101 , 102, 103, 104, 105, 106, 107, 108, 109, 110, 111 , 112, 113, 114, 115, 116, 117, 118, 119, 120, 121 , 122, 123, 124, 125, 126, 127, 128, 129, 130, 131 , 132, or 133) within a gene panel comprising one or more of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX,TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof and normalizing the gene expression measurements. One of skill in the art will appreciate that the method of normalizing gene expression measurements will depend upon the specifics of the multi-analyte profiling assay used. In accordance with some of the embodiments herein, a PLS-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, or 133) within a gene panel comprising one or more ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof and normalizing the gene expression measurements to gene expression levels of a set of control genes (e.g., BA T3, NDUFA2, C0X8A, HNRNPA2B1, HINT1, ATP5B). In various aspects, normalizing the gene expression measurements comprises quantifying raw expression of each gene as a digital count of its transcript and then dividing each digital count by the geometric mean of the raw counts of the control genes.

[0171] In some embodiments, normalized gene expression measurements produced by a PLS-NAFLD assay herein may be used to generate a PLS-NAFLD score. In general, the method of determining a PLS-NAFLD score generally involves comparing a gene expression profile of a subject to a gene expression profile of a reference profile (or template) of high risk and a reference profile (or template) of low risk for hepatocellular carcinoma (e.g., in a NAFLD specific population). In some embodiments this involves converting normalized gene expression measurements produced by a PLS-NAFLD assay herein into a predicted confidence p value with proximity to either a high-risk reference profile or a low-risk reference profile, and then using the predicted confidence p value with proximity to either of the high or low-risk reference profile to calculate the PLS-NAFLD score, as described below. In some embodiments, normalized gene expression measurements produced by a PLS-NAFLD assay herein may be converted into high or low risk genes by top quartile cut-off in the optimization set, wherein a high-risk gene is selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, ML SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, and / or NAT9 and a low-risk gene is AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof. Accordingly, a high-risk reference profile may be generated as a vector concatenating 1 for the 80 high-risk-associated genes and 0 for the 53 low-risk-associated genes, and the low-risk reference profile may be generated as a vector concatenating 0 for the 80 high-risk-associated genes and 1 for the 53 low-risk- associated genes. These high or low risk reference profiles are then used in the methods below to derive the PLS-NAFLD score for a sample.

[0172] In various aspects, converting normalized gene expression measurements (herein referred to as a “gene expression profile”) into a PLS-NAFLD score comprises the following steps. In a first step, the normalized gene expression measurements are compared to the high and low risk reference profiles described above. In some aspects, comparing the geneexpression profile to the high and low risk reference profiles comprises quantifying the similarity between the gene expression profile and each of the high or low risk reference profile. The similarity of the gene expression profile to either of the reference profiles may in some aspects be quantified by cosine distance. In various aspects, the risk reference profile (e.g., the high or low risk reference profile) having the lowest cosine distance (highest similarity to the gene expression profile) is then selected and a prediction confidence p value for the gene expression profile in reference to (i.e. , “with proximity to”) that risk reference profile is calculated based on random permutation test. This prediction confidence p value is assigned a sign depending on which risk reference profile is used in its derivation (e.g., positive for high- risk reference profile and negative for low-risk reference profile) and converted to a logarithmic scale (base 10) with negative sign to generate the PLS-NAFLD score. For example, a prediction confidence p value with proximity to the high-risk reference profile of 0.05 would correspond to a PLS-NAFLD score of +1.3013 (+(-logio(0.05)) = +1.3013)). A prediction confidence p value with proximity to the low-risk reference profile of 0.05 would correspond to a PLS-NAFLD score of -1.3013 (-(-logio(0.05)) = -1.3013). When the p value is less than 0.05 with proximity to the high-risk reference profile (PLS-NAFLD score greater than +1.3013), the subject is at high risk (has a “similar” gene expression profile to the theoretical patients having NAFLD with the highest risk of hepatocellular carcinoma). When the p value is less than 0.05 with proximity to the low-risk reference profile (PLS-NAFLD score less than -1.3013), the subject is at low risk (has a “similar” gene expression profile to the theoretical patients having NAFLD with the lowest risk of hepatocellular carcinoma). When the p value is equal or greater than 0.05 with proximity to either reference profile (PLS-NAFLD score between -1.3013 and +1.3013), the subject would normally be considered at intermediate risk. However, as described further in the Examples below, the risk profiles between intermediate and high-risk patients were not materially different in this specific patient population (e.g., individuals having or suspected of having NAFLD). Therefore, the methods and assays provided herein generally categorize a subject has high risk if a PLS-NAFLD score greater than -1.3013 is determined. In each case, the PLS-NAFLD score is a measure of how similar the gene expression profile of the subject is to either of the reference profiles (wherein the high- and low-risk reference profiles represent the theoretical upper and lower limit for the range of risk level, respectively).

[0173] Therefore, in various aspects, the PLS-NAFLD scores herein are provided as a measure of a risk of an individual (e.g., an individual having or suspected of having NAFLD) for developing hepatocellular carcinoma. In some embodiments, a subject having a PLS- NAFLD score as determined herein above a given threshold (e.g., +1.30103, corresponding to a prediction confidence p-value of 0.05 with proximity to the high-risk reference profile) maybe predicted to be at high risk for developing hepatocellular carcinoma. In some embodiments, a subject having a PLS-NAFLD score as determined herein below a given threshold (e.g., -1.30103, corresponding to a prediction confidence p-value of 0.05 with the low-risk reference profile) may be predicted to be at low risk for developing hepatocellular carcinoma. In some embodiments, a subject having a PLS-NAFLD score as determined herein between these two thresholds (e.g., -1.30103 to +1.30103, which correspond to a prediction confidence p-value of 0.05 irrespective of closer reference profile) may be predicted to be at high risk for developing hepatocellular carcinoma. In some embodiments, a subject having a PLS-NAFLD score as determined herein above a given threshold (e.g., -1.30103, corresponding to a prediction confidence p-value that is not less than 0.05 with proximity to the low-risk reference profile) may be predicted to be at high risk for developing hepatocellular carcinoma.

[0174] In some aspects, an MASLD subtype is identified as an i-MASLD subtype when the PLS-MASLD score comprises or consists of one or more signature genes selected from APOH, CFHR2, MPC2, TM9SF2, UGT2B7, ATP5C1 , MRPS15, ESD, HSD17B12, NDUFB1 , CXCR6, or a combination thereof; a p-MASLD subtype when the PLS-MASLD score comprises or consists of one or more signature genes selected from SELO, HNF4A, D2HGDH, SMO, IL17RC, NEIL1 , LLGL2, RXRA, HNF1A, HPN, or a combination thereof; and / or an a- MASLD subtype when the PLS-MASLD score comprises or consists of one or more signature genes selected from DGKA, ZEB2, PARP8, ITGA4, ANKRD44, ENTPD1 , SACS, TCF4, WSB1 , GLS, or a combination thereof. PLS-MASLD score for MASLD subtyping may be greater than 1 or less than 1.PLSec-NAFLD Assay and PLSec-NAFLD Score

[0175] In some embodiments, a sample collected from a subject as disclosed herein can be processed and used in a PLSec-NAFLD assay. As used herein, a “PLSec-NAFLD assay” refers to subjecting a sample to any method suitable for determining the level of protein expression of any one of the proteins comprising a PLSec-NAFLD for hepatocellular carcinoma risk assessment as disclosed herein. In some embodiments, a PLSec-NAFLD assay may be a method of measuring protein abundance of one or more proteins within a PLSec-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments, a PLSec-NAFLD assay may be a method of measuring protein abundance of one or more of: lymphotactin, progranulin, angiopoietin 2, hepatocyte growth factor or a combination thereof within a PLSec-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments, a PLSec-NAFLD assay may be a method of measuring protein abundance of one or more of lymphotactin, progranulin, angiopoietin 2, hepatocyte growth factor or a combination thereof within a PLSec-NAFLD for hepatocellular carcinoma risk assessment. In some embodiments,a PLSec-NAFLD assay may be a method of measuring protein abundance of two to four (e.g., 2, 3, 4,) or more of lymphotactin, progranulin, angiopoietin 2, hepatocyte growth factor or a combination thereof within a PLSec-NAFLD for hepatocellular carcinoma assessment. In some embodiments, a PLSec-NAFLD assay may be a method of measuring protein abundance of a PLSec-NAFLD for hepatocellular carcinoma risk assessment, wherein the PLSec-NAFLD may be a protein panel of lymphotactin, progranulin, angiopoietin 2 and hepatocyte growth factor.

[0176] In some embodiments, a PLSec-NAFLD assay described herein may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins within a PLSec-NAFLD. A multi-analyte profiling assay (xMAP; also known as a multiplex assay) is a type of immunoassay that uses magnetic beads to simultaneously measure multiple analytes in a single experiment. A multiplex assay is a derivative of an ELISA using beads for binding the capture antibody. Non-limiting examples of multi-analyte profiling (xMAP) assays suitable for use herein may include Myriad RBM MAP LuminexxMAP, and / or bead array assays performed on either multi-use flow cytometers (such as the commonly available clinical cytometers from Becton Dickinson, Beckman-Coulter, Dako-Cytomation, or Partec). In some embodiments, a PLSec-NAFLD assay may entail subjecting a sample collected from a subject herein to an FDA-approved multiplex clinical diagnostic technology, xMAP platform (e.g., Luminex).

[0177] In some embodiments, a PLSec-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins (e.g., 1 , 2, 3, 4) within a protein panel of lymphotactin, progranulin, angiopoietin 2, hepatocyte growth factor or a combination thereof. In some embodiments, a PLSec-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of lymphotactin, progranulin, angiopoietin 2, and hepatocyte growth factor.

[0178] In some embodiments, a PLSec-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins (e.g., 1 , 2, 3, 4) within a protein panel of lymphotactin, progranulin, angiopoietin 2, hepatocyte growth factor or a combination thereof and normalizing the protein quantification measurements. One of skill in the art will appreciate that the method of normalizing the protein quantification measurements will depend upon the specifics of the multi-analyte profiling assay used. In accordance with some of the embodiments herein, a PLSec-NAFLD assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins (e.g., 1 , 2, 3, 4) within a protein panel of lymphotactin,progranulin, angiopoietin 2, hepatocyte growth factor or a combination thereof and normalizing the protein quantification measurements to median fluorescent intensity.

[0179] In some embodiments, normalized protein quantification measurements produced by a PLSec-NAFLD assay herein may be used to generate a PLSec-NAFLD score. In some embodiments, normalized protein quantification measurements produced by a PLSec-NAFLD assay herein may be converting into an aggregated score, wherein the aggregated score is the PLSec-NAFLD score. In some embodiments, normalized protein quantification measurements produced by a PLSec-NAFLD assay herein may be converted into high or low abundance by top quartile cut-off in the optimization set, and calculated a semiquantitative score according to Formula I:wherein a high-risk protein lymphotactin and / or progranulin and a low-risk protein is angiopoietin 2 and / or hepatocyte growth factor.

[0180] In some embodiments, a subject having a PLSec-NAFLD score as determined herein below 1 may be predicted to be at low risk for developing hepatocellular carcinoma. In some embodiments, a subject having a PLSec-NAFLD score as determined herein of 1 or higher may be predicted to be at high risk for developing hepatocellular carcinoma.

[0181] In some embodiments, a sample may be further processed and analyzed to derive a PLSec score and subsequently a PLSec-AFP score. A PLSec score provides a risk for liver cancer independently of NAFLD and is described along with a PLSec-AFP score in detail in U.S, Application No. 17 / 896,944 which is incorporated by reference herein in its entirety. Briefly, a PLSec score may be derived by performing a multi-analyze profiling assay for protein quantification for “high risk proteins”: vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), and C-C motif chemokine ligand 21 (CCL-21), and “low risk proteins”: angiogenin, and protein S and converting normalized protein quantification measurements to a PLSec score using the methods described herein for the PLSec-NAFLD score (e.g., Formula I).

[0182] In some embodiments, the amount of alpha-fetoprotein (AFP) in a sample collected from a subject herein can be measured. The amount of AFP can then be integrated with the PLSec score described above to derive an integrated PLSec-AFP score using the followingequation: PLSec-AFP = 0.175 x PLSec+ 0.325 x Iog2(1+AFP), which is described in more detail in U.S. Application No. 17 / 896,944. In various aspects, a subject having an integrated PLSec-AFP score of 1.66 or higher may be classified as high risk for developing liver cancer (e.g., HCC) and a subject having an integrated PLSec-AFP score below 1.66 may be diagnosed as being at low risk for developing liver cancer (e.g., HCC).

[0183] In view of the foregoing, a single sample may be classified as low or high risk for liver cancer (e.g., HCC) using the PLSec-NAFLD score described herein and may be classified as low or high risk for liver cancer (e.g., HCC) using the PLSec-AFP score provided in U.S Application No. 17 / 896,944 and described above. In various embodiments, these two scores may be compared and integrated to generate an etiology specific etPLSec-NAFLD score. In accordance with these embodiments, an integrated, etiology specific etPLSec-NAFLD may be used to predict risk, classify, and / or diagnose liver cancer (e.g., HCC) severity in a subject herein. In various aspects, when a subject has an integrated PLSec-AFP score greater than 1.166 and a PLSec-NAFLD score greater than 1 (e.g., when both PLSec-AFP and PLSec- NAFLD scores are considered “high risk”), the subject may be diagnosed as at high risk for developing a liver cancer like HCC (i.e. , have a “high risk” etPLSec-NAFLD score). In various aspects, when a subject has an integrated PLSec-AFP score greater than 1.166 and a PLSec- NAFLD score less than 1 or when the subject has an integrated PLSec-AFP score less than 1.166 and a PLSec-NAFLD score greater than 1 (e.g., when either the PLSec-AFP or PLSec- NAFLD score is considered “high risk”, but not both), the subject may be diagnosed as at intermediate risk for developing a liver cancer like HCC (i.e., have a “intermediate risk” etPLSec-NAFLD score). Finally, in some embodiments, when a subject has an integrated PLSec-AFP score less than 1.166 and a PLSec-NAFLD score less than 1 (e.g., when both the PLSec-AFP score and PLSec-NAFLD score is considered “low risk”), the subject may be diagnosed as at low risk for developing a liver cancer like HCC (i.e., have a “low risk” etPLSec- NAFLD score).

[0184] In some embodiments, an integrated etPLSec-NAFLD score may be used to predict recurrence after one or more curative treatments of a liver cancer (e.g., HCC) is administered. Even after complete HCC tumor resection or ablation, carcinogenic tissue in the remnant liver can give rise to recurrent de novo HCC tumors, which may progress into incurable, advanced- stage disease. In some embodiments, an integrated etPLSec-NAFLD score may be used to predict recurrence within about one to about 10 years (e.g., about 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10 years) after one or more curative treatments of a liver cancer (e.g., HCC) is administered.

[0185] In some embodiments, methods of determining a PLSec-NAFLD score (or an integrated etPLSec-NAFLD score) as disclosed herein may identify a subject in need of riskbased liver cancer (e.g., HCC) screening. For subjects deemed to be at risk for developing aliver cancer and / or recovering from a liver cancer, current practice guidelines recommend regular HCC screening. Non-limited examples of liver cancer (e.g., HCC) screening methods can include measuring circulating cell-free methylated DNA, measuring a-fetoprotein (AFP), ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and the like. In some embodiments, methods of determining a PLSec-NAFLD score (or an integrated etPLSec-NAFLD score) as disclosed herein may identify a subject in need of risk-based liver cancer (e.g., HCC) screening to be performed at least once a year. In some aspects, methods of determining a PLSec-NAFLD score (or an integrated etPLSec-NAFLD score) as disclosed herein may identify a subject in need of risk-based liver cancer (e.g., HCC) screening to be performed about once a year to about six-times a year (e.g., about once, twice, three-times, four-times, five-times, six-times a year). In some examples, methods of determining a PLSec- NAFLD score (or an integrated etPLSec-NAFLD score) as disclosed herein may identify a subject in need of risk-based liver cancer (e.g., HCC) screening to be performed about twice a year.

[0186] In some embodiments, methods of diagnosing a liver cancer (e.g., HCC) in a subject may entail performing a PLSec-NAFLD assay and / or determining a PLSec-NAFLD score (or an integrated etPLSec-NAFLD score) as disclosed herein. In some embodiments, methods of diagnosing HCC in a subject having or suspected of having HCC may entail performing a PLSec-NAFLD assay and / or determining a PLSec-NAFLD score (or an integrated etPLSec- NAFLD score) as disclosed herein in addition to performing a liver biopsy, one or more blood tests to assess liver function, computed tomography, magnetic resonance imaging, or a combination thereof. In some aspects, the one or more blood tests performed to assess liver function may be a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L- lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof.

[0187] In some aspects, an MASLD subtype is identified as an i-MASLD subtype when PLSec-MASLD score comprises or consists of one or more signature proteins selected from Parkinson disease protein 7 (PARK7), soluble HGF receptor, N-cadherin, or angiopoietin-like protein 3; a p-MASLD subtype when the PLSec-MASLD score comprises or consists of one or more signature proteins selected from furin, inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), or angiopoietin-related protein 6; or an a-MASLD subtype when the PLSec-MASLD score comprises or consists of one or more signature proteins selected from cathepsin S, CCL-20, collage IV alpha 1 , autotaxin, a-FABP, osteopontin, pro-collagen I alpha I, or IGFBP- 7. A PLSeq-MASLD score for MASLD subtyping may be greater than 1 or less than 1.III. Methods of Treating Liver Cancer in a Subject

[0188] In general, methods disclosed herein include treating a subject having or suspected of having a liver cancer (e.g., HCC) by performing a PLSec-NAFLD assay to measure protein abundance of one or more of the circulating proteins associated with PLSec-NAFLD as disclosed herein, obtaining a PLSec-NAFLD score from the PLSec-NAFLD assay results, and administering the appropriate treatment based on the PLSec-NAFLD score. In some embodiments, treatment after determining the PLSec-NAFLD score as disclosed herein may depend on if the PLSec-NAFLD score is indicative of a high risk for hepatocellular carcinoma (e.g., greater than or equal to 1) or a low risk for hepatocellular carcinoma (e.g., less than 1).

[0189] The methods described herein also provide for MASLD (NAFLD) subtyping,. MASLD subtyping may be combined with any of the methods described herein. The subtyping may be relative to relative to fibrosis-4 (FIB-4) index or its change.

[0190] Further methods disclosed herein include treating a subject having or suspected of having a hepatocellular carcinoma by performing a PLS-NAFLD assay to measure gene expression of one or more of the genes associated with PLS-NAFLD as disclosed herein, obtaining a PLS-NAFLD score from the PLS-NAFLD assay results, and administering the appropriate treatment based on the PLS-NAFLD score. In some embodiments, treatment after determining the PLS-NAFLD score as disclosed herein may depend on if the PLS-NAFLD score is indicative of a high risk for hepatocellular carcinoma (e.g., greater than +1.30103, corresponding to a prediction confidence p value of 0.05 with proximity to the high-risk reference profile) or a low risk for hepatocellular carcinoma (e.g., less than -1.30103, corresponding to a prediction confidence p value of 0.05 with proximity to the low-risk reference profile) or an intermediate risk for hepatocellular carcinoma (e.g., between -1.30103 and +1.30103, corresponding to a prediction confidence p value of 0.05 with proximity to either of the high-risk reference profile or low-risk reference profile).

[0191] A suitable tailored treatment approach for liver cancer (e.g., HCC) as used herein may be selected based on the subject’s diagnosis and / or classification of cancer severity. In some embodiments, a subject can be diagnosed with liver cancer (e.g., HCC) based on protein abundance of one or more circulating protein markers that make up a serum-protein-based PLSec-NAFLD as disclosed herein. In some embodiments, a subject can be predicted to have a high or low risk for liver cancer (e.g., HCC) based on increased protein abundance of one or more circulating protein markers that make up a serum-protein-based PLSec-NAFLD as disclosed herein. In some embodiments, a subject can be classified as having a high or low risk for liver cancer (e.g., HCC) based on increased protein abundance of one or more circulating protein markers that make up a serum-protein-based PLSec-NAFLD as disclosed herein.

[0192] The PLSec-NAFLD and PLS-NAFLD scores herein are provided to address the unique needs of individuals with non-alcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) because of the known link between these diseases and liver cancer. In some embodiments, a subject having or suspected of having non-alcoholic fatty liver disease can be diagnosed and / or predicted to have high or low risk for hepatocellular carcinoma based on methods of determining a PLS-NAFLD and / or PLSec-NAFLD score as disclosed herein in addition to an assessment of at least one other disease, condition, or combination thereof that predisposes the subject to hepatocellular carcinoma (e.g., other than NAFLD or NASH). In some embodiments, further assessment of at least one other disease, condition, or combination thereof that predisposes the subject to liver cancer (e.g., HCC) may include at diagnosis and / or a determination of severity of chronic infection of hepatitis B virus (HBV), chronic infection of hepatitis C virus (HCV), hereditary hemochromatosis, type 2 diabetes, obesity, tobacco use, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alphal-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage diseases, Wilson disease, or a combination thereof. Methods of diagnosing these diseases and conditions are known in the art. (See e.g., HARRISON'S PRINCIPLES OF INTERNAL MEDICINE, 18e. New York, NY: McGraw-Hill; 2012).

[0193] A suitable tailored treatment approach for liver cancer (e.g., HCC) as used herein may be selected based on the subject’s diagnosis and / or classification of cancer severity. In some embodiments, a subject can be diagnosed with liver cancer (e.g., HCC) based on gene expression levels of one or more gene markers that make up PLS-NAFLD as disclosed herein. In some embodiments, a subject can be predicted to have a high or low risk for liver cancer (e.g., HCC) based on increased gene expression of one or more gene markers that make up a PLS-NAFLD as disclosed herein. In some embodiments, a subject can be classified as having a high or low risk for liver cancer (e.g., HCC) based on increased gene expression of one or more gene markers that make up PLSec-NAFLD as disclosed herein.

[0194] In some embodiments, a subject can be identified as having or diagnosed with an indolent-MASLD (i-MASLD) subtype; a progressive-MASLD (p-MASLD) subtype; or an advanced-MASLD (a-MASLD) subtype.

[0195] In some embodiments, a subject can be diagnosed and / or predicted to have high or low risk for liver cancer (e.g., HCC) based on methods of determining a PLS-NAFLD and / or PLSec-NAFLD score as disclosed herein in addition to at least one other method of diagnosing liver cancer (e.g., HCC), optionally they identified as having or diagnosed with an indolent- MASLD (i-MASLD) subtype; a progressive-MASLD (p-MASLD) subtype; or an advanced- MASLD (a-MASLD) subtype. In some embodiments, an additional method of diagnosing liver cancer (e.g., HCC) that can be used in addition to determination of PLS-NAFLD and / or PLSec-NAFLD score may be histological or imaging-based (contrast-enhanced multiphase CT, ultrasound, and / or MRI) examinations according to the American Association of the Study of Liver Disease (AASLD) practice guidelines. Imaging features used to diagnose an HCC include size, kinetics, and pattern of contrast enhancement, and growth on serial imaging wherein size may be measured as the maximum cross-section diameter on the image where the lesion is most clearly seen. The histologic appearance of HCC can range from well- differentiated (with individual hepatocytes appearing nearly identical to normal hepatocytes) to poorly differentiated lesions consisting of pleomorphic tumor cells in a solid or compact growth pattern wherein central necrosis of large tumors can be commonly observed.

[0196] In some embodiments, a PLS-NAFLD and / or PLSec-NAFLD score may be obtained using the methods herein to determine one or more treatment options for liver cancer (e.g., HCC) in a subject. In some embodiments, a PLS-NAFLD and / or PLSec-NAFLD score may be obtained using the methods herein to determine one or more treatment options for liver cancer (e.g., HCC) in a subject in conjunction with one or more additional factors. In some aspects, treatment options for liver cancer (e.g., HCC) in a subject herein may depend on a PLS-NAFLD and / or PLSec-NAFLD score as disclosed herein and one or more of the following additional factors: size, number, and location of tumors; presence or absence of cirrhosis; operative risk based on extent of cirrhosis and comorbid diseases; overall performance status; portal vein patency; presence or absence of metastatic disease, or a combination thereof.

[0197] In some embodiments, a PLS-NAFLD and / or PLSec-NAFLD score may be obtained using the methods herein to determine one or more treatment options for liver cancer (e.g., HCC) in a subject wherein the one or more treatments may include surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, or a combination thereof. In some aspects, surgical treatments of liver cancer (e.g., HCC) can include, but are not limited to, intra-arterial brachytherapy (IAB), transarterial chemoembolization (TACE), surgical resection, radiofrequency ablation (RFA), and the like. In some aspects, drug therapy of liver cancer (e.g., HCC) can include administration of one or more drugs to the subject, wherein the drugs are comprised of sorafenib, regorafenib, nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof. In some aspects, chemotherapy may comprise administration of one or more platinum-based chemotherapeutics. As used herein, a “platinum-based chemotherapeutic” is a chemotherapeutic that is an organic compound which contains platinum as an integral part of the molecule. In some embodiments, compositions of use herein can contain one or more platinum-based chemotherapeutics including, but not limited to, cisplatin, carboplatin, nedaplatin, triplatin tetranitrate, phenanthriplatin, picoplatin, satraplatin or a combinationthereof. In some embodiments, a platinum-based chemotherapeutic can be administered separately from the compounds disclosed herein. In some embodiments, the platinum-based chemotherapeutic or salt thereof or derivative thereof includes cisplatin. In certain embodiments, platinum-based chemotherapeutic agents can be administered to a subject alone or in combination with at least one drug therapy (e.g., sorafenib, regorafenib), daily, every other day, twice weekly, every other day, every other week, weekly or monthly or other suitable dosing regimen. One of skill in the art will appreciate that dosing regimens can vary and require optimization for a subject to be treated based on the various factors such as that subject’s age, weight, gender, renal / liver function, and the like. In some embodiments, any of the methods disclosed herein can further include monitoring occurrence of one or more adverse effects in the subject having a PLSec score indicative of a high-risk for liver cancer (e.g., HOC). Adverse effects may include, but are not limited to, hepatic impairment, hematologic toxicity, neurologic toxicity, cutaneous toxicity, gastrointestinal toxicity, or a combination thereof. When one or more adverse effects are observed, the methods disclosed herein can further include reducing or increasing the dose of one or more of the treatment regimens depending on the adverse effect or effects in the subject. For example, when a moderate to severe hepatic impairment is observed in a subject after treatment, compositions of use to treat the subject can be reduced in concentration or frequency of dosing with one or more disclosed drugs (e.g., sorafenib, regorafenib), the dose or frequency of the chemotherapeutics can be adjusted, or a combination thereof.

[0198] In some embodiments, a PLS-NAFLD and / or PLSec-NAFLD score may be obtained using the methods herein to determine one or more chemoprevention options for preventing liver cancer (e.g., HCC) in a subject. As used herein, “chemoprevention” refers to administration of one or more agents to prevent cancer from occurring and / or reoccurring in a subject. In some non-limiting examples, a medication, vitamin and / or supplement may be administered as chemoprevention agent. In some embodiments, a subject herein may be administered one or more chemoprevention agents after determination of their PLS-NAFLD and / or PLSec-NAFLD score. In some embodiments, a subject having a PLSec-NAFLD score equal to or greater than 1 may be administered one or more chemoprevention agents. In some embodiments, a subject having a PLS-NAFLD score equal to or greater than -1.30103 may be administered one or more chemoprevention agents. In some aspects, chemoprevention agents suitable for administration to a subject herein after determination of the PLSec score in the subject can include an antiviral (e.g., tenofovir disoproxil fumarate), a statin (e.g., simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin), an anti-diabetic (e.g., metformin), a dietary and / or nutritional agent (e.g., polyunsaturated fatty acids (PUFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA),Branched-chain amino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe)), an antiinflammatory (e.g., celecoxib, aspirin), an immunomodulatory (e.g., thalidomide, thymalfasin), or a combination thereof.

[0199] In certain embodiments, the PLS-NAFLD and / or PLSec-NAFLD score may be monitored in an individual before and after treatment. In some cases, changes in the PLS- NAFLD and / or PLSec-NAFLD score may lead to continuing or discontinuing the treatment. For example, if the PLS-NAFLD and / or PLSec-NAFLD score in a subject decreases after treatment, the treatment may be continued. If the PLS-NAFLD and / or PLSec-NAFLD score in a subject increases or doesn’t change after treatment, the treatment may be discontinued.

[0200] In some aspects, treatment of a subject after determining the PLS-NAFLD and / or PLSec-NAFLD score as disclosed herein, may prevent liver cancer progression. In some aspects, treatment of a subject after determining the PLS-NAFLD and / or PLSec-NAFLD score as disclosed herein, may ameliorate one or more symptoms associated with liver cancer (e.g., HCC). In still other aspects, treatment of a subject after determining the PLS-NAFLD and / or PLSec-NAFLD score as disclosed herein, may reduce risk of liver cancer recurrence in the subject. In other aspects, treatment of a subject after determining the PLS-NAFLD and / or PLSec-NAFLD score as disclosed herein, may slow tumor growth in the liver of the subject. In some other aspects, treatment of a subject after determining the PLS-NAFLD and / or PLSec- NAFLD score as disclosed herein, may reduce the risk of metastasis in the subject.

[0201] In some embodiments, methods of treatment disclosed herein can impair liver tumor growth compared to liver tumor growth in an untreated subject with identical disease condition and predicted outcome. In some embodiments, liver tumor growth can be stopped following treatments according to the methods disclosed herein. In other embodiments, liver tumor growth can be impaired at least about 5% or greater to at least about 100%, at least about 10% or greater to at least about 95% or greater, at least about 20% or greater to at least about 80% or greater, at least about 40% or greater to at least about 60% or greater compared to an untreated subject with identical disease condition and predicted outcome. In other words, liver tumors in subject treated according to the methods disclosed herein grow at least 5% less (or more as described above) when compared to an untreated subject with identical disease condition and predicted outcome. In some embodiments, liver tumor growth can be impaired at least about 5% or greater, at least about 10% or greater, at least about 15% or greater, at least about 20% or greater, at least about 25% or greater, at least about 30% or greater, at least about 35% or greater, at least about 40% or greater, at least about 45% or greater, at least about 50% or greater, at least about 55% or greater, at least about 60% or greater, at least about 65% or greater, at least about 70% or greater, at least about 75% or greater, at least about 80% or greater, at least about 85% or greater, at least about 90% orgreater, at least about 95% or greater, at least about 100% compared to an untreated subject with identical disease condition and predicted outcome. In some embodiments, liver tumor growth can be impaired at least about 5% or greater to at least about 10% or greater, at least about 10% or greater to at least about 15% or greater, at least about 15% or greater to at least about 20% or greater, at least about 20% or greater to at least about 25% or greater, at least about 25% or greater to at least about 30% or greater, at least about 30% or greater to at least about 35% or greater, at least about 35% or greater to at least about 40% or greater, at least about 40% or greater to at least about 45% or greater, at least about 45% or greater to at least about 50% or greater, at least about 50% or greater to at least about 55% or greater, at least about 55% or greater to at least about 60% or greater, at least about 60% or greater to at least about 65% or greater, at least about 65% or greater to at least about 70% or greater, at least about 70% or greater to at least about 75% or greater, at least about 75% or greater to at least about 80% or greater, at least about 80% or greater to at least about 85% or greater, at least about 85% or greater to at least about 90% or greater, at least about 90% or greater to at least about 95% or greater, at least about 95% or greater to at least about 100% compared to an untreated subject with identical disease condition and predicted outcome.

[0202] In some embodiments, treatment of liver tumors according to the methods disclosed herein can result in a shrinking of a liver tumor in comparison to the starting size of the liver tumor. In some embodiments, liver tumor shrinking may be at least about 5% or greater to at least about 10% or greater, at least about 10% or greater to at least about 15% or greater, at least about 15% or greater to at least about 20% or greater, at least about 20% or greater to at least about 25% or greater, at least about 25% or greater to at least about 30% or greater, at least about 30% or greater to at least about 35% or greater, at least about 35% or greater to at least about 40% or greater, at least about 40% or greater to at least about 45% or greater, at least about 45% or greater to at least about 50% or greater, at least about 50% or greater to at least about 55% or greater, at least about 55% or greater to at least about 60% or greater, at least about 60% or greater to at least about 65% or greater, at least about 65% or greater to at least about 70% or greater, at least about 70% or greater to at least about 75% or greater, at least about 75% or greater to at least about 80% or greater, at least about 80% or greater to at least about 85% or greater, at least about 85% or greater to at least about 90% or greater, at least about 90% or greater to at least about 95% or greater, at least about 95% or greater to at least about 100% (meaning that the liver tumor is completely gone after treatment) compared to the starting size of the liver tumor.

[0203] In various embodiments, treatments administered according to the methods disclosed herein can improve patient life expectancy compared to the life expectancy of an untreated subject with identical disease condition (e.g., liver cancer) and predicted outcome. As usedherein, “patient life expectancy” is defined as the time at which 50 percent of subjects are alive and 50 percent have passed away. In some embodiments, patient life expectancy can be indefinite following treatment according to the methods disclosed herein. In other aspects, patient life expectancy can be increased at least about 5% or greater to at least about 100%, at least about 10% or greater to at least about 95% or greater, at least about 20% or greater to at least about 80% or greater, at least about 40% or greater to at least about 60% or greater compared to an untreated subject with identical disease condition and predicted outcome. In some embodiments, patient life expectancy can be increased at least about 5% or greater, at least about 10% or greater, at least about 15% or greater, at least about 20% or greater, at least about 25% or greater, at least about 30% or greater, at least about 35% or greater, at least about 40% or greater, at least about 45% or greater, at least about 50% or greater, at least about 55% or greater, at least about 60% or greater, at least about 65% or greater, at least about 70% or greater, at least about 75% or greater, at least about 80% or greater, at least about 85% or greater, at least about 90% or greater, at least about 95% or greater, at least about 100% compared to an untreated subject with identical disease condition and predicted outcome. In some embodiments, patient life expectancy can be increased at least about 5% or greater to at least about 10% or greater, at least about 10% or greater to at least about 15% or greater, at least about 15% or greater to at least about 20% or greater, at least about 20% or greater to at least about 25% or greater, at least about 25% or greater to at least about 30% or greater, at least about 30% or greater to at least about 35% or greater, at least about 35% or greater to at least about 40% or greater, at least about 40% or greater to at least about 45% or greater, at least about 45% or greater to at least about 50% or greater, at least about 50% or greater to at least about 55% or greater, at least about 55% or greater to at least about 60% or greater, at least about 60% or greater to at least about 65% or greater, at least about 65% or greater to at least about 70% or greater, at least about 70% or greater to at least about 75% or greater, at least about 75% or greater to at least about 80% or greater, at least about 80% or greater to at least about 85% or greater, at least about 85% or greater to at least about 90% or greater, at least about 90% or greater to at least about 95% or greater, at least about 95% or greater to at least about 100% compared to an untreated patient with identical disease condition and predicted outcome.IV. Kits

[0204] The present disclosure provides kits for performing any of the methods disclosed herein. In some aspects, the present disclosure provides a kit for determining expression of one or more markers of liver cancer (e.g., HCC) or MASLD subtyping as disclosed herein, or for diagnosing the cancer. Such a kit may comprise a means for determining any of the combinations proteins that make up a panel of circulating proteins referred to as a serum-protein-based PLSec-NAFLD as disclosed herein. Alternatively, such kits may comprise a means for determining any of the combination genes that make up a panel of genes referred to as PLS-NAFLD as disclosed herein.

[0205] In some embodiments, the means for determining expression of one or more circulating proteins of PLSec-NAFLD as disclosed herein may have a set of antibodies, peptides, aptamers, or a combination thereof. In some examples, a means for determining expression of one or more circulating proteins of PLSec-NAFLD disclosed herein may have a set of antibodies / antigens. Each of the antibodies / antigens can detect a target circulating protein of PLSec-NAFLD in the combination and the whole set, collectively, may be designed for detecting at least one, at least two, at least three, or at least four PLSec-NAFLD proteins (e.g., lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor,) in combination. Design of such antibodies / antigens for detecting a particular protein using xMAP assay is within the knowledge of a skilled person in the art. See, e.g., Sambrook et al., MOLECULAR CLONING— A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989).

[0206] Likewise, in some embodiments, the means for determining expression of one or more genes of PLS-NAFLD as disclosed herein may have a set of nucleic acid probes, primers, oligonucleotides or any other means to detect levels of a nucleic acid. For example, in some aspects the means for determining the gene expression profile of one or more genes of PLS- NAFLD disclosed herein may be a set of nucleic acid probes labeled with color-coded microbeads to mRNA transcribed from one or more genes in the PLS-NAFLD assay (e.g., ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof). In someexamples, a means for determining the gene expression profile of one or more genes of PLS- NAFLD disclosed herein may be a set of primers and / or oligonucleotides. Each oligonucleotide may detect a target gene or an mRNA transcribed from a target gene of PLS- NAFLD in the combination and the whole set, collectively, may be designed for detecting at least 2, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 110, at least 120 or at least 130 PLS- NAFLD genes (e.g., ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA- DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA- RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof). Design of such oligonucleotides, primers or probes for detecting expression of particular genes using microarrays is within the knowledge of a skilled person in the art. See, e.g., Sambrook et al. et al., MOLECULAR CLONING— A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). In exemplary embodiments, the means for determining expression of one or more genes of PLS-NAFLD herein may include standard components included in an nCounter® assay (NanoString Inc).

[0207] In some embodiments, kits disclosed herein can have a solid support member, on which the set of antibodies or nucleic acids (e.g., “probes”) can be immobilized. In some examples, kits disclosed herein may comprise a platform comprising a support member, on which the set of probes can be immobilized. The probes may have oligonucleotide or peptide molecules that bind to a specific target molecule. The support member in the platform may be either porous or non-porous. For example, the probes may be attached to a nitrocellulose or nylon membrane or to a bead. Alternatively, the support member may have a glass or plastic surface. In some examples, the solid phase may be a nonporous or, optionally, a porous material such as a gel.

[0208] In some embodiments, a platform array may comprise a support member with an ordered array of binding (e.g., hybridization) sites or “probes” each representing one of the target protein or gene markers described herein. Preferably the platform arrays are addressable arrays, and more preferably positionally addressable arrays. For example, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). In preferred embodiments, each probe is covalently attached to a solid support. In some aspects, the solid support may be a bead.

[0209] Any of the kits disclosed herein may further comprise a container for placing a biological sample, and optionally a tool for collecting a biological sample from a subject. Alternatively, or in addition, the kit may further comprise one or more reagents for determining protein levels of the one or more circulating proteins of PLSec-NAFLD as disclosed herein from the biological sample. In some examples, the kit may comprise reagents for immunodetection of one or more circulating proteins of PLSec-NAFLD as disclosed herein. Alternatively, or in addition, the kit may further comprise one or more reagents for determining gene expression levels of the one or more genes of PLS-NAFLD as disclosed herein from the biological sample. In some examples, the kit may comprise reagents for detecting gene expression of one or more genes in PLS-NAFLD as disclosed herein using a microarray. In other examples, the kit may comprise reagents for hybridization.

[0210] Any of the kits may further comprise an instruction manual providing guidance for using the kit to determine a protein panel and / or gene expression profile having any combination of the one or more circulating proteins of PLSec-NAFLD and / or one or more genes of PLS- NAFLD as disclosed herein.

[0211] Further, any of the kits disclosed herein may comprise a processor, e.g., a computational processor, for assessing abundance of one or more of the circulating proteins of PLSec-NAFLD and / or one or more expressed genes of PLS-NAFLD as disclosed herein. Such a processor may be configured with a regression model such as those disclosed herein. By inputting the marker profile (e.g., the protein expression level of circulating proteins of PLSec-NAFLD or the gene expression profile of genes of PLS-NAFLD), the processor may process the information to diagnose liver fibrosis and optionally diagnose the level of liver fibrosis severity by generating a PLSec-NAFLD score and / or a PLS-NAFLD score according to the methods disclosed herein.

[0212] Having described several embodiments, it will be recognized by those skilled in the art that various modifications, alternative constructions, and equivalents may be used without departing from the spirit of the present disclosure. Additionally, a number of well-knownprocesses and elements have not been described in order to avoid unnecessarily obscuring the present disclosure. Accordingly, this description should not be taken as limiting the scope of the present disclosure.

[0213] Those skilled in the art will appreciate that the presently disclosed embodiments teach by way of example and not by limitation. Therefore, the matter contained in this description or shown in the accompanying drawings should be interpreted as illustrative and not in a limiting sense. The following claims are intended to cover all generic and specific features described herein, as well as all statements of the scope of the method and assemblies, which, as a matter of language, might be said to fall there between.EXAMPLES

[0214] The following examples are included to demonstrate preferred embodiments of the disclosure. It should be appreciated by those of skill in the art that the techniques disclosed in the examples that follow represent techniques discovered by the inventor to function well in the practice of the present disclosure, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the present disclosure.Example 1 - Introduction to Examples

[0215] Nonalcoholic fatty liver disease (NAFLD), alongside the global obesity epidemic, is rapidly emerging as a dominant liver disease etiology that leads to hepatocellular carcinoma and its terminal stage, cirrhosis. Cirrhosis is a well-established high-risk condition for developing hepatocellular carcinoma (HCC), the fastest rising cancer-related mortality in the United States. Given the survival benefit of diagnosing early-stage HCC, practice guidelines recommend semi-annual HCC screening in patients with cirrhosis. However, in real-world clinical practice, regular screening is applied in <25% of at-risk patients, resulting in frequent late-stage diagnoses and poor survival. These gaps in care highlight an issue of insufficient medical resources to conduct recommended ultrasound-based screening among the large at- risk population. Furthermore, HCC screening in NAFLD is more challenging compared to other etiologies such as viral hepatitis due to the vast size of the patient population and associated low HCC incidence rate; the estimated global prevalence of NAFLD is 25%, whereas annual HCC incidence rate is only 0.1 to 0.6% in biopsy-proven NAFLD cirrhosis, which is below the cutoff of 1.5% to justify HCC screening as cost effective. Thus, HCC risk prediction is particularly critical in NAFLD to identify a subset of patients with elevated likelihood of developing HCC among the larger, mostly indolent NAFLD population.

[0216] To date, several clinical risk scores have been proposed to identify patients with elevated HCC risk, although refined risk prediction is needed to further reduce the number of patients with NAFLD who should be closely monitored by HCC screening. Biomarkers predictive of long-term HCC risk using liver tissue [prognostic liver signature (PLS)] and serum [prognostic liver secretome signature (PLSec)] have been developed in patients with chronic liver disease from viral and metabolic etiologies, including NAFLD. However, there is still room for improvement, especially in identifying patients with negligible risk, who may not require HCC screening, and identifying those at highest risk to whom intensive screening efforts could be targeted. To address the challenge, the following examples describe a NAFLD-specific HCC risk signature as a “plug-in” module to refine the etiology-agnostic risk prediction using prospective specimen collection, retrospective blinded evaluation (PRoBE) design for biomarker validation.Example 2 - Overall Study Design

[0217] Study design and patient demographics are summarized in FIG. 1 and Tables 1A and 1B. Because HCC incidence rate is low in HCC-naTve NAFLD patients (< 1% per year) (6, 7), it is challenging to identify a molecular signature reliably associated with time to HCC development with sufficient statistical power. FIG. 6A depicts in the left and right panels scenarios where the proportion of patients with a positive biomarker is 50% or 33% (or 67%), respectively. For example, when annual incidence rate is 0.5%, proportion of patients who develop an event is 2.5% during 5 years of follow-up, and 1 ,496 or 1 ,692 patients would be required under an assumption that biomarker positivity is 50% or 33% (or 67%), respectively. This challenge was previously addressed during development of the etiology-agnostic PLS by defining the biomarker in patients with a history of prior HCC based on the following assumptions: (i) their livers are already primed as soil for metachronous and multicentric carcinogenesis with approximately three-time higher incidence rate compared to HCC-naTve patients, and (ii) HCC incidences (clinically recognized as HCC recurrences) in these patients are likely de novo HCC recurrence, i.e., metachronous recurrence clonally independent from previously treated tumor, as shown in our previous loss-of-heterozygosity analysis. Subsequently, PLS was successfully validated in independent cohorts of HCC-naTve as well as HCC-experienced patients. Herein, the same strategy was used to identify and validate a NAFLD-specific HCC risk signature (PLS-NAFLD). First signature was defined in a cohort of 48 patients with prior history of HCC (derivation set) for emergence of de novo HCC recurrence (FIG. 6B). It is clinically well established that there are two distinct types of HCC recurrence, namely “disseminative recurrence”, which develops from microscopic tumor cell dissemination from the treated primary HCC tumor, and “de novo recurrence”, which develops from remnant cirrhotic liver as metachronous cancer clonally unrelated to the treated primaryHCC tumor as confirmed by SNP array-based loss of heterozygosity (LOH) analysis. In clinical practice, disseminative recurrence is typically observed within 2 years of treating primary HCC tumor, and de novo recurrence gradually increases beyond 2 years after the treatment of primary HCC tumor. These two types of recurrence can be recognized as two distinct peaks in recurrence hazard plot over time after the treatment of primary HCC tumor (i.e., the first peak of disseminative recurrence within 2 years and second peak of de novo recurrence after 2 years). When HCC tumor is diagnosed at early stage and curatively treated with rigorous confirmation of no residual tumor, the first peak of disseminative recurrence disappears as observed in the PLS-NAFLD derivation set (FIG. 6B, left) in contrast to the patients with more advanced HCC tumors (FIG. 6B, right) (patients with > 5 cm tumor, 0% and 38%, respectively; Fisher’s exact test, p<0.001).

[0218] The signature was subsequently validated in independent cohorts of 106 HCC-naTve NAFLD patients (tissue validation set 1) and 59 HCC-experienced patients who previously underwent curative surgical tumor resection (tissue validation set 2). Further, PLS- NAFLD was translated to a serum-protein panel, PLSec-NAFLD, using the computational algorithm, TexSEC, and validated it in an independent cohort of 59 HCC-naTve NAFLD cirrhosis patients (serum validation cohort) and externally validated it in an independent cohort of 59 HCC-naive patients with NAFLD and cirrhosis (serum validation set). The objectives were to confirm that NAFLD-specific HCC risk biomarker achieves risk stratification with hazard ratio (HR) > 2 that would enable cost-effective risk-stratified HCC screening and to confirm improved prognostic prediction by our etiology-agnostic HCC risk biomarker, PLS / PLSec. Prognostic association of the etiology-agnostic PLS in the derivation and tissue validation sets was limited, supporting exploration of NAFLD-specific biomarker to refine the risk prediction (FIGS. 6C-6E).TABLE 1A: Clinical demographics of derivation and validation setsTABLE 1B: Clinical demographics of derivation and validation sets- Single nucleotide polymorphism (SNP)*Example 3 - Derivation of a tissue-based NAFLD HCC risk signature, PLS-NAFLD

[0219] In the derivation set, a 133-gene PLS-NAFLD was defined, consisting of 80 high- and 53 low-risk genes (TABLE 2), which classified 19 (40%), 9 (18%), and 20 (42%) patients into high-, intermediate-, and low-risk groups, respectively. Of note, high- and intermediate-risk groups showed similar HCC incidence rates (FIG. 7A), suggesting that the main utility of this signature is to distinguish patients with low risk of HCC development. Indeed, annual rate of (assumedly metachronous) HCC recurrence was substantially less frequent in the low-risk group (7.2%) compared to the rest (60.4%). Therefore, for subsequent validation, the highland intermediate-risk groups were merged as “high-risk” to be compared with the low-risk group (FIGS. 2A-2B). It is noteworthy that PLS-NAFLD was independent of any clinical, histological, or NAFLD-related genetic features, including fibrosis stage and single nucleotide polymorphisms, supporting that PLS-NAFLD provides complementary prognostic information beyond these clinically available variables (FIGS. 7C-7E). High-risk PLS-NAFLD showed a robust prognostic association in various multivariable models adjusted for potential confounding variables (TABLES 3A-3C, TABLE 4). None of previously reported gene expression signatures for NAFLD was predictive of long-term HCC risk (TABLE 3D). There was no prognostic association of PLS-NAFLD in patients affected with other etiologies such as hepatitis C, hepatitis B, or alcohol-related liver disease (FIGS. 7F-7I), supporting NAFLD specificity of the signature.TABLE 2: PLS-NAFLD signature genesTABLE 3A: Prognostic association of PLS-NAFLD and clinical / molecular variables with HCC risk in the derivation set using univariable Cox regression: General VariablesTABLE 3B: Prognostic association of PLS-NAFLD and clinical / molecular variables with HCC risk in the derivation set using univariable Cox regression: Single Nucleotide PolymorphismsTABLE 3C: Prognostic association of PLS-NAFLD and clinical / molecular variables with HCC risk in the derivation set using univariable Cox regression: HistologyTABLE 3D: Prognostic association of PLS-NAFLD and clinical / molecular variables with HCC risk in the derivation set using univariable Cox regression: Hepatic-transcriptome- based signatures for NAFLD in literatureTABLE 4: Prognostic Association of PLS / PLSec-NAFLD and etPLSec-NAFLD with HCC risk in the serum validation set using multivariable Cox regressionModel 1 includes age, sex, and advanced fibrosis defined as F-stage 23. Model 2 includes model 1 + HCC stage (AJCC stage >1). Model 3 includes model 2 + ALT (as continuous). ALT showed a significant association with HCC risk in univariable analyses in the derivation set.* High-risk vs. low-risk, f Firth’s correction was applied to account for the monotone likelihood problem arising due to perfect separation of group and events (43) PLS, prognostic liver signature; PLSec, prognostic liver secretome signature; et, etiology-specific; NAFLD, non-alcoholic fatty liver disease;HR, hazard ratio; Cl, confidence interval.Example 4 - Molecular dysregulations encoded in PLS-NAFLD in hepatic cells

[0220] Molecular pathway analyses revealed that the high-risk pattern of PLS-NAFLD was associated with T cell-mediated immune reaction, B cell activation, and macrophage migration, as well as T cell exhaustion (FIG. 71 and TABLE 5). Specifically, the high-risk genes include HLA-A, HLA-DPA1, and HLA-G as well as ITGAX, encoding CD11c (dendritic cell [DC] marker), suggesting involvement of antigen-presenting cells in NAFLD- associated hepatocarcinogenesis. Tryptophan hydroxylase 1 encoded by THP1 in hepatocytes produces serotonin, which activates hepatic stellate cell (HSC) and accelerates hepatocarcinogenesis via transforming growth factor p (TGF ) as a potential mechanism underlying sex disparity in HCC risk. Histidine decarboxylase encoded by HDC is a histamine-producing enzyme, whose receptor blocker, nizatidine, was found as a potential HCC chemopreventative in our recent study with a PLS-inducible cell culture system. The low-risk genes include large tumor suppressor kinase 2 encoded by LATS2 that suppresses oncogenic transcription factor, Yap1 , in the Hippo pathway, and its overexpression can inhibit mutant - catenin-induced hepatocarcinogenesis in mice.TABLE 5: Molecular pathways associated with PLS-NAFLD in the derivation set*Hallmark are Meta-analysis pathway targets; All remaining datasets represent curated pathway members.

[0221] High-risk PLS-NAFLD was also associated with pathways involved in NAFLD driven disease progression and hepatocarcinogenesis, including the tumor necrosis factor pathway, autophagy, and yes-associated protein (YAP) signaling, and disrupted circadian rhythm. Activation of signal transducer and activator of transcription 3 (STAT3), but not STAT1 , was observed in patients with high-risk PLS-NAFLD. In contrast, metabolic pathway regulators involved in homeostatic maintenance of normal liver function, particularly bile acid metabolism as indicated by fibroblast growth factor 19 (FGF19) and FGF21 and nuclear receptor signaling, for example, the farnesoid X receptor (FXR) pathway, were down-regulated in association with high-risk PLS-NAFLD. A transcriptional target gene signature of an FXR agonist, obeticholic acid, was also suppressed, suggesting that patients with high-risk PLS-NAFLD prediction may be candidates for obeticholic acid treatment. Despite no prognostic association with histological inflammation grade (TABLES 3A-3D), PLS-NAFLD encoded diverse inflammatory pathways. This highlights limitation of gross histological evaluation of inflammation (semiquantitative scoring of lymphocyte infiltration) and the importance of characterizing immune cell subsets and their functional status in predicting long-term HCC risk over up to 11 years.

[0222] Next, cellular sources of the high-risk PLS-NAFLD were determined based on a meta- analysis of four human hepatic single-cell RNA sequencing (scRNAseq) datasets (108,855cells in total) representing all major cell types anticipated in healthy to NAFLD-affected cirrhotic livers (TABLE 6).TABLE 6: Hepatic single-cell RNA-seq datasets for meta-analysis of human NAFLD and healthy liversImmunol. 37, 193-207 (2016); (3) M. Elosua-Bayes et al., Nucleic Acids Res. 49, e50 (2021); (4) M. Efremova, et al., Nat. Protoc. 15, 1484-1506 (2020).

[0223] Thirty-one (31) single-cell clusters (SC01 to SC31) were identified based on similarity of transcriptome pattern (FIG. 2C, FIG. 8A, and TABLE 7). As expected, a uniform manifold approximation and projection plot showed clustering of the cells by cell type not by dataset (FIG. 2C and FIG. 8B). Both NAFLD-associated cell types such as C-X-C chemokine receptor type 6 (CXCR6+) Programmed cell death protein 1 (PD-1+) CD8 T cells and Toll-like receptor4 (TLR4+) Kupffer cells were confirmed to be present in the liver cell clusters (FIG. 8C), and some of were associated with the high-risk pattern of PLS-NAFLD (FIG. 2A).Table 7 - Hepatic single-cell cluster annotation based on meta-analyses of single-cell transcriptome profiles of human NAFLD and healthy livers.LC, innate lymphoid cell; MP, mononuclear phagocyte; NK, natural killer; pDC, plasmacytoid dendritic cell.

[0224] The high-risk PLS-NAFLD score defined by average relative expressions of signature genes was mainly associated with immune cell clusters, whereas the low-risk score was mostly associated with the endothelial cell clusters (FIG. 2D). Within the mononuclear phagocyte cluster, high- and low-risk scores were enriched in distinct subclusters (for example, high- and low-risk genes in clusters SC29 and SC10, respectively) (FIG. 2D and FIG. 8D). It was found that the high-risk-related SC29 cluster contained X-C motif chemokine receptor 1 (XCR1+) type 1 conventional dendritic cells (DCs) implicated in NAFLD progression but not yet in HCC risk (FIG. 8E and FIG. 8F). In contrast, the low-risk-related SC10 cluster contained bone marrow-derived monocytes, which are located at the bifurcation of differentiation to either matured macrophages or DCs in hepatic cirrhosis. These results suggest that the mutually exclusive presence of these cell types contributes to the generation of an HCC-prone hepatic microenvironment in NAFLD.

[0225] Across all cell type clusters, it was found that cells in SC29 almost exclusively expressed IDO1, encoding indoleamine 2,3-dioxygenase 1 (IDO1) (FIG. 8G). IDO1 is a rate-limiting enzyme that converts L-tryptophan to L-kynurenine, which suppresses antitumor immunity via induction of tolerogenic conventional DCs (eDCs) and T cell anergy. Enrichment of SC29 signature significantly correlated with PLS-NAFLD-based risk prediction (rho = 0.44; P = 0.002) (FIG. 2A), suggesting that IDO1 may be a therapeutic target to modulate high-risk PLS-NAFLD and reduce HCC incidence in NAFLD.

[0226] PLS-NAFLD was then spatially mapped onto histological architecture in human NAFLD-affected liver (F-stage, 2; NAFLD activity score, 5) using genome-wide spatial transcriptome profiling (FIG. 9A). First, we determined portal tract, periportal, mid-lobular, and pericentral areas (based on grid-like regions called “spots” in the assay) according to histologically determined portal area and central vein and computationally defined proximity to these two histological architectures (FIG. 2E and FIGS. 9B and 9C, See Example 1). The spots with elevated high-risk PLS-NAFLD score were mainly observed in portal tracts, whereas elevated low-risk scores distributed across the four types of the area / spot (FIG. 2F).

[0227] The elevated high-risk score in portal tracts was associated with the presence of SC29 (IDO1+ eDCs), SC03 (exhausted CD8+ T cells), SC19 (memory B cells), SC20 (mesenchymal cells), and SC24 (plasma cells), whereas low-risk score in pericentral and mid-lobular areas was associated with SC30 endothelial cells (ECs) and SC10 (bone marrow-derived macrophages) based on a nonnegative matrix factorization-based cell type deconvolution (FIG. 2G). The five high-risk PLS-NAFLD-related cell types and colocalization were most frequently observed in portal tracts (FIGS. 9D-9E). Among the five cell types, cell-cell interactions between SC29 (IDO+ eDCs) and SC03 (exhausted CD8+ T cells) stood out (FIG. 2H and FIG. 9F). IDO1+ eDCs and PD-1+ CD8+ T cells were indeed in close proximity in portal tracts with elevated high-risk PLS-NAFLD scores (FIG. 2I).Example 5 - Independent validation of PLS-NAFLD in HCC-naYve patients with NAFLD

[0228] PLS-NAFLD was externally tested in an independent cohort of 106 HCC-naive patients with NAFLD (tissue validation set 1) (FIG. 3A and TABLES 1A-1B). During a median followup of 8.9 [interquartile range (IQR), 5.1 to 11.9] years, HCC developed in six patients (annual incidence rate, 0.6%). The signature classified 71 (67%) and 35 (33%) patients into high- and low-risk groups, respectively (FIG. 3B). There was no association between age and high-risk PLS-NAFLD (P = 0.14). Annual HCC incidence rates were 0.9 and 0%, and 15-year probabilities were 22.7% [95% confidence interval (Cl), 3.6 to 38.0%] and 0% (95% Cl, 0 to 0%) in the high- and low-risk groups, respectively (FIG. 3C). High-risk PLS-NAFLD was independently associated with incident HCC [adjusted HR (aHR) with Firth’s correction, 260.0; 95% Cl, 1.02 to infinity (Inf)] (TABLE 4). Three HCC cases developed in patients with minimalfibrosis (F-stage, 1), a patient population typically excluded from the recommended regular HCC screening, who were identified as high-risk by PLS-NAFLD.Example 6 - Refined HCC risk prediction by repeated PLS-NAFLD measurement

[0229] Clinical biomarkers such as a-fetoprotein (AFP) often fluctuate over time, and their change over time series measurements better inform prognosis compared to a single cross- sectional measurement. It was hypothesized that repeated measurement of PLS-NAFLD might improve HCC risk prediction, which was tested in tissue validation set 1 using follow-up liver biopsies. Given that there was no incident HCC in the low-risk group, 58 of 71 high-risk patients who had a second biopsy with a median interval of 2.3 (IQR, 1.8 to 3.0) years were profiled. The combined enrichment score (CES) (S. Nakagawa et al., Cancer Cell 30, 879- 890 (2016)) was used to quantify modulated PLS-NAFLD-based prognostic risk between the serial biopsies. There were 27 (47%) patients with decreased HCC risk, 22 (38%) with stable risk, and 9 (16%) with worse PLS-NAFLD-based risk of developing HCC (FIG. 3D). All HCCs developed in patients without improvement in PLS-NAFLD-based risk. Change in PLS- NAFLD risk was significantly associated with HCC development [aHR with Firth’s correction, 15.5; 95% Cl, 1.25 to 2273] and showed stably high discrimination ability (FIGS. 3E-3F). Annual incidence rates were 2.2 and 0% in PLS-NAFLD nonimproved and improved patients, respectively. The incidence rate among nonimproved patients is higher than the traditionally used cutoff of 1.5% to justify cost effectiveness of the HCC screening. CES was significantly correlated with change in fibrosis stage [rho = 0.41 ; false discovery rate (FDR) = 0.028] (FIG. 3D). The CES also showed better prognostic capability than change in fibrosis stage (FIG. 3F and TABLE 8). Collectively, repeated assessment of PLS-NAFLD would refine HCC risk estimation in NAFLD.TABLE 8: Comparisons of prognostic capability between change of PLS-NAFLD and fibrosis stage overtime in tissue validation set 1Example 7 - Independent validation of PLS-NAFLD in clinical NAFLD with history of prior HCC

[0230] PLS-NAFLD was externally validated in 59 HCC-experienced patients with NAFLD who underwent complete tumor resection with no radiological / pathological residual tumor (tissue validation set 2). PLS-NAFLD classified 34 (58%) and 25 (42%) patients into high and low-risk groups, respectively. The high-risk prediction was associated with HCC recurrence after curative resection (aHR, 2.38; 95% Cl, 1.07 to 5.25) (FIGS. 3G-3H, and TABLE 4). HCC incidence rates were 34.0 and 13.8% at 1 year and 71.8 and 42.9% at 5 years in high- and low-risk patients, respectively.Example 8 - Serum protein-based surrogate marker of PLS-NAFLD (PLSec-NAFLD)

[0231] Requirement of liver tissue would be a major bottleneck for widespread use of tissuebased PLS-NAFLD, particularly in HCC-naTve patients who do not undergo liver surgery. To address this limitation, tissue transcriptome-based PLS-NAFLD was converted into a bloodbased four-protein secretome panel (PLSec-NAFLD), (FIG. 10A), using a previously developed computational pipeline, TexSEC (texsec-app.org, N. Fujiwara, et al., Med (N Y) 2836-850. e10 (2021), incorporated herein by reference in its entirety). PLSec-NAFLD consists of two high-risk proteins (lymphotactin and progranulin, encoded by XCL1 and GRN, respectively) and two low-risk proteins (angiopoietin 2 and hepatocyte growth factor receptor, encoded by ANGPT2 and MET, respectively). Lymphotactin is a ligand of XCR1, suggesting that the panel monitors status of the XCR1+ eDCs in the liver. PLSec-NAFLD was implemented in a U.S. Food and Drug Administration-approved multiplex clinical diagnostic platform, xMAP assay (Luminex), and a cutoff of >1 was defined for prediction of high risk in two independent optimization sets (n = 73 and 72) covering broad stages of NAFLD (FIG. 10B).

[0232] PLSec-NAFLD was subsequently externally validated in an independent cohort of 59 HCC-naive patients with NAFLD cirrhosis (serum validation set). PLSec-NAFLD identified 42 (71%) high-risk and 17 (29%) low-risk patients (FIG. 4A). Similar to tissue-based PLS- NAFLD, PLSec-NAFLD-based prediction was independent of known HCC risk-associated clinical variables. Annual HCC incidence rates were 2.7 and 0%, and 15-year probabilities were 37.6 and 0% in high- and low-risk patients, respectively (FIG. 4B). High-risk predictions were significantly associated with HCC development [aHR with Firth’s correction (36), 32.4; 95% Cl, 1.13 to 1.96 x 1010], and PLSec-NAFLD was well-calibrated over time (FIG. 4C and TABLE 4). All HCC developed in high-risk patients, supporting that the biomarker mightdiscriminate low-risk patients who are spared from the regular HCC screening because of negligible HCC risk.

[0233] Last, to test whether the etiology-specific PLSec-NAFLD can serve as a plug-in module to improve prognostic prediction by the etiology-agnostic PLSec-AFP (as described in US20230073981A1 , incorporated herein by reference in its entirety), a combination of these HCC risk predictive signatures was tested as an integrative etiology-specific method of predicting NAFLD-related HCC risk (etPLSec-NAFLD) in the serum validation set. First, it was confirmed that high-risk PLSec-AFP (n = 10, 17%) was associated with HCC development (FIG. 10C). High-risk predictions with PLSec-AFP and PLSec- NAFLD were independent, suggesting their complementary prognostic utility (FIG. 10D). Integration of two signatures into etPLSec- NAFLD substantially improved HCC risk stratification. Annual HCC incidence rates were 13.3, 1.2, and 0% in patients with high-risk prediction by both (etPLSec-NAFLD high-risk group), either (intermediate-risk group), and neither (low-risk group) of the signatures, respectively (FIG. 4D). High-risk etPLSec-NAFLD scores were significantly associated with HCC development [aHR with Firth’s correction (36), 100.7; 95% Cl, 2.95 to Inf for high-risk compared to low-risk patients] (TABLE 4). In addition, this integration achieved consistently high prognostic performance compared to its individual components over time (FIG. 4E).Example 9 - Therapeutic modulation of PLS-NAFLD as surrogate biomarker of future HCC incidence

[0234] Various NAFLD therapeutics have been actively explored, targeting specific pathologies such as steatosis, inflammation, and fibrosis. However, it is difficult to estimate the impact of their short-term therapeutic modulation on long-term prognosis within the typical time frame of clinical trial. To address this challenge, the utility of PLS-NAFLD as a surrogate biomarker of HCC development was evaluated. As proof of concept, the magnitude of PLS- NAFLD modulation and its association with future HCC risk in patients with NAFLD was assessed in the following three distinct types of therapeutic interventions.

[0235] The first example is bariatric surgery, which is associated with reduced HCC incidence in patients with NAFLD. Hepatic transcriptome profiles of paired liver biopsies obtained before and after therapies in 79 patients with NAFLD were analyzed (FIG. 5A). On the basis of pretreatment hepatic transcriptomes, 68 (86%) and 11 (14%) patients were classified to highland low-risk groups, respectively. Among the high-risk patients, bariatric surgery showed more frequent significant reduction of PLS-NAFLD-based HCC risk compared to lifestyle-only intervention [odds ratio (OR), 0.21 ; 95% Cl, 0.06 to 0.73], which is comparable with the magnitude of reduction in HCC incidence by bariatric surgery in the previous epidemiologicalstudy (HR, 0.32), whereas no low-risk patients showed improved PLS-NAFLD status (FIG. 5B). The second example is use of lipophilic statins such as atorvastatin and simvastatin, which is associated with reduced HCC incidence in NAFLD patient cohorts (HR, 0.36). Prevalence of high-risk PLS-NAFLD in lipophilic statin users was threefold lower compared to non statin users (conditional OR, 0.33; 95% Cl, 0.16 to 0.68) in severely obese patients, which is again comparable to the magnitude of reduction in HCC incidence (FIGS. 5C-5D). The third example is use of an IDO1 inhibitor, which could potentially target the IDO1+ eDCs we identified in the single-cell and spatial transcriptome profiling. In this PLS- inducible cell culture model (cPLS system, described in Crouchet et al., Nat. Commun. 12, 5525 (2021) and incorporated herein by reference), it was confirmed that high-risk pattern of PLS-NAFLD induced by free fatty acid treatment was reversed by an IDO1 inhibitor under clinical development for malignancies, epacadostat, in a dose-dependent manner (FIGS. 5E- 5F).

[0236] These examples collectively support the utility of PLS-NAFLD to monitor therapeutic modulation of HCC risk to gauge anticipated HCC-preventive effect as a surrogate end point in clinical trials of anti-NAFLD therapies. In addition, the presence of high-risk PLS-NAFLD may justify enrollment to HCC chemoprevention clinical trials because of elevated HCC risk. PLS-NAFLD might also be used for cell-based high-throughput screening of candidate chemoprevention agents for NAFLD-related HCC.Example 10 - Discussion of Examples 2-9

[0237] The global shift of the etiology of liver cirrhosis and HCC from viral hepatitis to NAFLD highlights an urgent need for developing a risk stratification markers to allocate limited medical resources such as regular HCC screening to those who would benefit most. Etiology-agnostic biomarkers, tissue-based PLS, and serum-based PLSec-AFP have been previously developed to identify patients with chronic liver disease at high risk of developing HCC. Although these biomarkers showed promising prognostic capability, there was room to improve the identification of low-risk patients who do not require HCC screening. Here, it was found that PLS / PLSec-NAFLD identified low-risk patients with NAFLD who had negligible HCC risk consistently in derivation and validation sets. This is a substantial step toward refining HCC screening because over-screening of low-risk patients can cause unnecessary physical, economical, and psychological harms, and underscreening of high-risk patients likely results in late HCC diagnosis and misses the chance of curative treatment. Although serumbased PLSec-NAFLD might be preferred for HCC risk stratification given the invasiveness of liver biopsy, tissue-based PLS-NAFLD could still be useful to gauge HCC-preventive effect of anti-NAFLD agents in clinical trials, in which liver biopsy is typically performed, along with other tissue-based end points.

[0238] Furthermore, these examples proof of principle that combining an etiology-specific biomarker with an etiology-agnostic biomarker can improve prognostication as a plug-in module, which can be extended to hepatitis B virus-infected, hepatitis C virus-cured, and patients with alcoholic liver disease. PLS-NAFLD may also guide application of screening biomarkers such as circulating cell-free methylated DNA according to predicted HCC risk. Previously proposed hepatic gene expression- based signatures for NAFLD were all derived from cross-sectionally obtained samples; therefore, it was challenging to know whether these signatures reflect cause or consequence of disease progression as evidenced by the lack of their prognostic association. PLS-NAFLD is the first gene expression signature to predict future HCC risk, reflecting status of molecular drivers of hepatocarcinogenesis in HCC-naive NAFLD livers. It was found that IDO1+ eDCs associated with high-risk signatures, suggesting that they may be possible targets for HCC chemoprevention in NAFLD.

[0239] There is increased interest in HCC chemoprevention using generic agents such as aspirin and statins as well as other molecular-targeted agents. However, it is practically infeasible to follow patients in a clinical trial until significant reduction of HCC incidence is observed because of the low incidence rate (<1 % / year). Our study showed that PLS-NAFLD can be modulated in a short time period (in several days to weeks) by potential medical interventions reducing HCC risk, suggesting that PLS-NAFLD might serve as a surrogate end point to estimate their long-term prognostic benefit in clinical trials. This concept is already incorporated in our ongoing clinical trials using the etiology-agnostic PLS / PLSec as companion biomarkers (NCT02273362 and NCT04172779).

[0240] Although PLS / PLSec-NAFLD showed promising prognostic capability, there are several limitations. First, the number of events was small in the HCC-naive cohorts (the tissue validation set 1 and serum validation set) as typically observed in clinical studies, which may obscure accurate magnitude of risk difference between the high and low-risk groups. Nevertheless, the absence of HCC incidence in low-risk patients supports the capability of PLS / PLSec-NAFLD to identify patients with negligible HCC risk. Second, given the heterogeneous clinical demographics between the cohorts, PLS / PLSec-NAFLD should be prospectively validated in future studies to eliminate potential influence of confounding factors before its clinical translation. Third, association of therapeutic modulation of the biomarkers and future HCC incidence should be prospectively confirmed. Last although we used a selective gene signature development method based on the leave-one-out procedure, further refining of the gene list may lower the bar for its clinical application. In summary, we developed and validated tissue transcriptome-and serum secretome-based signatures, PLS / PLSec- NAFLD, for predicting long-term HCC risk and estimating effects of therapeutic interventionsin patients with NAFLD. These signatures may lead to improvement of the poor prognosis of NAFLD-related HCC.Materials And MethodsPatients and specimens

[0241] Diagnoses of NAFLD and HCC were made based on the clinical practice guidelines from the American Association for the Study of Liver Diseases (Marrero et al., Hepatology 68, 723-750 (2018) and Bair et al., PLOS Biol. 2, E108 (2004), each incorporated herein by reference in their entirety) for patients in the PLS / PLSec- NAFLD derivation and validation sets detailed below. Histological grading and staging of NAFLD were performed in a centralized manner by an experienced liver pathologist blinded to clinical information according to the established criteria. In these prospective-retrospective cohorts, biospecimens were collected and analyzed by utilizing the prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) design for biomarker validation. The study was approved by institutional review board at respective institutions with written informed consent or exemption for use of archived de-identified samples (protocol numbers: STU062018-058, STU072018-071 , 2010P000220 / PHS, and HS13-00159_.

[0242] The PLS-NAFLD derivation set includes 48 patients who have history of curative ultrasonography-guided percutaneous radiofrequency ablation (RFA) for early-stage NAFLD- related HCC (AJCC T 1 / 2 tumor without extrahepatic lesion) at the University of Tokyo between May 2006 and December 2018. Absence of residual tumor was radiologically confirmed with contrast-enhanced multiphase CT / MRI. Non-cancerous liver biopsy (16-gauge needle) tissues were collected at the time of treatment and immediately fixed in formalin. Post treatment follow- up was performed with multiphase CT / MRI every 3-4 months and tumor markers (AFP, DCP, and AFP-L3). Time to HCC development was defined as the interval between the dates of RFA and HCC diagnosis or the last follow-up including death as a censored observation. During a median follow-up of 1.8 (IQR, 0.8-2.7) years, 28 patients developed HCC recurrence. The recurrence hazard curve suggested that the observed recurrences were assumed to be dominantly de novo HCC recurrence (FIG. 6B).

[0243] The tissue validation set 1 consists of 106 non-cirrhotic HCC-naTve NAFLD patients who underwent diagnostic liver biopsy at Hiroshima University between May 2003 and February 2015 and regularly followed up using abdominal ultrasound every 6 months for a median of 8.9 (IQR, 5.1-11.9) years, during which 6 patients developed HCC. Time to HCC development was defined as the interval between biopsy and HCC diagnosis or the last followup. The tissue validation set 2 include 59 patients with early-stage NAFLD-related HCC who underwent curative surgical resection for HCC atToranomon Hospital or Kumamoto Universitybetween January 2003 and November 2011 . Post-treatment follow-up, diagnosis of HCC, and determination of time to HCC development were similarly performed as in the derivation set. During a median follow-up of 1.8 (IQR, 0.6-3.5) years, 32 patients developed HCC recurrence. The serum validation set includes 59 patients with NAFLD cirrhosis enrolled at University of Michigan between January 2004 and September 2006, and regularly followed up using ultrasound and AFP every 6 months for a median follow-up of 5.8 (1.8-10.8) years, during which 7 patients developed HCC. Time to HCC development was defined as the interval between the dates of blood sampling and HCC diagnosis or the last follow-up as a censored observation.Tissue transcriptome profiling

[0244] Total RNA samples (100 to 200 ng) isolated from three to five 10-pm-thick formalin- fixed paraffin-embedded (FFPE) tissue sections by the High Pure FFPET RNA isolation kit (Roche) were subjected to RNA-seq library preparation with the TruSeq RNA Exome kit (Illumina) and sequenced with NextSeq 500 (Illumina) (75 bp single-end) according to the manufacturer’s instructions. The raw sequencing reads were mapped to the reference genome (hg19) using the STAR aligner (Dobin et al., Bioinformatics 29, 15-21 (2013)), converted into transcript abundance data by the Subread package featurecounts (Liao et al., Bioinformatics 30, 923-930 (2014)), and normalized as the relative log expression (RLE) by the DESeq2 package (Love et al., Genome Biol. 15, 550 (2014)). Poor-quality profiles were identified by inter-sample correlation < 0.7 and excluded, and genes with low inter-sample variation (coefficient of variation [CV] <0.01) and expressed in <10% of samples were filtered out in subsequent general analyses such as exploration of molecular pathway dysregulation.Overview of Derivation and validation of PLS-NAFLD

[0245] More stringent gene filtering (CV <0.1 , max - min expression <100) was applied for derivation of PLS-NAFLD to ensure robust inter-sample differential expression and increased likelihood of successful independent validations. Subsequently, genes associated with the time to HCC development were selected using Cox score and leave-one-out cross-validation method, and prognostic prediction was performed with the nearest template prediction (NTP) algorithm as previously described. The PLS-NAFLD model defined in the derivation set was applied to the tissue validation sets 1 and 2 without modification in its parameters. Further details are provided below.Derivation of PLS-NAFLD in hepatic tissue transcriptome profiles

[0246] To define a transcriptome signature correlating with time to HCC development, we used the leave-one-out procedure as previously described (Hoshida et al., N. Engl. J. Med. 359, 1995-2004 (2008)). Briefly, in the derivation set including 48 patients, genes that wereless variably expressed across patients were excluded on the basis of the cutoffs of coefficient of variation (CV) < 0.1 and a difference between maximum and minimum expression < 100. This gene filtering did not rely on any prior knowledge related to the HCC status during followup. Subsequently, one patient was excluded, and genes associated with time to HCC development were selected in the remaining patients by using Cox score (56) calculated with the following Formula (3).where / is indices of samples; xi is the gene expression for sample / ; ti is the time for sample / ; k e1 , ... , and K are indices of unique death times z1, z2, ... , zK dk is the number of deaths at time zk mk is the number of samples in Rk = / : ti > zk, x*k = Zti = zkxi, and x > k = Z / e Rk x i / m k.

[0247] Genes with a random permutation test P value of less than 0.05 were selected for the HCC risk signature in the leave-one-out step. This procedure was repeated for each of the 48 patients, and 48 HCC risk signatures were derived. Genes consistently selected throughout the 48 HCC risk signatures were regarded as the PLS-NAFLD member genes (80 high-risk and 53 low-risk genes) to be subsequently evaluated for prognostic performance in the independent validation sets. Cox scores calculated in 48 patients for the final signature genes were used as a weight vector for prognostic prediction in independent cohorts as described further below.Assessment of molecular signature modulation

[0248] Transcriptome-signature-based prognostic prediction was performed by using our Nearest Template Prediction (NTP) algorithm implemented in the NearestTemplatePrediction module in the GenePattern genomic analysis toolkit (www.broadinstitute.org / genepattern) as previously reported (e.g., see Hoshida et al., N. Engl. J. Med. 359, 1995-2004 (2008)). Briefly, based on the 80 high-risk and 53 low-risk PLS-NAFLD genes, a template of high-risk patient and a template of low-risk patient were defined as vectors of 80 + 53 = 133 elements, in which a value of 1 was assigned to the high-risk genes and -1 was assigned to the low-risk genes for the template of high-risk patient and vice versa for the template of low-risk patient. Normalized transcriptome profiles of actual patients were standardized based on arithmetic mean and sample standard deviation of the analyzed cohort (or a pre-assayed reference cohort for a new patient in real-world clinical practice). Proximity of each patient to either of the NTP templates was assessed by using cosine distance with the Cox scores from the entirederivation set as a weight vector, and prediction of high or low risk was made based on closer template. Significance of the prediction was assessed as a nominal p-value (prediction confidence p-value) based on a null distribution for the distance generated by iterative random sampling of the signature genes (1 ,000 times). A prognostic prediction of high or low risk was made based on prediction confidence adjusted for multiple hypothesis testing with Benjamini- Hochberg FDR <0.10. The rest of the patients were predicted as having intermediate risk.Prognostic association of PLS-NAFLD-based HCC risk prediction

[0249] Association of the PLS-NAFLD-based prediction by the NTP algorithm, that is, either high- or low-risk prediction, was assessed by multivariable Cox regression adjusted for well- established clinical confounding variables, namely sex, older age defined as > 65 years old, and presence of advanced fibrosis defined as fibrosis stage >3, to calculate hazard ratio for the high-risk patients in the cohort compared to the low-risk patients as the reference group.Assessment of molecular signature modulation

[0250] Induction or suppression of molecular signatures of pathways and cell types in association with clinical prognosis and / or phenotypes was determined using comprehensive gene set collections of various molecular pathways in the Molecular Signature Database and gene sets from literature by gene set enrichment analysis (GSEA) using fgsea R package and visualized as gene set enrichment index (GSEI) (TABLE 9). Sample-level signature enrichment was determined using the eseach algorithm. Therapeutic modulation of PLS- NAFLD in paired samples was assessed by combined enrichment score (CES).TABLE 9: Signatures extracted from public datasets and database.(1) P. Ramachandran et al., Nature 575, 512- 518 (2019);(2) S. A. MacParland et al., Nat. Commun. 9,4383 (2018).(3) Molecular Signatures Database (v7.4)Determination of NAFLD-related single nucleotide polymorphisms (SNPs)

[0251] Known NAFLD-related germline SNPs in the coding regions were detected by using the GATK (ver 3.8) Best Practices workflow in the RNA-seq data. Briefly, raw sequencing reads were mapped to the reference genome (hg19) and curated using the STAR aligner (Dobin et al., supra). Picard (broadinstitute. github.io / picard), and GATK SplitNCigarReadsand BaseRecalibrator based on the known SNVs in the dbSNP database. SNVs were called using HaplotypeCaller and poor quality calls were excluded based on the following criteria: clusters of less than 3 SNPs within a window of 35 bases, Fisher strand values (FS > 30), qual by depth values (QD < 2), base call quality (QUAL < 40), and read depth on variant position (DP < 10).M ata-analysis of single-cell RNA-seq datasets

[0252] To construct a single-cell transcriptome atlas of human hepatic cells encompassing healthy to NAFLD-affected livers, four independent hepatic scRNA-seq datasets from the NCBI Gene Expression Omnibus (GEO) database (TABLE 6, above) were integrated using R package Seurat v4.0. Single cells with 500 to 2,500 detected genes and mitochondrial genes in total unique molecular identified (UMI) counts <30% were included in the metaanalysis. Log-normalized expression data were integrated using FindlntegrationAnchors and IntegrateData functions, and subjected to principal component analysis (PCA) for clustering the cells at the resolution cut-off of 0.8 and visualization using uniform manifold approximation and projection (UMAP) based on the top 50 principal components. The single cell clusters were annotated with gene signatures of cell types identified in human cirrhotic livers, including parenchymal, stromal, and liver-resident and bone-marrow-derived immune cells (FIG. 8A). A subset of cells in the endothelial cell cluster with distinctly high expression of mast cell signature were classified into a mast cell cluster (SC31). Marker genes of the cell clusters were defined as differentially expressed gene by Wilcoxon rank-sum test (TABLE 7, above), which were additionally used to enrich annotation of the cell clusters for inference of their functional status based on known marker genes of human hepatic cell populations and relevant literature.Spatial transcriptome profiling of human NAFLD liver

[0253] A NAFLD-affected clinical liver tissue was obtained at the time of HOC resection and fixed with 10% formalin and embedded in paraffin. Five-pm-thick tissue section was used for sequencing library preparation using the Visium FFPE Spatial Gene Expression kit (10x Genomics) and sequenced with NextSeq 500 system (Illumina) to generate genome-wide transcriptome profile for each of the grid-like regions (called “spot”, 55 pm in diameter) on the tissue section. Raw data were preprocessed using the Space Ranger pipeline (10x Genomics) based on reference genome (hg38) and subjected to further analyses with Seurat v4.0. The spots with > 200 UMI counts were retained, and genes with a total UMI count < 100 across all spots, expressed in < 5 spots, and hemoglobin-related genes were excluded, and normalized gene expression levels were determined (FIG. 9A). Induction of PLS-NAFLD in each spot was determined as the high- and low-risk PLS- NAFLD scores by using averagerelative expression of the signature genes. The spots were classified into four histological architectures (i.e., portal tract, peri-portal, peri-central, and mid- lobular) based on histological determination of portal tract and central vein by an experienced liver pathologist and relative proximity to the two structures by Euclidean distance (< 200 pm or within top quintile of distances between the two structures) (FIG. 9B). Relative abundance of the meta- analysisbased single-cell clusters in each of the Visium spots was inferred using SPOTIight algorithm based on a seeded non-negative matrix factorization regression (FIG. 2G). The single-cell clusters consisting of <4% of a spot were disregarded as background signal. Inter- cellular ligand-receptor interactions within each spot were inferred by using CellPhoneDB.Derivation and validation of PLSec-NAFLD

[0254] For derivation of PLSec-NAFLD (that is, serum-based surrogate marker of tissuebased PLS-NAFLD), the following 3 steps were taken, described in further depth below: (1) identification of proteins encoded by genes in the PLS-NAFLD and associated molecular pathways (PLS-NAFLD-associated gene / protein panel); (2) computational prediction of secreted proteins among the PLS-NAFLD- associated gene / protein panel for further optimization (pilot PLSec-NAFLD panel); and (3) optimization of the pilot panel by shaving genes / proteins carrying redundant prognostic information for assay implementation and validation (final PLSec-NAFLD panel) (Fig 10A).

[0255] Identification of proteins encoded by genes in PLS-NAFLD and associated molecular pathways (PLS-NAFLD-associated gene / protein panel). To identify PLS-NAFLD-associated molecular pathways, we systematically survey 2,321 gene sets of well-defined molecular pathways from Molecular Signature Database (MSigDB v7.4) using GSEA in three independent NAFLD patient cohorts (tissue validation sets 1 and 2 and GSE130970, 243 patients in total) based on gene ranking by Spearman’s correlation with induction of high- and low-risk PLS-NAFLD measured by eseach algorithm. Nominal p-values of the enrichment in the 3 cohorts were synthesized for each pathway gene set using Fisher’s inverse chi-square statistic, and its significance was assessed as nominal p-value based on null distribution generated by random iterative sampling of the GSEA nominal p-values (10,000 iterations) adjusted for multiple testing by Benjamini-Hochberg FDR. We identified 97 pathway gene sets as the PLS-NAFLD-associated pathways at significance cut-off of FDR < 0.25. For each of the associated pathways, proteins encoded by the core enrichment genes were included in the PLS-NAFLD-associated gene / protein panel together with the PLS-NAFLD member genes (1,263 proteins in total, 934 high-risk and 329 low-risk proteins).

[0256] Computational prediction of secreted proteins among the PLS-NAFLD-associated protein panel (pilot PLSec-NAFLD panel). Secreted proteins into circulation were predictedby utilizing our computational pipeline, Translation of tissue gene expression to secretome (TexSEC) (www.texsec-app.org), integrating amino-acid-sequence-based prediction of extracellular secretion as well as organ / tissue-specific tissue proteome and clinical secretome databases. Among the proteins in the PLS-NAFLD-associated gene / protein panel, we identified 31 (29 high and 2 low-risk) genes / proteins as the pilot PLSec-NAFLD panel for further optimization.

[0257] Optimization of the pilot panel by shaving genes / protein carrying redundant prognostic information for subsequent clinical utility validation (final PLSec-NAFLD panel). To further reduce the number of genes / proteins for clinical assay implementation, we explored genes / proteins carrying redundant prognostic information. In genome-wide transcriptome datasets of two independent NAFLD patient cohorts (GSE48452, GSE49541), HCC risk prediction was performed by the NTP algorithm using the PLS-NAFLD, and HCC risk level in each patient was inferred as -Iog10(nominal p-value of NTP prediction) with sign of +1 or -1 for high- or low-risk prediction, respectively. In each of two datasets, correlation of 31 genes’ expression levels with the NTP-based HCC risk levels across the patients was assessed by Pearson’s correlation, and genes with similar correlation were shaved by using the least absolute shrinkage and selection operator (LASSO) algorithm. Last, lymphotactin and progranulin as high-risk proteins and angiopoietin 2 and soluble HGF receptor as low-risk proteins were commonly selected in both of the two datasets as the final PLSec-NAFLD panel for implementation in the xMAP platform (Luminex) as we previously reported (12, 40, 78, 79) for subsequent clinical utility validation in independent NAFLD patient cohorts.

[0258] The PLSec-NAFLD score was defined based on semi-quantitative abundance of each protein as described in Formula I:

[0259] where high abundance indicates expression level greater than median for each protein. In the independent clinical NAFLD datasets, a cut-off of > 1 was defined to call a high- risk prediction with maximized consistency of prognostic prediction (FIG. 10B). The 4-protein PLSec-NAFLD panel was implemented in an FDA-approved multiplex clinical diagnostic platform, xMAP assay (Luminex) to be run on the Bio-Plex 200 systems (Bio-Rad) at UT Southwestern BioCenter according to the manufacturer’s protocol. The abundance of each protein was measured as median fluorescent intensity corrected for background signals fromnegative control probes and converted to concentration according to built-in dilution series of positive control proteins in each 96-well assay plate for plate-to-plate batch correction.Therapeutic modulation of PLS-NAFLD in clinical NAFLD cohorts

[0260] We quantitatively assessed magnitude of therapeutic modulation of PLS-NAFLD by therapeutic interventions clinically associated with reduced HCC incidence, namely bariatric surgery and lipophilic statins, using publicly available transcriptome datasets (GEO accession numbers, GSE83452, GSE106737, and GSE130991). Modulation of PLS-NAFLD was evaluated by using CES. Non-statin users were selected as controls to the statin users with propensity score matching for age and sex by Matchit R package (1 :2 matching).In vitro modulation of PLS-NAFLD by an IDO1 inhibitor, epacadostat

[0261] Therapeutic modulation of PLS-NAFLD was assessed in a clinical-prognostic- signature-inducible cell culture model (cPLS system) developed for screening of HCC chemopreventative agents. Briefly, PLS-NAFLD was induced by free fatty acid treatment (800 pM oleic acid and 400 pM palmitic acid) of co-culture of Huh7.5.1diff and LX2 cells, and its modulation was tested for treatment with epacadostat (at 10, 25, and 50 pM) (n=2-3 per group) (see FIG. 5E). PLS-NAFLD profile was performed by NanoString nCounter assay according to the manufacturer’s instruction. Modulation of PLS-NAFLD was quantified by CES and GSEIImmunohistochemistry staining

[0262] Immunohistochemical double staining was performed on serial FFPE tissue sections (5-pm-thick) using validated antibodies (TABLE 10). Briefly, deparaffinized tissue sections were blocked with 3% hydrogen peroxide, subjected to antigen retrieval with citrate buffer (pH 6.0), and incubated with the primary antibodies overnight. The secondary antibody conjugated with horseradish peroxidase was applied and imaged with BZ-X810 microscope (Keyence) and analyzed with Fiji software (imagej.net / software / fiji).TABLE 10: Antibodies for Immunohistochemical StainingStatistical data analysis

[0263] Categorical and continuous variables were tested by Fisher’s exact test and Wilcoxon rank-sum test, respectively. Time-to-event, prognostic analyses were performed using Kaplan-Meier method and uni / multivariable Cox regression modeling. Proportional-hazards assumption was confirmed by using cox.zph function in survival R package (TABLE 11). The matched case-control series was analyzed by conditional logistic regression model (FIG. 5D). Estimated hazard ratios were adjusted by the Firth’s correction using coxphf R package when no event occurred in a patient group and therefore hazard ratios could not be reliably calculated. To evaluate robustness of the signature’s prognostic association, multiple multivariable models were built with clinically known confounding variables (TABLE 4, above). Correction for multiple hypothesis testing was applied using false discovery rate as needed. A two-tailed p-value <0.05 was regarded as statistically significant. All data analyses were performed using R statistical language otherwise specified.TABLE 11 : Test for proportional hazard assumption of the molecular biomarkersExample 11 - Molecular subtypes of metabolic dysfunction-associated steatotic liver disease to inform disease progression

[0264] Metabolic dysfunction-associated steatotic liver disease (MASLD) has marked molecular and clinical heterogeneity, yet tools to inform disease progression and therapeutic decision making are lacking. By an integrative hepatic transcriptome meta-analysis of multi- regional cohorts, including 896 (derivation, n = 340; validation, n = 556) mild- to advanced- stage MASLD patients, three reproducible subtypes were identified and validated in an unsupervised manner. The three subtypes were annotated as advanced MASLD (a- MASLD) characterized by existing advanced fibrosis, progressive MASLD (p-MASLD), and indolent MASLD (i-MASLD). Despite the indistinguishable clinico-histological features, p- and i-MASLD showed distinct prognostic differences. The likelihood of 2-year histologicalfibrosis progression for p-MASLD was higher compared to i-MASLD (adjusted odds ratio, 1.42; 95% confidence interval [Cl], 1.09-1.83). While i-MASLD was HCC-free up to 15 years, incident HCC rates in p- and a-MASLD were comparable. Single-cell and spatial transcriptome analyses revealed that subpopulations of hepatocyte lead to distinct prognoses of MASLD. Bioinformatic inference suggested that some of the existing and candidate MASLD therapies may yield subtype-specific benefit. Consistent with the transcriptome-based drug response assessment, cenicriviroc, an oral CCR2 / 5 antagonist, was more effective in reducing histological fibrosis in p- and a-MASLD in the phase 2b CENTAUR trial. LINCS screening to predict efficacy among existing drugs based on transcriptomic responses identified a small molecule inhibitor of discoidin domain receptor tyrosine kinase 1 (DDR1) as a candidate a-MASLD-directed therapy. Indeed, pharmacological DDR1 inhibition reduced HCC development in the choline-deficient high-fat diet rat model of a-MASLD. Thus, molecular MASLD subtypes can serve as a powerful tool to inform disease progression and therapeutic decision making.

[0265] Non-alcohol fatty liver disease (MASLD) has become a rapidly emerging global health problem accompanying the epidemic of central obesity and metabolic syndrome. MASLD is estimated to affect one billion individuals globally and there are up to 80 million individuals with MASLD in the U.S, where the prevalent MASLD has increased from 20% to 32% over the past 30 years. While most patients with MASLD stay asymptomatic without clinically relevant outcomes for decades, 20% of MASLD can progress to more advanced form of MASLD, metabolic dysfunction-associated steatohepatitis (MASH), characterized by hepatic immune cell infiltration and hepatocyte damage, a subset of which further can reach lethal complications such as cirrhosis and hepatocellular carcinoma (HCC). Indeed, MASLD is already considered among the top etiologies for HCC and indications for liver transplantation in the U.S. The similar trends are observed everywhere in the world including Europe, China, and India. Thus, MASLD imposes a significant socioeconomical burden globally.

[0266] MASLD is a blanket term covering individuals with very different rates of disease progression and different clinical presentations. This high clinical heterogeneity reflects the diverse impacts of genetic factors, environment, microbiome as well as metabolisms. Experimental studies revealed that there are many molecular pathways contributing to the development of MASLD. However, contributions of these pathologic drivers are not likely to be identical among all patients. This clinical and molecular heterogeneity of MASLD can hamper appropriate selection of patients who benefit most from regular screening examinations.

[0267] A solution was found to the identification of molecular subtypes of MASLD to inform rapid disease progression by integrating hepatic transcriptomic data sets from multiple global regions covering mild to advanced MASLD.ResultsIdentification of molecular MASLD subtypes associated with disease severity and trajectory

[0268] To identify molecular subtypes of MASLD robustly reflecting functional status of the liver, integrative hepatic transcriptome meta-analysis was performed using five multi- regional / racial / ethnic derivation cohorts, including 340 mild- to advanced-stage MASLD patients with or without history of HCC (FIG. 11 A, FIG. 12A). A list of the signature genes is provided in TABLE 12. Three reproducible subtypes were identified across the cohorts, subtypes 1 , 2, and 3 that include 144 (42%), 94 (28%), and 102 (30%) patients, respectively (FIG. 11B, FIGS. 12A-C).

[0269] TABLE 12: Signature genes

[0270] Subtype 1 (indicated by red bar in FIG. 11B) was associated with more frequent obesity (80% vs. 66%), female dominance (61% vs. 44%), advanced histological fibrosis, and higher grade of hepatocyte ballooning and inflammation compared to other subtypes (TABLE 13). There was no notable difference in the clinico-histological features between the subtypes 2 and 3 (indicated by yellow and blue bars in FIG. 11 B, respectively). Consistent with the findings, the subtype 1 was associated with our previously reported transcriptomic signatures to predict fibrosis progression (T. Qian et al., Gastroenterology 162,1210-1225 (2022)), etiology-agnostic HCC risk (S. Nakagawa et al., Cancer Cell 30, 879-890 (2016)), along with a previously reported molecular signature representing fibrotic MASLD (O. Govaere et al., Sci Transl Med 12, (2020)). High risk prediction of the MASLD- specific HCC risk signature, PLS-MASLD, was significantly more distributed in subtype 1 and 2 compared to 3 (70% and 48% in subtype 1+2 and 3, respectively; p <0.001) (N. Fujiwara et al., Sci Transl Med 14, eabo4474 (2022)).

[0271] TABLE 13: Patient demographics in the derivation set

[0272] The prognostic association of the subtypes was evaluated. Subtype 1 was associated with lower likelihood of fibrosis regression (FIG. 11C). Of note, despite the indistinguishable clinico-histological features, the subtypes 2 and 3 showed distinct prognostic differences. The likelihood of histological fibrosis progression for the subtype 2 was higher than that of the subtype 3 (adjusted odds ratio [aOR], 1.42; 95% Cl, 1.09-1.83) (FIG. 11C). In the HCC-naTve subset (n=106), the subtype 3 was HCC-free up to 15 yearsof clinical follow-up, whereas HCC incidence rate in the subtype 2 was comparable to that of the subtype 1 (18.9%, 41.7%, and 0% at 15 years in subtype 1, 2, and 3, respectively) (FIG. 11D).

[0273] These observations suggest that the subtype 1 represents clinically more advanced MASLD, whereas the subtypes 2 and 3 represent clinically milder disease. These results indicated that the subtype 3 is indolent, while the subtype 2 is progressive over the long-term clinical follow-up, and this prognostic distinction cannot be captured by existing clinico- histological features and previously reported prognostic signatures. Based on these findings, subtypes 1, 2, and 3 are advanced-MASLD (a-MASLD), progressive-MASLD (p- MASLD), and indolent-MASLD (i-MASLD) subtypes, respectively, and defined their transcriptomic signatures (TABLE 12).

[0274] TABLE 14: Datasets for the validation set

[0275] The clinico-histological associations were externally validated in an integrative hepatic transcriptome meta-analysis of 5 independent cohorts of 556 MASLD patients from Europe, the U.S., and Japan using publicly available RNA-seq data (FIG. 13A, TABLE 14). The subtype signatures reproduced the 3 subtypes and the clinico-histological and molecular associations observed in the derivation cohorts, supporting robustness of our MASLD subtypes (TABLE 15).

[0276] TABLE 15: Patient demographics in the validation setImplementation of the MASLD subtyping in a clinical assay platform

[0277] Subsequently the signatures were implemented in an FDA-approved clinical diagnostic platform (Nanostring) for clinical deployment by reducing the number of signature genes without sacrificing the subtyping consistency (FIGS. 13B-C, TABLE 14). A subset of the derivation set was reanalyzed using the nCounter Sprint assay, which found that a 30- gene signature reproduced the original MASLD subtyping in the validation set (consistency, 79%; p <0.001) (see TABLE 16 and FIG. 11 F), further supporting the broad clinical utility of the MASLD subtype across the whole spectrum of severity of MASLD.

[0278] TABLE 16: Abbreviated signaturesMASLD sec retome signatures for subtyping

[0279] To non-invasively perform MASLD subtyping, a blood-based MASLD secretome signature strategy was developed (FIG. 14). Given that a-MASLD has distinct clinical presentation characterized by more advanced fibrosis, a two-step strategy was adapted: (i) a-MASLD identification (ii) p-MASLD identification among the rest (FIG. 14). For this strategy, through a computational algorithm, TexSEC, enabling a conversion of hepatic transcriptome signature to a secretome signature comprising eight proteins, including cathepsin S, CCL-20, collage IV alpha 1, autotaxin, a-FABP, osteopontin, pro-collagen I alpha I, and IGFBP-7, were identified for a-MASLD determination. Furthermore, among those classified into non a-MASLD, three p-MASLD proteins, including furin, inter-alpha- trypsin inhibitor heavy chain H4 (ITIH4), and angiopoietin-related protein 6, and four i- MASLD proteins, including Parkinson disease protein 7 (PARK7), soluble HGF receptor, N- cadherin, and angiopoietin-like protein 3, distinguished p- and i-MASLD patients, respectively. In the derivation cohort, consistency of serum-based classification with RNA- based subtyping was 87% and 76% in the two steps, respectively (FIGS. 15A-15B).

[0280] In a validation set of 44 patients with paired NanoString-based MASLD subtyping, overall consistency of serum-based subtyping with it was 74% (first-step, 95%; second-step, 71%) (16A-16B). Next, the prognostic associations of serum-based MASLD subtyping was evaluated. Fibrosis-4 (FIB-4) index is a widely used non-invasive score based on clinical biochemical tests and patient age to estimate liver fibrosis severity. Repeated FIB-4 measurements were available over 6.1 (IQR, 2.9-10.6) years of observation (median number of assessments per patient was 21 [IQR, 10-35]). Interestingly, the subtype 2 showed more deteriorating changes compared to the subtypes 1 and 3 (FIG. 17A). Furthermore, HCC development was observed in 3 patients, all of whom were classified to a-MASLD (FIG. 17B). Thus, serum-based subtyping achieved similar prognostic associations with RNA-based subtyping.MethodsPatient cohorts

[0281] In the derivation set, diagnoses of MASLD and HCC were made based on the clinical practice guidelines from the American Association for the Study of Liver Diseases (N. Chalasani et al., Hepatology 67, 328-357 (2018), JA. Marrero et al., Hepatology 68, 723-750 (2018)). The study was approved by institutional review board at respective institutions with written informed consent or exemption for use of archived de-identified samples (protocol numbers: STU062018-058, STU072018-071, 2010P000220 / PHS, and HS13-00159). In the validation set, all clinical data were retrieved through Gene Expression Omnibus at the NCBI.

[0282] The patient selection and tissue collection were detailed in the respective original papers. Briefly, the derivation set consisted of 5 independent cohorts as follows: Cohort 1, 106 non-cirrhotic HCC-naTve MASLD patients who underwent diagnostic liver biopsy at Hiroshima University, Japan, between May 2003 and February 2015 and regularly followed up using abdominal ultrasound every 6 months (N. Fujiwara et al., Sci Transl Med 14, eabo4474 (2022)). Cohort 2, 78 HCC-naTve patients who underwent diagnostic liver biopsy at Virginia Commonwealth University, the US, between 2012 and 2016 (SA Hoang et al., Sci Rep 9, 12541 (2019)). Cohort 3, 49 HCC-naTve patients who underwent bariatric surgery (Roux-en-Y gastric bypass surgery) at Geneva University Hospital, Switzerland, between June 1997 and December 2004. The livers tissues were obtained by left-lobe (segment 3) wedge-resection or needle liver biopsy during the surgery (N. Goossens et al., Clin Gastroenterol Hepatol 14, 1619-1628, (2016)). Cohort 4, 48 patients who have history of curative ultrasonography-guided percutaneous radiofrequency ablation (RFA) for early-stage MASLD-related HCC (AJCC T1 / 2 tumor without extrahepatic lesion) at the University of Tokyo, Japan, between May 2006 and December 2018. Non-cancerous liver biopsy (16- gauge needle) tissues that were used for this study were collected at the time of treatment and immediately fixed in formalin (N. Fujiwara et al., Sci Transl Med 14, eabo4474 (2022)). Cohort 5, 59 patients with early-stage MASLD-related HCC who underwent curative surgical resection for HCC at Toranomon Hospital or Kumamoto University, Japan, between January 2003 and November 2011. The adjacent non-cancerous liver tissues were used for this study (N. Fujiwara et al., Sci Transl Med 14, eabo4474 (2022)).Tissue transcriptome profiling

[0283] For cohorts 1, 3 to 5 in the derivation set, total RNA samples (100 to 200 ng) isolated from three to five 10-mm-thick formalin-fixed paraffin-embedded (FFPE) tissue sections by the High Pure FFPET RNA isolation kit (Roche) were subjected to RNA-seq librarypreparation with the TruSeq RNA Exome kit (Illumina) and sequenced with NextSeq 500 (Illumina) according to the manufacturer’s instructions. For cohort 2, RNA that was extracted from cells using a Qiagen RNeasy RNA Isolation Kit (Qiagen) were subjected to RNA-seq library preparation with the TruSeq Stranded mRNA Sample Preparation kit (Illumina) and sequenced with HiSeq2500 (Illumina) according to the manufacturer’s instructions. In the validation set, all fastq files were retrieved from Sequence Read Archive available from the NCBI.Mata-analysis of RNA-seq datasets

[0284] All raw sequencing reads were remapped to the reference genome (hg19) in a consistent manner using the STAR aligner (A. Dobin et al., Bioinformatics 29, 15-21 (2013)) and converted into transcript abundance data by the Subread package featurecounts (Y. Liao, GK Smyth, W. Shi, Bioinformatics 30, 923-930 (2014)). To correct the batch effects, the count data were corrected using the Combat-seq algorithm separately in the derivation and validation sets (Y. Zhang, G. Parmigiani, WE Johnson NAR Genom Bioinform 2, lqaa078 (2020)) and normalized as the relative log expression (RLE) by the DESeq2 package (Ml. Love, W. Huber, S. Anders, Genome biology 15, 1-21 (2014)). Appropriate batch correction was confirmed by using principal component analysis (FIG. 12B). For the drug susceptibility assessment, the count data from the derivation and validation sets were merged, recorrected, and renormalized. Poor-quality profiles were identified by inter-sample correlation < 0.7 and excluded, and genes with low inter-sample variation (coefficient of variation [CV] <0.01) and expressed in <50% of samples were filtered out in subsequent general analyses such as exploration of molecular pathway dysregulation.Identification of MA SLD subtype

[0285] After the batch correction and normalization, genes that were more variably and abundantly expressed across 340 patients in the derivation set were further included according to top 5,000 median absolute deviation and minimal expression >100, respectively. This gene filtering did not rely on any prior knowledge related to clinico- histological features. Consensus clustering algorithm was performed by the ConsensusClusterPlus R package (S. Monti et al., Machine Learning 52, 91-118 (2003)). 1- Pearson was used as the distance measure and the following detail settings were used for clustering: number of repetitions = 1000 bootstraps; item subsampling proportion = 0.7; feature subsampling proportion = 1). The optimal number of clusters (3 clusters) was determined by the plot of A area under the cumulative distribution function curve and the consensus matrix (FIGS. 12C-E). The signature genes for each MASLD subtype were identified according to differentially expressed gene (DEG) analyses by Wilcoxon’s rank-sumtest between one subtype vs. the rest. More specifically, the signature genes showed significantly high expression compared to either of other subtypes (false discovery rate [FDR] <0.001 and fold change >1.2). Besides, top 200 DEG were chosen according to sum of -log 10 FDR of the two comparisons (TABLE 15).Determination of MA SLD subtype

[0286] Transcriptomic-signature-based MASLD subtype determination was performed by using the Nearest Template Prediction (NTP) algorithm implemented in the NearestTemplatePrediction module in the GenePattern genomic analysis toolkit (M. Reich et al., Nat Genet 38, 500-1 (2006)) available at the Broad Institute as previously reported by us and others (S. Nakagawa et al., Cancer Cell 30, 879-890 (2016); N. Goossens et al., Clin Gastroenterol Hepatol 14, 1619-1628, (2016); Y. Hoshida et al., N Engl J Med 359, 1995- 2004 (2008); Y. Hoshida PLoS One 5, e15543 (2010); E. Trepo et al., Gastroenterology 154, 965-975 (2018); Y. Hoshida et al., Gastroenterology 144, 1024-30 (2013); LY. King et al., Gut 64, 1296-302 (2015); A. Ono et al., Hepatology 66, 1344-1346 (2017); A. Sadanandam et al., Nat Med 19, 619-25 (2013); KM. Joo et al., Cell Rep 3, 260-73 (2013); M. Bansal et al., Nat Biotechnol 32, 1213-22 (2014); J. Candia et al., Nat Commun 11, 4383 (2020); T. Qian et al., Gastroenterology (2021); F. Juhling et al., Gut 70, 57-169 (2021); P. Deltenre et al., Liver Int 40, 565-570 (2020)).Determination of MASLD-related single nucleotide polymorphism (SNPs)

[0287] Known MASLD-related germline SNPs in the coding regions were detected by using the GATK (ver 3.8) (GA Van der Auwera, BD O'Connor, O'Reilly Media (2020)). Best Practices workflow in the RNA-seq data. Briefly, raw sequencing reads were mapped to the reference genome (hg19) and curated using the STAR aligner ( A. Dobin et al., Bioinformatics 29, 15-21 (2013)), Picard (available at the Broad Institute), and GATK SplitNCigarReads and BaseRecalibrator based on the known SNVs in the dbSNP database (ref). SNVs were called using HaplotypeCaller and poor-quality calls were excluded based on the following criteria: clusters of less than 3 SNPs within a window of 35 bases, Fisher strand values (FS > 30), qual by depth values (QD < 2), base call quality (QUAL < 40), and read depth on variant position (DP < 10).Statistical analysis

[0288] Categorical and continuous variables were tested by Fisher’s exact test and Wilcoxon rank-sum test, respectively. Case-control analyses were conducted using the multivariable logistic regression method, in which the odds ratios of the MASLD subtypes were adjusted for older age (defines as >60 years old), sex, and history of diabetes. Time- to-event analyses were performed using the Kaplan-Meier method. Correction for multiplehypothesis testing was applied using FDR as needed. A two-tailed p-value <0.05 was regarded as statistically significant. All data analyses were performed using R statistical language otherwise specified.STATEMENT OF INVENTIONSEmbodiment 1. A method of identifying a MASLD subtype in a subject, comprising determining a MASLD-associated prognostic liver secretome signature (PLSec- MASLD) score for the subject and identifying the MASLD subtype as(a) an indolent-MASLD (i-MASLD) subtype;(b) a progressive-MASLD (p-MASLD) subtype; or(c) an advanced-MASLD (a-MASLD) subtype, wherein the identification is based on the PLSec-MASLD score.Embodiment 2. The method of Embodiment 1 , further comprising a method of obtaining the PLSec-MASLD score for the subject, wherein the method of obtaining the PLSec- MASLD score comprises:(a) having obtained or obtaining a blood sample from the subject;(b) subjecting the blood sample to a multi-analyte profiling assay for protein quantification of one or more signature proteins to obtain one or more quantification measurements;(c) normalizing the one or more quantification measurements; and(d) converting the quantification measurements into an aggregated score wherein the aggregated score is the PLSec-MASLD score.Embodiment 3. The method of Embodiment 2, wherein one or more signature proteins is selected from(a) an i-MASLD subtype signature protein panel;(b) a p-MASLD subtype signature protein panel; and / or(c) an a-MASLD subtype signature protein panel.Embodiment 4. The method of Embodiment 2 or Embodiment 3, wherein the one or more signature proteins is selected from(i) Parkinson disease protein 7 (PARK7), soluble HGF receptor, N- cadherin, angiopoietin-like protein 3, or a combination thereof;(ii) furin, inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), angiopoietin- related protein 6, or a combination thereof; and / or(iii) cathepsin S, CCL-20, collage IV alpha 1 , autotaxin, a-FABP, osteopontin, pro-collagen I alpha I, IGFBP-7, or a combination thereof,.Embodiment 5. The method of any one of Embodiment 1 to Embodiment 4, wherein the MASLD subtype is identified as(a) an i-MASLD subtype when the PLSec-MASLD score comprises or consists of one or more signature proteins selected from Parkinson disease protein 7 (PARK7), soluble HGF receptor, N-cadherin, or angiopoietin-like protein 3, and wherein the PLSec-MASLD is greater than 1 .(b) a p-MASLD subtype when the PLSec-MASLD score comprises or consists of one or more signature proteins selected from furin, inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), or angiopoietin-related protein 6, and wherein the PLSec-MASLD is greater than 1 ; or(c) an a-MASLD subtype when the PLSec-MASLD score comprises or consists of one or more signature proteins selected from cathepsin S, CCL-20, collage IV alpha 1 , autotaxin, a-FABP, osteopontin, pro-collagen I alpha I, or IGFBP-7, and wherein the PLSec-MASLD is greater than 1.Embodiment 6. The method of any one of Embodiment 1 to Embodiment 5, wherein the method of identifying a MASLD subtype in the subject comprises performing a liver biopsy, one or more blood tests to assess liver function, measuring levels of alphafetoprotein in blood, computed tomography, magnetic resonance imaging, or a combination thereof.Embodiment 7. The method of Embodiment 6, wherein the one or more blood tests performed to assess liver function comprises a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof.Embodiment 8. The method of any one of Embodiment 1 to Embodiment 7, further comprising administering one or more prophylactic therapies and / or treatments of MASLD or hepatocellular carcinoma (HCC) to the subject.Embodiment 9. The method of Embodiment 8, wherein the one or more prophylactic therapies and / or treatments of MASLD or HCC comprise surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof.Embodiment 10. The method of Embodiment 9, wherein the drug therapy comprises administration of one or more drugs to the subject, wherein the drugs are selected from(a) an oral CCR2 / 5 antagonist optionally selected from cenicriviroc, or a discoidin domain receptor tyrosine kinase 1 (DDR1) inhibitor, or(b) sorafenib, regorafenib, nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof.Embodiment 11. A method of identifying a MASLD subtype in a subject, comprising determining a MASLD-associated prognostic liver (PLS-MASLD) score for the subject and identifying the MASLD subtype as(a) an indolent-MASLD (i-MASLD) subtype;(b) a progressive-MASLD (p-MASLD) subtype; or(c) an advanced-MASLD (a-MASLD) subtype, wherein the identification is based on the PLS-MASLD score.Embodiment 12. The method of Embodiment 11, further comprising a method of obtaining the PLS-MASLD score for the subject, wherein the method of obtaining the PLS-MASLD score comprises:(a) having obtained or obtaining a tissue sample from the subject;(b) subjecting the tissue sample to a multi-analyte profiling assay for quantification of one or more signature genes or nucleotide sequences encoding a signature protein to obtain one or more quantification measurements;(c) normalizing the one or more quantification measurements;(d) converting the quantification measurements into a PLS- MASLD score, wherein the PLS- MASLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a reference gene expression profile for(i) an indolent-MASLD (i-MASLD) subtype;(ii) a progressive-MASLD (p-MASLD) subtypes; and / or(iii) an advanced-MASLD (a-MASLD) subtype.Embodiment 13. The method of Embodiment 12, wherein one or more signature genes or nucleotide sequences encoding a signature protein is selected from(a) an i-MASLD subtype signature gene panel, optionally wherein the i-MASLD subtype signature gene panel comprises one or more genes selected from TABLE 12 or TABLE 16;(b) a p-MASLD subtype signature gene panel, optionally wherein the p-MASLD subtype signature gene panel comprises one or more gene selected from TABLE 12 or TABLE 16; and / or(c) an a-MASLD subtype signature gene panel, optionally wherein the a-MASLD subtype signature protein panel comprises one or more proteins selected from TABLE 12 or TABLE 16.Embodiment 14. The method of Embodiment 12, wherein one or more signature genes or nucleotide sequences encoding a signature protein is selected from(a) an i-MASLD subtype signature gene selected from APOH, CFHR2, MPC2, TM9SF2, UGT2B7, ATP5C1 , MRPS15, ESD, HSD17B12, NDUFB1 , CXCR6, or a combination thereof;(b) a p-MASLD subtype signature gene selected from SELO, HNF4A, D2HGDH, SMO, IL17RC, NEIL1 , LLGL2, RXRA, HNF1A, HPN, or a combination thereof; and / or(c) an a-MASLD subtype signature gene selected from DGKA, ZEB2, PARP8, ITGA4, ANKRD44, ENTPD1 , SACS, TCF4, WSB1 , GLS, or a combination thereof,.Embodiment 15. The method of any one of Embodiment 11 to Embodiment 14, wherein the MASLD subtype is identified as(a) an i-MASLD subtype when the PLS-MASLD score comprises or consists of one or more signature genes selected from APOH, CFHR2, MPC2, TM9SF2, UGT2B7, ATP5C1 , MRPS15, ESD, HSD17B12, NDUFB1 , CXCR6, or a combination thereof, and wherein the PLS-MASLD is greater than 1.(b) a p-MASLD subtype when the PLS-MASLD score comprises or consists of one or more signature genes selected from SELO, HNF4A, D2HGDH, SMO, IL17RC, NEIL1 , LLGL2, RXRA, HNF1A, HPN, or a combination thereof, and wherein the PLS-MASLD is greater than 1 ; and / or(c) an a-MASLD subtype when the PLS-MASLD score comprises or consists of one or more signature genes selected from DGKA, ZEB2, PARP8, ITGA4, ANKRD44, ENTPD1 , SACS, TCF4, WSB1 , GLS, or a combination thereof, and wherein the PLSec-MASLD is greater than 1 .Embodiment 16. The method of any one of Embodiment 11 to Embodiment 15, wherein the method of identifying a MASLD subtype in the subject comprises performing a liver biopsy, one or more blood tests to assess liver function, measuring levels of alphafetoprotein in blood, computed tomography, magnetic resonance imaging, or a combination thereof.Embodiment 17. The method of Embodiment 16, wherein the one or more blood tests performed to assess liver function comprises a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof.Embodiment 18. The method of any one of Embodiment 11 to Embodiment 17, further comprising administering one or more prophylactic therapies and / or treatments of MASLD or hepatocellular carcinoma (HCC) to the subject.Embodiment 19. The method of Embodiment 18, wherein the one or more prophylactic therapies and / or treatments of MASLD or HCC comprise surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof.Embodiment 20. The method of Embodiment 19, wherein the drug therapy comprises administration of one or more drugs to the subject, wherein the drugs are selected from(a) an oral CCR2 / 5 antagonist optionally selected from cenicriviroc, or a discoidin domain receptor tyrosine kinase 1 (DDR1) inhibitor, or(b) sorafenib, regorafenib, nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof.Embodiment 21. A method for determining the severity of liver fibrosis in a subject diagnosed with or suspected of having MASLD, the method comprising performing the method of any one of Embodiment 1 to Embodiment 20 and determining the severity of liver fibrosis based on whether the MASLD subtype is identified as i-MASLD, p- MADLS, or a-MADLS.Embodiment 22. A method for determining the number of years to onset of liver fibrosis in a subject diagnosed with or suspected of having MASLD, the method comprising performing the method of any one of Embodiment 1 to Embodiment 20 and determining the number of years to onset of liver fibrosis based on whether the MASLD subtype is identified as i-MASLD, p-MASLD, or a-MADLS.

Claims

CLAIMSWhat is claimed is:

1. A method of predicting the risk for developing hepatocellular carcinoma (HCC) in a subject comprising determining a non-alcoholic fatty liver disease associated prognostic liver secretome signature (PLSec-NAFLD) score for the subject and predicting the risk for developing HCC in the subject based upon the PLSec-NAFLD score, wherein the subject has or is suspected of having non-alcohol fatty liver disease (NAFLD).

2. The method of claim 1 further comprising a method of obtaining the PLSec-NAFLD score for the subject, wherein the method of obtaining the PLSec-NAFLD score comprises:(a) obtaining a blood sample from the subject;(b) subjecting the sample to a multi-analyte profiling assay for protein quantification of lymphotactin, progranulin, angiopoietin 2, and / or hepatocyte growth factor receptor;(c) normalizing protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor to median fluorescent intensity; and(d) converting the normalized protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and and / or hepatocyte growth factor receptor into an aggregated score wherein the aggregated score is the PLSec- NAFLD score.

3. The method of claim 2, wherein the subject is predicted to be at low risk for developing HCC if the PLSec-NAFLD score is less than or equal to 1.

4. The method of claim 2, wherein the subject is predicted to be at high risk for developing HCC if the PLSec-NAFLD score is greater than 1 .

5. The method of any one of claims 1 to 4, further comprising deriving a PLSec-AFP score in the subject based on circulating levels of alpha-fetoprotein (AFP) and at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S in the subject, wherein the PLSec-AFP score is either a “high-risk” PLSec-AFP score or a “low-risk” PLSec-AFP score.

6. The method of claim 5, wherein the subject is:(a) predicted to be at high risk for developing HCC if the PLSec-NAFLD score is greater than 1 and the PLSec-AFP score is high risk;(b) predicted to be at intermediate risk for developing HCC if the PLSec-NAFLD score is greater than 1 and the PLSec-AFP score is low risk;(c) predicted to be at intermediate risk for developing HCC if the PLSec-NAFLD score is less than 1 and the PLSec-AFP score is high risk; or(d) predicted to be at low risk for developing HCC if the PLSec-NAFLD score is less than 1 and the PLSec-AFP score is low risk.

7. The method of any one of claims 4-6, further comprising detecting and / or diagnosing HCC in the subject predicted to be at a high risk for HCC.

8. The method of claim 7, wherein the subject is diagnosed with HCC and / or HCC is detected at an earlier stage compared to a diagnosis made via semi-annual HCC screening.

9. The method of claim 7 or 8, wherein the method of detecting and / or diagnosing HCC in the subject comprises performing a liver biopsy, one or more blood tests to assess liver function, measuring levels of alpha-fetoprotein in blood, computed tomography, magnetic resonance imaging, or a combination thereof.

10. The method of claim 9, wherein the one or more blood tests performed to assess liver function comprises a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gammaglutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof.

11. The method of any one of claims 4 to 10, further comprising administering one or more prophylactic therapies and / or treatments of HCC to the subject predicted to be at a high risk for HCC.

12. The method of claim 11, wherein the one or more prophylactic therapies and / or treatments of HCC comprise surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof.

13. The method of claim 12, wherein the drug therapy comprises administration of one or more drugs to the subject, wherein the drugs are comprised of sorafenib, regorafenib,nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof.

14. The method of claim 12, wherein the chemopreventative agent comprises an antiviral, a statin, an anti-diabetic, a dietary and / or nutritional agent, an anti-inflammatory, an immunomodulatory, or a combination thereof.

15. The method of claim 14, wherein the chemopreventative agent is selected from tenofovir disoproxil fumarate, simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin, metformin, polyunsaturated fatty acids (PLIFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), Branched-chain amino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe), celecoxib, asprin thalidomide, thymalfasin, or a combination thereof.

16. A method of determining a non-alcoholic fatty liver disease associated prognostic liver secretome signature (PLSec-NAFLD) score for a subject comprising:(a) obtaining a blood sample from the subject;(b) subjecting the sample to a multi-analyte profiling assay for protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor to median fluorescent intensity;(c) normalizing the protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor to median fluorescent intensity; and(d) converting the normalized protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor into an aggregated score, wherein the aggregated score is the PLSec-NAFLD score.

17. The method of claim 16, wherein the subject has or is suspected of having non-alcohol fatty liver disease (NAFLD).

18. The method of claim 16 or 17, wherein the subject is predicted to be at low risk for developing HCC if the PLSec-NAFLD score is less than or equal to 1.

19. The method of claim 16 or 17, wherein the subject is predicted to be at high risk for developing HCC if the PLSec-NAFLD score is greater than 1.

20. The method of any one of claims 16 to 19, further comprising deriving a PLSec-AFP score in the subject based on circulating levels of alpha-fetoprotein (AFP) and at leasttwo proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S in the subject, wherein the PLSec-AFP score is either a “high-risk” PLSec-AFP score or a “low-risk” PLSec- AFP score.21 . The method of claim 20, wherein the subject is:(a) predicted to be at high risk for developing HCC if the PLSec-NAFLD score is greater than 1 and the PLSec-AFP score is high risk;(b) predicted to be at intermediate risk for developing HCC if the PLSec-NAFLD score is greater than 1 and the PLSec-AFP score is low risk;(c) predicted to be at intermediate risk for developing HCC if the PLSec-NAFLD score is less than 1 and the PLSec-AFP score is high risk; or(d) predicted to be at low risk for developing HCC if the PLSec-NAFLD score is less than 1 and the PLSec-AFP score is low risk.

22. A diagnostic kit for determining non-alcoholic fatty liver disease associated prognostic liver secretome signature (PLSec-NAFLD) score of a subject comprising one or more reagents for use in a multi-analyte profiling assay.

23. The diagnostic kit of claim 22, wherein the one or more reagents for use in a multianalyte profiling assay comprises beads labeled with antibodies to lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor.

24. The diagnostic kit of claim 22 or 23 further comprising one or more reagents to measure levels of alpha-fetoprotein (AFP), vascular cell adhesion molecule 1 (VCAM- 1), insulin-like growth factor- binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin- 6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, and / or protein S in a sample to determine a PLSec-AFP score of the subject.

25. A method of preventing and / or treating hepatocellular carcinoma (HCC) in a subject at high risk for developing HCC, the method comprising:(a) determining if the subject is at high risk for developing HCC by a method comprising:(i) obtaining a blood sample from the subject;(ii) determining protein levels of at least two liver disease biomarkers in the sample wherein, one of the at least two liver disease biomarkers is selected from lymphotactin or progranulin; and(iii) the other one of the at least two liver disease biomarkers is selected from angiopoietin 2 or hepatocyte growth factor receptor;(iv) converting the protein levels of the at least two disease biomarkers to an aggregated PLSec-NAFLD score based on levels of the at least two disease biomarkers in a control sample, wherein the control is a blood sample from a subject known to not have any liver disease;(v) determining that the subject is at high risk for developing HCC if the aggregated PLSec-NAFLD score is greater than 1 ; and(b) administering one or more prophylactic therapies and / or treatments of HCC to the subject determined to be at high risk for developing HCC.

26. The method of claim 25, wherein the subject is at high risk for developing HCC if any one of lymphotactin and progranulin has a higher protein expression compared to the control and / or any one of angiopoietin 2 and hepatocyte growth factor receptor has equivalent or lower protein expression compared to the control.

27. The method of claim 25 or 26, wherein the subject is at high risk for developing HCC if lymphotactin and progranulin have a higher protein expression compared to the control and / or angiopoietin 2 and hepatocyte growth factor receptor have equivalent or lower protein expression compared to the control.

28. The method of any one of claims 25 to 27, further comprising deriving a PLSec-AFP score in the subject based on circulating levels of alpha-fetoprotein (AFP) and at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S in the subject, wherein the PLSec-AFP score is either a “high-risk” PLSec-AFP score or a “low-risk” PLSec- AFP score, and wherein the subject is at high risk for developing HCC if the aggregated PLSec-NAFLD score is greater than 1 and the PLSec-AFP score is a “high-risk” PLSec-AFP score.

29. The method of any one of claims 25 to 28, wherein the protein levels of the at least two liver disease biomarkers, alpha-fetoprotein (AFP), and / or at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factorbinding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, and protein S are determined by one or more methods selected the group consisting of Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling assay, mass spectrometry,HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two-dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix- assisted laser desorption / ionization (MALDI), or a combination thereof.

30. The method of claim 29, wherein the protein level of the at least two liver disease biomarkers, alpha-fetoprotein (AFP), and / or at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S are determined by ELISA or multi-analyte profiling assay.

31. A method of monitoring outcome of one or more prophylactic therapies and / or treatments of HCC in a subject having or suspected of having non-alcoholic fatty liver disease, the method comprising:(a) determining a first PLSec-NAFLD score for a subject having or suspected of having non-alcoholic fatty liver disease by a method comprising:(i) obtaining a blood sample from the subject;(ii) subjecting the sample to a multi-analyte profiling assay for protein quantification measurements of lymphotactin, progranulin, angiopoietin 2, and / or hepatocyte growth factor receptor to median fluorescent intensity;(iii) normalizing the protein quantification measurements of lymphotactin, progranulin, angiopoietin 2, and / or hepatocyte growth factor receptor to median fluorescent intensity; and(iv) converting the normalized protein quantification measurements of lymphotactin, progranulin, angiopoietin 2 and / or hepatocyte growth factor receptor into an aggregated score, wherein the aggregated score is the PLSec-NAFLD score;(b) administering one or more prophylactic therapies and / or treatments of HCC to the subject;(c) repeating step (a) to obtain a second PLSec-NAFLD score; and(d) monitoring an outcome of the one or more prophylactic therapies and / or treatments of HCC administered in (b) based on differences between the second PLSec-NAFLD score in (c) and the first PLSec-NAFLD score in (a).

32. The method of claim 31 , wherein the one or more prophylactic therapies and / or treatments of HCC administered in (b) has a positive outcome when the first PLSec- NAFLD score is greater than the second PLSec-NAFLD score.

33. The method of claim 31 , wherein the one or more prophylactic therapies and / or treatments of HCC administered in (b) has a negative outcome when the first PLSec- NAFLD score in less than or equal to the second PLSec-NAFLD score.

34. The method of claim 31 , wherein step (a) further comprises obtaining a first PLSec- AFP score and comparing to the first PLS-NAFLD score to derive a first etPLSec- NAFLD score and (c) further comprises obtaining a second PLSec-AFP score and comparing to the second PLSec-NAFLD score to derive a second etPLSec-NAFLD score; wherein the first and second PLSec-AFP score are derived from circulating levels of alpha-fetoprotein (AFP) and at least two proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor-binding protein 7 (IGFBP-7), gp130, matrilysin, interleukin-6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), angiogenin, or protein S in the subject, wherein the effectiveness of the one or more prophylactic therapies and / or treatments of HCC is evaluated by comparing the first and second etPLSec-NAFLD scores.

35. The method of any one of claims 25 to 34, wherein the one or more prophylactic therapies and / or treatments of HCC comprises surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof.

36. The method of claim 35, wherein the drug therapy comprises administration of one or more drugs to the subject, wherein the drugs are comprised of sorafenib, regorafenib, nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof.

37. The method of claim 36, wherein the chemopreventative agent comprises an antiviral, a statin, an anti-diabetic, a dietary and / or nutritional agent, an anti-inflammatory, an immunomodulatory, or a combination thereof.

38. The method of claim 37, wherein the chemopreventative agent is selected from tenofovir disoproxil fumarate, simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin, metformin, polyunsaturated fatty acids (PUFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), Branched-chainamino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe), celecoxib, asprin thalidomide, thymalfasin, or a combination thereof.

39. A method of predicting risk for hepatocellular carcinoma (HCC) in a subject comprising determining a non-alcoholic fatty liver disease associated prognostic liver signature (PLS-NAFLD) score for the subject and predicting the risk for developing HCC in the subject based upon the PLS-NAFLD score, wherein the subject is having or is suspected of having non-alcoholic fatty liver disease.

40. The method of claim 39, further comprising a method of obtaining the PLS-NAFLD score for the subject, wherein the method of obtaining the PLS-NAFLD score comprises:(a) obtaining a tissue sample from the subject;(b) subjecting the tissue sample to a multi-analyte profiling assay for gene expression of one or more genes selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof, to obtain a gene expression measurement for each of the one or more genes;(c) normalizing the gene expression measurements of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1,LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, ML SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, COMMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a control set of genes to obtain a gene expression profile of the subject;(d) converting the gene expression profile of the subject into a PLS-NAFLD score, wherein the PLS-NAFLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile.

41. The method of claim 40, wherein the subject is predicted to be at low risk for hepatocellular carcinoma if the PLS-NAFLD score is less than -1.3013.

42. The method of claim 40, wherein the subject is predicted to be at high or intermediate risk for HCC if the PLS-NAFLD score is greater than or equal to -1.3013.

43. The method of claim 42, further comprising detecting and / or diagnosing HCC in the subject predicted to be at high or intermediate risk for HCC.

44. The method of claim 43, wherein the HCC is detected and / or diagnosed at an earlier stage compared to diagnosis or detection via regular semi-annual HCC screening.

45. The method of claim 43 or 44, wherein detecting and / or diagnosing HCC in the subject comprises performing a liver biopsy, one or more blood tests to assess liver function, computed tomography, magnetic resonance imaging, or a combination thereof.

46. The method of claim 45, wherein the one or more blood tests performed to assess liver function comprises a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or a combination thereof.

47. The method of any one of claims 42 to 46, further comprising administering one or more prophylactic therapies and / or treatments of HCC to the subject.

48. The method of claim 47, wherein the one or more prophylactic therapies and / or treatments of HCC comprises surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof.

49. The method of claim 48, wherein the drug therapy comprises administration of one or more drugs to the subject, wherein the drugs are comprised of sorafenib, regorafenib, nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof.

50. The method of claim 48, wherein the chemopreventative agent comprises an antiviral, a statin, an anti-diabetic, a dietary and / or nutritional agent, an anti-inflammatory, an immunomodulatory, or a combination thereof.

51. The method of claim 50, wherein the chemopreventative agent is selected from tenofovir disoproxil fumarate, simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin, metformin, polyunsaturated fatty acids (PLIFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), Branched-chain amino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe), celecoxib, asprin, thalidomide, thymalfasin, or a combination thereof.

52. A method of determining a non-alcoholic fatty liver disease associated prognostic liver signature (PLS-NAFLD) score for a subject comprising:(a) obtaining a liver biopsy sample from the subject;(b) subjecting the sample to a multi-analyte profiling assay for gene expression of one or more genes selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX, SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2,HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof, to obtain a gene expression measurement for each of the one or more genes;(c) normalizing the gene expression measurements of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAG LA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MLX SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a control set of genes to obtain a gene expression profile of the subject; and(d) converting the gene expression profile of the subject into a PLS-NAFLD score, wherein the PLS-NAFLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile.

53. The method of claim 52, wherein the subject is predicted to be at low risk for hepatocellular carcinoma if the PLS-NAFLD score is less than -1.3013.

54. The method of claim 52, wherein the subject is predicted to be at high or intermediate risk for hepatocellular carcinoma if the PLS-NAFLD score is greater than or equal to - 1.3013.

55. The method of any one of claims 52 to 54, wherein the subject has or is suspected of having non-alcohol fatty liver disease (NAFLD).

56. A diagnostic kit for determining a non-alcoholic fatty liver disease associated prognostic liver signature (PLS-NAFLD) score of a subject comprising one or more reagents for use in a multi-analyte profiling assay.

57. The diagnostic kit of claim 56, wherein the one or more reagents for use in a multianalyte profiling assay comprises one or more nucleic acid probes labeled with color- coded microbeads to mRNA transcribed from one or more genes selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA- DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A1, USP5, RAB4B-EGLN2, MLX SLC4A10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof.

58. A method of preventing and / or treating hepatocellular carcinoma (HCC) in a subject with non-alcoholic fatty liver disease at high risk for HCC, the method comprising:(a) determining if the subject with non-alcoholic fatty liver disease is at high risk for hepatocellular carcinoma by a method comprising:(i) obtaining a liver biopsy sample from the subject;(ii) subjecting the liver biopsy sample to a multi-analyte profiling assay for gene expression of one or more genes selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1,ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MIX, SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA1217, or a combination thereof to obtain a gene expression measurement of each of the one or more genes;(iii) normalizing the gene expression measurements of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA 1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, MI , SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a controlset of genes to obtain a gene expression profile of the subject;(iv) converting the gene expression profile of the subject into a PLS-NAFLD score, wherein the PLS-NAFLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile; and(v) determining that the subject is at high or intermediate risk for hepatocellular carcinoma if the PLS-NAFLD score is greater than or equal to -1.3013, and(b) administering one or more prophylactic therapies and / or treatments of HCC to the subject determined to be at high or intermediate risk for developing hepatocellular carcinoma.

59. A method of monitoring outcome of a prophylactic therapy and / or treatment for HCC in a subject having or suspected of having non-alcoholic fatty liver disease, the method comprising:(a) determining a first PLS-NAFLD score for a subject having or suspected of having non-alcoholic fatty liver disease by a method comprising:(i) obtaining a liver biopsy sample from the subject;(ii) subjecting the liver biopsy sample to a multi-analyte profiling assay for gene expression of one or more genes selected from ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, ML SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, GOLPH3, TTC27, THOP1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TROVE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, SORBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72,CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, KIAA 1217 or a combination thereof to obtain a gene expression measurement of each of the one or more genes;(iii) normalizing the gene expression measurements of ACRBP, USB1, HLA-A, TPH1, ADAMTSL1, B3GNTL1, PHGR1, DYM, PIK3IP1, DTD1, ITGAL, DAGLA, RAB4B, DCAF4, LCK, PLP2, RGS3, PSME3, ICOS, HLA-DPA1, MPV17L2, ABCC5, VAV1, KIFC2, ATP6V0B, RNF166, HIST1H2BI, MFSD11, PIM1, SLA2, EHD2, ERCC4, SKAP1, EP400, ABI3, NAT10, STX6, BCL2L1, LRRC27, RGS10, P2RY13, ARFGEF2, UBXN2A, HLA-G, ERI1, SMPD2, CD6, MIDN, ADAT2, SLC15A3, TECPR1, CD109, BTN3A 1, USP5, RAB4B-EGLN2, ML SLC4A 10, TAF5L, IPCEF1, MIA-RAB4B, RAB40C, TCIRG1, PRF1, C0MMD2, HTT, AKNA, ITGAX, TUBA 1C, SYTL1, HDC, ARF3, WBSCR16, PRR5L, HIST1H2BK, PNKD, CCDC88C, PPP1R37, ATP6V0A2, CD48, NAT9, AURKA, SMARCC1, NPPA, TLE2, MKLN1, DLG2, GAREM, MASTL, TTC6, G0LPH3, TTC27, TH0P1, BDH2, ZFAND5, PPP1R1C, NSUN6, CSNK2A2, DDX59, PGAP1, BAZ2B, ETNK1, TR0VE2, TP53BP2, ANAPC10, THSD7A, NR5A2, C1orf111, PRR14L, WDR43, S0RBS2, ARNT, IKZF5, RAB3GAP2, ABHD17B, AFTPH, RTN4R, SH3RF1, PITPNB, PHF10, L3MBTL4, LATS2, C9orf72, CACNB2, CHD1, CTBP2, C16orf87, TMEM184C, PPFIBP1, STAU2, PLD1, RSBN1, FAF1, and / or KIAA1217 to expression levels of a control set of genes to obtain a gene expression profile of the subject; and(iv) converting the gene expression profile of the subject into a PLS-NAFLD score, wherein the PLS-NAFLD score is a numerical value corresponding to a similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile;(b) administering one or more prophylactic therapies and / or treatments of HCC to the subject; and(c) repeating step (a) to determine a second PLS-NAFLD score; and(d) monitoring an outcome of the one or more prophylactic therapies and / or treatments of HCC administered in (b) based on differences between the second PLS-NAFLD score in (c) and the first PLS-NAFLD score in (a).

60. The method of claim 59, wherein the one or more treatments of HCC administered in (b) has a positive outcome when the first PLS-NAFLD score in greater than the second PLS-NAFLD score.61 . The method of claim 59, wherein the one or more treatments of HCC administered in (b) has a negative outcome when the first PLS-NAFLD score in less than or equal to the second PLS-NAFLD score.

62. The method of any one of claims 59 to 61 , wherein the one or more prophylactic therapies and / or treatments of HCC comprises surgical removal of one or more liver tumors, liver transplant, radiation therapy, drug therapy, immunotherapy, chemotherapy, a chemopreventative agent, or a combination thereof.

63. The method of claim 62, wherein the drug therapy comprises administration of one or more drugs to the subject, wherein the drugs are comprised of sorafenib, regorafenib, nivolumab, erlotinib, lenvatinib, cabozantinib, ramucirumab, pembrolizumab, durvalumab, tremelimumab, atezolizumab, bevacizumab, or a combination thereof.

64. The method of claim 62, wherein the chemopreventative agent comprises an antiviral, a statin, an anti-diabetic, a dietary and / or nutritional agent, an anti-inflammatory, an immunomodulatory, or a combination thereof.

65. The method of claim 64, wherein the chemopreventative agent is selected from tenofovir disoproxil fumarate, simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin, metformin, polyunsaturated fatty acids (PUFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), Branched-chain amino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe), celecoxib, aspirin thalidomide, thymalfasin, or a combination thereof.

66. A method of excluding a subject from a prophylactic therapy for hepatocellular carcinoma, the method comprising determining a PLS-NAFLD score according to claim 50 and excluding the subject from the prophylactic therapy when the PLS-NAFLD score is less than -1.3013.

67. The method of claim 66, wherein the prophylactic therapy comprises a chemopreventative agent selected from an antiviral, a statin, an anti-diabetic, a dietary and / or nutritional agent, an anti-inflammatory, an immunomodulatory, or a combination thereof.

8. The method of claim 67, wherein the chemopreventative agent is selected from tenofovir disoproxil fumarate, simvastatin, atorvastatin, lovastatin, pravastatin, rosuvastatin, metformin, polyunsaturated fatty acids (PLIFAs), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), Branched-chain amino acid (BCAA), Vitamin D, S-adenosylmethionine (SAMe), celecoxib, aspirin thalidomide, thymalfasin, or a combination thereof.

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