Methods and assays for early detection and diagnosis of metabolic dysfunction-associated steatohepatitis-related hepatocellular carcinoma or risk thereof

Abstract

Methods for identifying metabolic dysfunction in a cell indicative of metabolic dysfunction-associated steatohepatitis (MASH)-related hepatocellular carcinoma (HCC) or risk thereof include: detecting a biological switch in one or more cells of a biological sample based on the detected expression level or activity of one or more biomarkers selected from survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3k9me3, and H3K9ac; and identifying the biological sample as including one or more metabolically dysfunctional cells indicative of MASH-related HCC or risk thereof subsequent to the detection of the biological switch. Methods and assays for diagnosis or prognosis of MASH-related HCC or risk thereof as well as methods for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC which utilize the foregoing biomarkers are also provided. A combination of biomarkers including two or more of the foregoing biomarkers can be utilized in the provided methods and assays.

Description

METHODS AND ASSAYS FOR EARLY DETECTION AND DIAGNOSIS OFMETABOLIC DYSFUNCTION-ASSOCIATED STEATOHEPATITIS-RELATEDHEPATOCELLULAR CARCINOMA OR RISK THEREOFRELATED APPLICATIONS

[0001] This application claims priority to U.S. Patent Application Serial No. 63 / 584,679 filed on September 22, 2023, the entire disclosure of which is incorporated herein by reference.TECHNICAL FIELD

[0002] The presently-disclosed subject matter generally relates to methods and assays that make use of biomarkers for early detection and diagnosis of non-alcoholic steatohepatitis (NASH), which is now more commonly referred to as metabolic dysfunction-associated steatohepatitis (MASH), and / or MASH-related hepatocellular carcinoma (HCC).BACKGROUND

[0003] Chronic liver disease is increasing. It has also been observed that the global incidence and prevalence of chronic liver disease and its sequelae end-stage liver disease (ESLD) and hepatocellular carcinoma (HCC) are also increasing because of viral hepatitis and a continuous spread of the metabolic disturbances from an obesity epidemic. Between 1990 and 2013, global viral hepatitis deaths increased from 0.89M to 1.45M, and in 2013 viral hepatitis was the seventh leading cause of death worldwide.

[0004] Nevertheless, hepatitis B virus (HBV) vaccination has shown to decrease its prevalence infection rates in endemic countries, and it is expected that global HCV deaths will continue to decrease because of the development of highly successful anti -HCV therapies. In contrast though, it is estimated that 2.2 billion people around the world will be overweight and 1.1 billion will be obese by the year 2030. Indeed, metabolic dysfunction-associated steatohepatitis (MASH) related ESLD is now the second most common cause for liver transplantation in the US, and affects preferentially racial / ethnic minorities (African American and Hispanic), low socio-economic populations with health access disparities, and veterans. Similar disparities also influence HCC survival, the fastest rising malignancy with the highest cancer-related death rate in the US.

[0005] Accordingly, assays and methods that make use of a signature from biological markers predictive of MASH progression and early-stage HCC detection would be both highly desirable and beneficial, and may increase the overall survival suffering from or at risk from those diseases.SUMMARY

[0006] The presently disclosed subject matter meets some or all of the above-identified needs, as will become evident to those of ordinary skill in the art after a study of information provided in this document.

[0007] This summary describes several embodiments of the presently disclosed subject matter, and in many cases lists variations and permutations of these embodiments. This summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment cantypically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently disclosed subject matter, whether listed in this summary or not. To avoid excessive repetition, this summary does not list or suggest all possible combinations of such features.

[0008] The presently disclosed subject matter generally includes methods and assays which make use of biomarkers whose detected expression level or activity may prove useful in the early detection of metabolic dysfunction-associated steatohepatitis (MASH)-related hepatocellular carcinoma (HCC) or risk of MASH-related HCC in a subject.

[0009] Provided are methods for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk of MASH-related HCC. In some embodiments, a method for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk of MASH- related HCC comprises the steps of: (a) obtaining a biological sample from a subject that includes one or more cells; (b) assaying the biological sample to detect an expression level or activity of one or more biomarkers in the one or more cells of the biological sample; (c) detecting a biological switch in the one or more cells of the biological sample based on the detected expression level or activity of the one or more biomarkers relative to a control expression level or activity of the one or more biomarkers; and (d) identifying the biological sample as including one or more metabolically dysfunctional cells indicative of MASH-related HCC or risk of MASH-related HCC subsequent to the detection of the biological switch in the one or more cells of the biological sample.

[0010] In some embodiments, detecting the biological switch includes detecting an apoptotic to non-apoptotic switch in the one or more cells of the biological sample, detecting a downregulation of cell autophagy in the one or more cells of the biological sample, and / or anincrease in oxidative stress in the one or more cells of the biological sample. In some embodiments the one or more biomarkers is a combination of biomarkers including two or more biomarkers selected from survivin, second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), Na+ / K+ transporting subunit alpha 1 (ATP1A1), caspase-cleaved cytokeratin-18 (CK-18) fragment, forkhead box protein 01 (F0X01), microtubule-associated protein 1 light chain 3-II (LC3-II), brain and muscle arnt-like protein- 1 (BMAL1), Rev-erb-alpha, glutathione (GSH), histone 3 lysine 9 trimethylation (H3K9me3), and histone 3 lysine 9 acetylation (H3K9ac). In some embodiments, the combination of biomarkers includes one or more biomarkers selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac.

[0011] In some embodiments of the method for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk of MASH-related HCC, detecting the biological switch includes detecting an apoptotic to non-apoptotic switch in the one or more cells of the biological sample by detecting an increase in expression level or activity of survivin, detecting an increase in expression level or activity of Smac / DIABLO, detecting an increase in expression level or activity of ATP1A1, and / or detecting a decrease in expression level or activity of FOXO1 in the one or more cells of the biological sample relative to a control expression level or activity for such biological marker(s). In some embodiments, detecting the biological switch includes detecting a downregulation of cell autophagy in the one or more cells of the biological sample by detecting a decrease in expression level or activity of LC3-II, detecting a decrease in the expression level or activity of BMAL1, and / or detecting a decrease in the expression level or activity of Rev-erb-alpha in the one or more cells of the biological sample relative to a control expression level or activity for such biological marker(s). In some embodiments, detecting thebiological switch includes detecting an increase in oxidative stress in the one or more cells of the biological sample by detecting a decrease in expression level or activity of GSH, detecting an increase in expression level or activity of H3K9me3, and / or detecting an increase in expression level or activity of H3K9ac in the one or more cells of the biological sample relative to a control expression level or activity for such biological marker(s). In some embodiments, detecting the biological switch includes detecting the apoptotic to non-apoptotic switch, a downregulation in cell autophagy, and an increase in oxidative stress in the one or more cells of the biological sample. In one such embodiment, the combination of biomarkers includes: one or more biomarkers selected from survivin, Smac / DIABLO, ATP 1 Al, caspase-cleaved CK-18 fragment, and F0X01; one or more biomarkers selected from LC3-II, BMAL1, and Rev-erb-alpha; and one or more biomarkers selected from GSH, H3K9me3, and H3K9ac.

[0012] In some embodiments of the method for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk of MASH-related HCC, the method further includes steps of: assaying the biological sample to detect an amount of one or more metabolites including a lipid, a carbohydrate, a peptide, or a combination thereof in the one or more cells of the biological sample; and detecting a difference in the amount of the one or more metabolites in the one or more cells of the biological sample relative to a control amount of the one or more metabolites. In such embodiments, the identification of the biological sample as including one or more metabolically dysfunctional cells indicative of MASH-related HCC or risk of MASH- related HCC occurs subsequent to detecting the difference in the amount of the one or more metabolites in the one or more cells of the biological sample relative to the control amount of the one or more metabolites. In some embodiments, the one or more metabolites includes oxidized glutathione, 3-ureidopropionic acid, lysine, tyrosine, zeatin, butyric acid, benzoic acid, citricacid, glucose, homovanillic acid, indole-3 -pyruvic acid, methyl beta-d galactoside, citramalic acid, malic acid, arachidonic acid, corticosterone, heptadecanoic acid, lignoceric acid, nervonic acid, oleic acid, palmitic acid, palmitoleic acid, or combinations thereof.

[0013] Further provided, in some embodiments, are methods for diagnosis or prognosis of MASH-related HCC or risk of MASH-related HCC in a subject. In some embodiments, a method for diagnosis or prognosis of MASH-related HCC or risk of MASH-related HCC in a subject comprises the steps of (a) obtaining a biological sample from the subject; (b) detecting an expression level or activity in the biological sample of one or more biomarkers; (c) detecting a measurable difference between the expression level or activity of the one or more biomarkers in the biological sample relative to a control expression level or activity of the one or more biomarkers; and (d) diagnosing the subject as having MASH-related HCC or risk thereof based on the detected measurable difference between the expression level or activity of the one or more biomarkers in the biological sample relative to the control expression level or activity level of one or more biomarkers. In some embodiments, the one or more biomarkers is a combination of biomarkers including two or more biomarkers selected from survivin, Smac / DIABLO, ATP 1 Al, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac. In some embodiments, the combination of biomarkers includes one or more biomarkers selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac.

[0014] In some embodiments, the method for diagnosis or prognosis of MASH-related HCC or risk of MASH-related HCC, the detected measurable difference may correspond to the detection of an increase in expression level or activity of survivin, an increase in expression level or activity of Smac / DIABLO, an increase in expression level or activity of ATP1A1, a decrease in expression level or activity of FOXO1, a decrease in expression level or activity of LC3-II, adecrease in the expression level or activity of BMAL1, a decrease in the expression level or activity of Rev-erbo-alpha, a decrease in expression level or activity of GSH, an increase in expression level or activity of H3K9me3, and / or an increase in expression level or activity of H3K9ac in the biological sample.

[0015] In some embodiments, the method for diagnosis or prognosis of MASH-related HCC or risk of MASH-related HCC further includes a step of selecting a treatment or modifying a treatment for MASH-related HCC based on the detected expression level or activity of the one or more biomarkers in the one or more cells of the biological sample relative to the control expression level or activity of the one or more biomarkers.

[0016] In some embodiments, the method for diagnosis or prognosis of MASH-related HCC or risk of MASH-related HCC further includes steps of: detecting one or more metabolites including a lipid, a carbohydrate, a peptide, or a combination thereof in the biological sample; and detecting a measurable difference in the amount of the one or more metabolites in the one or more cells of the biological sample relative to a control amount of the one or more metabolites. In such embodiments, diagnosing the subject as having MASH-related HCC or risk thereof may be further based on the detected measurable difference between the amount of the one or more metabolites in the biological sample relative to the control amount of the one or more metabolites.

[0017] Still further provided, in some embodiments are methods for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC in a subject. In some embodiments, a method for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC in a subject comprises the steps of: (a) obtaining a series of biological samples taken over a period of time from the subject; (b) assaying the series of biologicalsamples to determine an expression level or activity in each of the biological samples of one or more biomarkers; and (c) detecting a measurable change in the expression level or activity of the one or more biomarkers in the series of biological samples to thereby determine whether to initiate or continue prophylaxis or treatment of MASH-related HCC in the subject. In some embodiments, the one or more biomarkers is a combination of biomarkers including two or more biomarkers selected from survivin, Smac / DIABLO, ATP 1 Al, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac. In some embodiments, the combination of biomarkers includes one or more biomarkers selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac. In some embodiments, the subject has MASH-related HCC. In some embodiments, the subject does not have MASH-related HCC.

[0018] In some embodiments of the method for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC in a subject, the detected measurable change in the expression level or activity of the one or more biomarkers in the series of biological samples may correspond to the detection of an increase in expression level or activity of survivin, an increase in expression level or activity of Smac / DIABLO, an increase in expression level or activity of ATP1A1, a decrease in expression level or activity of FOXO1, a decrease in expression level or activity of LC3-II, a decrease in the expression level or activity of BMAL1, a decrease in the expression level or activity of Rev-erbo-alpha, a decrease in expression level or activity of GSH, an increase in expression level or activity of H3K9me3, and / or an increase in expression level or activity of H3K9ac in the series of biological samples.

[0019] In some embodiments, the method for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC in a subject further includes the steps of: detecting one or more metabolites including a lipid, a carbohydrate, a peptide, or a combinationthereof in the series of biological samples; and detecting a measurable difference in the amount of the one or more metabolites in the series of biological samples. In such embodiments, the determination as to whether to initiate or continue prophylaxis or treatment of MASH-related HCC in the subject may be further based on the detected measurable difference between the amount of the one or more metabolites in the series of biological samples.

[0020] In some embodiments of the method for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC in a subject, the prophylaxis or treatment includes administration of an antagonist of a Na / K ATPase / Src receptor complex to the subject.

[0021] In yet further embodiments, assays for screening for MASH-related HCC or risk of MASH-related HCC in a subject are provided. In some embodiments, an assay for screening for MASH-related HCC in a subject comprises: (a) applying, to a biological sample from the subject, one or more agents capable of detecting an expression level or activity of one or more biomarkers selected from survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and / or H3K9ac; and (b) detecting the expression level or activity of the one or more biomarkers in the biological sample. In some embodiments, the one or more biomarkers is a combination of biomarkers including two or more biomarkers selected from survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac. In some embodiments, the combination of biomarkers includes one or more biomarkers selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac. In some embodiments, the combination of biomarkers includes each of survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac.

[0022] In some embodiments of the various methods and assays disclosed herein, the biological sample(s) is from a human subject. In some embodiments of the various methods and assays disclosed herein, the biological sample includes blood, plasma, serum, or a tumor biopsy. In some embodiments of the various methods and assays disclosed herein, detection of the expression level or activity of one or more biomarkers includes assaying the biological sample using mass spectrometry (MS) analysis, immunoassay analysis, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay, or a combination thereof.

[0023] Further features and advantages of the present disclosure will become evident to those of ordinary skill in the art after a study of the description, figures, and non-limiting examples in this document.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG. 1A is a series of images showing expression of survivin and second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO) expression in normal, metabolic dysfunction-associated steatohepatitis (MASH) and hepatocellular carcinoma (HCC) human liver tissues.

[0025] FIG. IB is a graph showing plasma concentration of survivin in normal, MASH, and HCC subject (*p<0.05, ANOVA).

[0026] FIG. 1C is a graph showing plasma concentration of Smac / DIABLO in normal, MASH, and HCC subject s(*p<0.05, ANOVA).

[0027] FIG. ID is a graph showing plasma concentration of caspase-cleaved cytokeratin-18 fragment in normal, MASH, and HCC subjects (*p<0.05, ANOVA).

[0028] FIG. IE is a graph showing plasma concentration of microtubule-associated protein 1 light chain 3-II (LC3-II) in normal, MASH, and HCC subjects (*p<0.05, ANOVA).

[0029] FIG. IF is a graph showing plasma concentration of histone 3 lysine 9 trimethylation (H3K9me3) in normal, MASH, and HCC subjects (*p<0.05, **p<0.01, ANOVA).

[0030] FIG. 1G is a graph showing plasma concentration of glutathione (GSH) in normal, MASH, and HCC subjects (*p<0.05, P<0.01, ANOVA).

[0031] FIG. 2A is a graph showing plasma concentration of survivin in 12-week MASH mice on a normal mouse chow (NMC) and high-fat diet (HFD).

[0032] FIG. 2B is a graph showing plasma concentration of survivin in 24- and 28-week treated (low-dose pNaKtide and high-dose pNaktide) and untreated MASH-HCC mice on high- fat diets (p<0.05, ANOVA).

[0033] FIG. 2C is a graph showing plasma concentration of Smac / DIABLO in 12-week MASH mice on a normal mouse chow (NMC) and high-fat diet (HFD).

[0034] FIG. 2D is a graph showing plasma concentration of Smac / DIABLO in 24- and 28- week treated (low-dose pNaKtide and high-dose pNaktide) and untreated MASH-HCC mice on high-fat diets.

[0035] FIG. 3A is a graph showing plasma concentration of survivin in severe combined immunodeficiency (SCID) mice with no HCC tumor growth, untreated SCID mice with HCC tumor growth, and treated (pNaKtide) SCID mice with HCC tumor growth.

[0036] FIG. 3B is a graph showing plasma concentration of Smac / DIABLO in SCID mice with no HCC tumor growth, untreated SCID mice with HCC tumor growth, and treated (pNaKtide) SCID mice with HCC tumor growth (*p<0.05, ANOVA).

[0037] FIG. 3C is a graph showing plasma concentration of caspase-cleave in SCID mice with no HCC tumor growth, untreated SCID mice with HCC tumor growth, and treated (pNaKtide) SCID mice with HCC tumor growth (*p<0.05, ANOVA).

[0038] FIG. 3D is a graph showing percentage of apoptotic cells in untreated SCID mice with HCC tumor growth and treated (pNaKtide) SCID mice with tumor growth (**p<0.01,ANOVA).

[0039] FIG. 3E is a series of images showing apoptosis activity in untreated and pNaKtide- treated xenograft tumors.

[0040] FIG. 4 is a series of images showing Smac / DIABLO and survivin expression in normal, MASH, HCC, and metastases human liver tissues.

[0041] FIG. 5A is a graph showing plasma concentration of Smac / DIABLO in 24-week MASh-HCC NMC, HFD, HFD-low-treated (low-dose pNaKtide) and HFD-High-treated (high- dose pNaKtide) mice.

[0042] FIG. 5B is a graph showing plasma concentration of survin in 24-week MASh-HCC HFD, HFD-low-treated (low-dose pNaKtide) and HFD-High-treated (high-dose pNaKtide) mice.

[0043] FIG. 6 is a graph showing pNaKtide half-life in mouse plasma.

[0044] FIG. 7 is a graph showing left ventricular ejection fraction (LVEF) by echo in the mice by diet ± treatment.

[0045] FIG. 8 is a schematic diagram showing a pNaKtide dosage protocol.

[0046] FIG. 9A is a series of images showing normalization of Na+ / K+ transporting subunit alpha 1 (ATP1A1) through pNaKtide treatment has no effect on normal hepatocytes.

[0047] FIG. 9B is a series of images showing normalization of ATP1A1 through pNaKtide treatment inhibits HCC in SNU475 human cell lines.

[0048] FIG. 9C is an image of a western blot showing the effect of pNaKtide on nuclear forkhead box protein 01 (FOXO1) expression in SNU475 human cell lines.

[0049] FIG. 9D is an image of a western blot showing the effect of pNaKtide on cytoplasmic FOXO1 expression in SNU475 human cell lines.

[0050] FIG. 9E is a graph showing integrated density of nuclear FOXO1 in untreated and pNaKtide-treated SNU475 human cell lines (*p<0.05, ANOVA).

[0051] FIG. 9F is a graph showing integrated density of cytoplasmic FOXO1 in untreated and pNaKtide-treated SNU475 human cell lines (**p<0.01, ANOVA).

[0052] FIG. 10A is a series of images showing Smac / DIABLO and survivin expression in normal, MASH, HCC, and metastases human liver tissues.

[0053] FIG. 10B is a graph showing the immunoreactive scores of Smac / DIABLO in normal, MASH, HCC, and metastasis liver tissues (*p<0.05, **p<0.01, ANOVA).

[0054] FIG. 10C is a graph showing the immunoreactive scores of survivin in normal, MASH, HCC, and metastasis liver tissues (**p<0.01, ANOVA).

[0055] FIG. HA is a graph showing ATP1A1 expression in the male livers of ATP1A1 wild-type, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0056] FIG. 11B is a graph showing brain and muscle arnt-like protein-1 (BMAL1) expression in the male livers of ATP1A1 wild-type, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0057] FIG. 11C is a graph showing Rev-erb-alpha expression in the male livers of ATP1A1 wild-type, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0058] FIG. 11D is a graph showing survivin expression in the male livers of ATP 1 Al wildtype, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0059] FIG. HE is a graph showing ATP1A1 expression in the male kidneys of ATP1A1 wild-type, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0060] FIG. 11F is a graph showing BMAL1 expression in the male kidneys of ATP1 Al wild-type, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0061] FIG. 11G is a graph showing Rev-erb-alpha expression in the male kidneys of ATP1A1 wild-type, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0062] FIG. 11H is a graph showing survivin expression in the male kidneys of ATP1A1 wild-type, ATP1A1 floxed, ATP1A1 heterozygous, and ATP1A1 homozygous mice.

[0063] FIG. 12A is a graph showing plasma glucose in HFD, pNaKtide-treated, and exercise-treated MASH mice over the course of 24 weeks.

[0064] FIG. 12B is a graph showing plasma GSH in HFD, pNaKtide-treated, and exercise- treated MASH mice over the course of 24 weeks.

[0065] FIG. 12C is a graph showing plasma GS:SG in HFD, pNaKtide-treated, and exercise-treated MASH mice over the course of 24 weeks.

[0066] FIG. 12D is a graph showing plasma peroxisome proliferator-activated receptor gamma (PPAR-y) expression in NMc, HFD, pNaKtide-treated, and exercise-treated MASH mice over the course of 24 weeks.

[0067] FIG. 12E is a graph showing plasma peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-la) expression in NMC, HFD, pNaKtide-treated, and exercise- treated MASH mice over the course of 24 weeks.

[0068] FIG. 12F is a graph showing FOXO1 expression in NMC, HFD, pNaKtide-treated, and exercise-treated MASH mice over the course of 24 weeks.

[0069] FIG. 13 is an image of a western blot showing the genotyping of Cre-Alb / ATPl Al hepatocyte-specific ATP1A1 KO mice. Wild type mice showed the 351 bp band, while theheterozygote mice showed 351 and 390 bands, and the homozygote mice only the 390 bp band.No bands in the normal control (NC, wild type).

[0070] FIG. 14 is a series of images showing ATP1A1 expression in the Cre-Alb / ATPlAl hepatocyte specific ATP1A1 KO mice (upper panel). While ATP1A1 is <10% expressed in liver from the KO mice (female), the ATP1A1 expression in kidney from the same animal is normal when compared to the WT mice (by immune-confocal assessment). Liver morphology in the hepatocyte specific ATP1 Al KO mice at 10 weeks (lower panel). Livers developed significantly different histology when compared to livers from the WT mice. Primary liver malignancy was noted consistently at 10 weeks.

[0071] FIG. 15A is a graph showing metabolic prints on treated plasma from 24-week untreated and pNaKtide-treated MASH mice, where mice were exposed to normal mouse chow (NMC) or high fat diet (HFD).

[0072] FIG. 15B is a graph showing metabolic prints on treated plasma from 24-week untreated and exercise-treated MASH mice, where mice were exposed to normal mouse chow (NMC) or high fat diet (HFD).

[0073] FIG. 15C are heat maps showing metabolite data of FIGS. 15A and 15B log10transformed (Left: NMC vs. HFD; Middle: HFD vs. HFD-pNaKtide; Right NMC vs. HFD+Exercise) in plasma of mice by diet and treatment and illustrating a significant effect on metabolic expression by treatment (n=5-7 per group, p<0.01, PCA).

[0074] FIG. 15D shows gene expression in mice model and human HCC cell lines. RNA sequencing performed in liver homogenates from a hepatocyte specific ATP1A1 KO mouse, and two human HCC cell lines, and displayed as heat maps (upper) or volcano plots (lower). Whilesamples from KNO mice were compared to WT mice, human HCC non-treated cell lines were compared to cell lines treated with pNaKtide (p<0.01, PCA).

[0075] FIG. 16A is a graph showing plasma concentration of oxidized glutathione in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24- week MASH mice (*p<0.05, **p<0.01, ANOVA).

[0076] FIG. 16B is a graph showing plasma concentration of 3-ureidopropionic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24- week MASH mice (*p<0.05, ANOVA).

[0077] FIG. 16C is a graph showing plasma concentration of lysine in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, **p<0.01, ANOVA).

[0078] FIG. 16D is a graph showing plasma concentration of tyrosine in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, ANOVA).

[0079] FIG. 16E is a graph showing plasma concentration of zeatin in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice.

[0080] FIG. 17A is a graph showing plasma concentration of butyric acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, **p<0.01, ANOVA).

[0081] FIG. 17B is a graph showing plasma concentration of benzoic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, ANOVA).

[0082] FIG. 17C is a graph showing plasma concentration of citric acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice(**p<0.01, ANOVA).

[0083] FIG. 17D is a graph showing plasma concentration of clucose in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice.

[0084] FIG. 17E is a graph showing plasma concentration of homovanillic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, **p<0.01, ANOVA).

[0085] FIG. 17F is a graph showing plasma concentration of indole 3-pyruvic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24- week MASH mice (*p<0.05, ANOVA).

[0086] FIG. 17G is a graph showing plasma concentration of methyl beta-d-galactoside in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24- week MASH mice (**p<0.01, ANOVA).

[0087] FIG. 17H is a graph showing plasma concentration of citramalic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice.

[0088] FIG. 171 is a graph showing plasma concentration of malic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice.

[0089] FIG. 18A is a graph showing plasma concentration of arachidonic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, ANOVA).

[0090] FIG. 18B is a graph showing plasma concentration of corticosterone in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice.

[0091] FIG. 18C is a graph showing plasma concentration of heptadecanoic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24- week MASH mice (*p<0.05, **p<0.01, ANOVA).

[0092] FIG. 18D is a graph showing plasma concentration of lignoceric acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, ANOVA).

[0093] FIG. 18E is a graph showing plasma concentration of linoleic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, ANOVA).

[0094] FIG. 18F is a graph showing plasma concentration of nervonic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, ANOVA).

[0095] FIG. 18G is a graph showing plasma concentration of oleic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, ANOVA).

[0096] FIG. 18H is a graph showing plasma concentration of palmitic acid in untreated NMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice.

[0097] FIG. 181 is a graph showing plasma concentration of palmitoleic acid in untreatedNMC and HFD 24-week MASH mice and treated (pNaKtide or exercise) HFD 24-week MASH mice (*p<0.05, **p<0.01, ANOVA).DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0098] The details of one or more embodiments of the presently-disclosed subject matter are set forth in this document. Modifications to embodiments described in this document, and other embodiments, will be evident to those of ordinary skill in the art after a study of the information provided in this document. The information provided in this document, and particularly the specific details of the described exemplary embodiments, is provided primarily for clearness of understanding and no unnecessary limitations are to be understood therefrom. In case of conflict, the specification of this document, including definitions, will control.

[0099] While the terms used herein are believed to be well understood by those of ordinary skill in the art, certain definitions are set forth to facilitate explanation of the presently-disclosed subject matter.

[0100] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which the invention(s) belong.

[0101] All patents, patent applications, published applications and publications, GenBank sequences, databases, websites and other published materials referred to throughout the entire disclosure herein, unless noted otherwise, are incorporated by reference in their entirety.

[0102] Where reference is made to a URL or other such identifier or address, it is understood that such identifiers can change and particular information on the internet can comeand go, but equivalent information can be found by searching the internet. Reference thereto evidences the availability and public dissemination of such information.

[0103] As used herein, the abbreviations for any protective groups, amino acids and other compounds, are, unless indicated otherwise, in accord with their common usage, recognized abbreviations, or the IUPAC-IUB Commission on Biochemical Nomenclature (see, Biochem. (1972) 11(9): 1726-1732).

[0104] Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently-disclosed subject matter, representative methods, devices, and materials are described herein.

[0105] The present application can “comprise” (open ended), “consist of’ (closed ended), or “consist essentially of’ the components of the present invention as well as other ingredients or elements described herein. As used herein, “comprising” is open ended and means the elements recited, or their equivalent in structure or function, plus any other element or elements which are not recited. The terms “having” and “including” are also to be construed as open ended unless the context suggests otherwise.

[0106] Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a cell” includes a plurality of such cells, and so forth.

[0107] Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims areapproximations that can vary depending upon the desired properties sought to be obtained by the presently-disclosed subject matter.

[0108] As used herein, the term “about,” when referring to a value or to an amount of mass, weight, time, volume, concentration or percentage is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed method.

[0109] As used herein, ranges can be expressed as from “about” one particular value, and / or to “about” another particular value. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.

[0110] As used herein, “optional” or “optionally” means that the subsequently described event or circumstance does or does not occur and that the description includes instances where said event or circumstance occurs and instances where it does not. For example, an optionally variant portion means that the portion is variant or non-variant.

[0111] As noted, non-alcoholic steatohepatitis (NASH) is now more commonly referred to as metabolic dysfunction-associated steatohepatitis (MASH). Accordingly, it is appreciated that any reference to NASH in the present disclosure or drawings is synonymous with MASH.

[0112] The presently disclosed subject matter is based, at least in part, on the discoveries that: (i) certain biological and metabolite markers are differently expressed in subjects with metabolic dysfunction-associated steatohepatitis (MASH) and in subjects with MA SH-r elatedhepatocellular carcinoma (HCC); and (ii) certain expression level or activity characteristics of certain biological markers are useful in identifying various biological switches in the cells of a biological sample indicative of metabolic dysfunction consistent with that found in subjects with MASH-HCC or at risk of MASH-HCC. In this regard, it has been discovered that in the progression of MASH to HCC that an apoptotic to non-apoptotic switch, a downregulation of cell autophagy, and increase in oxidative stress occurs in the cells of biological samples obtained from affected subjects.

[0113] It has further been discovered that the detection of an apoptotic to non-apoptotic switch in the cells of such samples can be detected based on the expression level or activity of survivin, second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), Na+ / K+ transporting subunit alpha 1 (ATP1A1), caspase- cleaved cytokeratin-18 (CK-18) fragment, and / or forkhead box protein 01 (F0X01), and that survivin expression level or activity can be utilized to discriminate between MASH and MASH- related HCC. It has also been discovered that the detection of a biological switch of a downregulation of cell autophagy can be detected based on the expression level or activity of microtubule-associated protein 1 light chain 3 -II (LC3-II), brain and muscle arnt-like protein- 1 (BMAL1), and / or Rev-erb-alpha. Furthermore, it has been discovered that a biological switch of an increase in oxidative stress can be detected based on the expression level or activity of glutathione (GSH), histone 3 lysine 9 tri methylation (H3K9me3), and histone 3 lysine 9 acetylation (H3K9ac), and that GSH expression level or activity may be useful in discriminating between MASH and MASH-related HCC.

[0114] Accordingly, in some embodiments of the presently disclosed subject matter, methods for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or riskof MASH-related HCC are provided that include: obtaining a biological sample from a subject which includes one or more cells; assaying the biological sample to detect an expression level or activity of one or more biomarkers in the one or more cells of the biological sample selected from survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac; detecting a biological switch in the one or more cells of the biological sample based on the detected expression level of the one or more biomarkers and a control expression level or activity of the one or more biomarkers; and identifying the biological sample as including one or more metabolically dysfunctional cells indicative of MASH-related HCC or risk of MASH-related HCC subsequent to and based on the detection of the biological switch in the one or more cells of the biological sample. As MASH can progress to HCC, one or more cells identified as having metabolic dysfunction indicative of MASH can be characterized as having metabolic dysfunction indicative of risk of MASH-related HCC. In other words, a cell or subject identified as having MASH or metabolic dysfunction indicative of MASH can be characterized as being at risk for MASH-related HCC. Furthermore, it is appreciated that where reference is made to obtaining a biological sample from a subject in the various methods disclosed herein, such reference encompasses embodiments in which the biological sample is acquired directly from the subject as well as embodiments in which the biological sample is obtained indirectly from the subject, such as through a third party who initially obtained the biological sample from the subject.

[0115] In various embodiments, detecting the biological switch includes detecting an apoptotic to non-apoptotic switch, a downregulation in cell autophagy, and / or detecting an increase in oxidative stress in the one or more cells in the biological sample. Accordingly, in various embodiments, the detected biological switch can correspond to a single detectedbiological switch or multiple biological switches in the biological sample. To detect multiple biological switches, further confirm the occurrence of a particular biological switch, and / or distinguish between metabolic dysfunction indicative of MASH versus metabolic dysfunction indicative of MASH-related HCC in the one or more cells of the biological sample, the one or more biomarkers assayed to detect the expression level or activity of is, in some embodiments, is a combination of biomarkers including two or more of survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac. Of course, in such embodiments, the above-noted assaying and detecting steps include assaying the biological sample to detect an expression level or activity of the combination of biomarkers in the one or more cells of the biological sample and detecting a biological switch in the one or more cells of the biological sample based on the detected expression level or activity of the combination of biomarkers relative to a control expression level or activity of the combination of biomarkers, respectively. With respect to the methods disclosed herein employing the use of a combination including two or more of survivin, Smac / DIABLO, ATP 1 Al, caspase-cleaved CK-18 fragment, F0X01, LC3-II, BMAL1, Reverb-alpha, GSH, H3K9me3, and H3K9ac, it should be appreciated that embodiments of such methods which encompass the use of each possible combination of the foregoing biomarkers are contemplated herein. In other words, different embodiments of such methods in which each respective embodiment utilizes a different one of the possible 2,036 biomarker combinations are contemplated herein.

[0116] To facilitate the detection of an apoptotic to non-apoptotic switch in the one or more cells of the biological sample, in some embodiments, the combination of biomarkers includes survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, and / or FOXO1.In such embodiments, an apoptotic to non-apoptotic switch in the one or more cells of the biological sample includes detecting an increase in expression level or activity of survivin, an increase in expression level or activity of SmacZDIABLO, an increase in expression level or activity of ATP 1 Al, an increase in expression level or activity of caspase-cleaved CK-18 fragment, a decrease in expression level or activity of F0X01 relative to a control expression level or activity in the one or more cells of the biological sample.

[0117] To facilitate the detection of a downregulation of cell autophagy in the one or more cells of the biological sample, in some embodiments, the combination of biomarkers includes LC3-II, BMAL1, and / or Rev-erb-alpha. In such embodiments, downregulation of cell autophagy can be detected based on the detection of a decrease in expression level or activity of LC3-II, a decrease in the expression level or activity of BMAL1 relative to a control expression level or activity of BMAL1, and / or a decrease in the expression level or activity of Rev-erb- alpha relative to a control expression level or activity in the one or more cells of the biological sample.

[0118] To facilitate the detection of an increase in oxidative stress in the one or more cells of the biological sample, in some embodiments, the combination of biomarkers includes GSH, H3K9me3, and / or H3K9ac. In such embodiments, an increase in oxidative stress can be detected based on the detection of a decrease in expression level or activity of GSH, an increase in expression level or activity of H3K9me3, and / or an increase in expression level or activity of H3K9ac relative to a control expression level or activity in the one or more cells of the biological sample.

[0119] In some embodiments, detecting the biological switch includes detecting each of an apoptotic to non-apoptotic switch, a downregulation in cell autophagy, and an increase inoxidative stress in the one or more cells of the biological sample. In such embodiments, the combination of biomarkers may thus include: one or more biomarkers selected from survivin, Smac / DIABLO, ATP 1 Al, caspase-cleaved CK-18 fragment and FOXO1; one or more biomarkers selected from LC3-II, BMAL1, and Rev-erb-alpha; and one or more biomarkers selected from GSH, H3K9me3, and H3K9ac. In some embodiments, the combination of biomarkers includes at least survivin, Smac / DIABLO, caspase-cleaved CK-18 fragment, LC3-II, BMAL1, Rev-erb-alpha, GSH, and H3K9me3.

[0120] It has also been surprisingly discovered that certain metabolites are provided in different amounts in subjects with MASH. Accordingly, in some embodiments, the method for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk thereof further includes assaying the biological sample to detect one or more metabolites in the one or more cells of the biological sample; and detecting a difference in the amount of the one or more metabolites in the one or more cells of the biological sample relative to a control amount of the one or more metabolites. In such embodiments, identification of the biological sample as including one or more metabolically dysfunctional cells indicative of MASH-related HCC or risk of MASH-related HCC may be based on, and subsequent to the detection of, the difference in the amount of the one or more metabolites in the one or more cells of the biological sample relative to the control amount of the one or more metabolites. In some embodiments, the one or more metabolites include a lipid, a carbohydrate, a peptide, or a combination thereof. In some embodiments, the one or more metabolites are selected from the lipids, carbohydrates, and peptides disclosed in Table 2 below. In some embodiments, the one or more metabolites includes oxidized glutathione, 3-ureidopropionic acid, lysine, tyrosine, zeatin, butyric acid, benzoic acid, citric acid, glucose, homovanillic acid, indole-3 -pyruvic acid, methyl beta-dgalactoside, citramalic acid, malic acid, arachidonic acid, corticosterone, heptadecanoic acid, lignoceric acid, nervonic acid, oleic acid, palmitic acid, palmitoleic acid, or combinations thereof.

[0121] Further provided, in some embodiments of the presently disclosed subject matter, are methods and systems for diagnosis or prognosis of MASH-related HCC or risk of MASH- related HCC that make use of at least one biomarker. In some embodiments, a method for diagnosis or prognosis of MASH-related HCC or risk thereof includes the steps of: obtaining a biological sample from a subject; detecting an expression level or activity in the biological sample of the at least one biomarker; detecting a measurable difference between the expression level or activity of the at least one biomarker in the biological sample relative to a control expression level or activity of the at least one biomarker; and diagnosing the subject as having MASH-related HCC or risk thereof based on the detected measurable difference between the expression level or activity of the at least one biomarker in the biological sample relative to the control expression level of the at least one biomarker. In some embodiments, measurable differences between the expression level or activity of the at least one biomarker relative to the control expression level or activity of the at least one biomarker may correspond to some or all of the increases and decreases of survivin, Smac / DIABLO, ATP1A1, cypase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac referred to above with respect to the various embodiments of the method for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk of MASH-related HCC.

[0122] In some embodiments, the at least one biomarker used to diagnose or prognosis of MASH-related HCC or risk thereof is selected from survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3,H3K9ac, and combinations thereof. In some embodiments, the at least one biomarker comprises a combination of two or more biomarkers selected from survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, F0X01, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, H3K9ac. Accordingly, in such embodiments, the control expression level or activity against which the expression level or activity of the combination of biomarkers is assessed would correspond to control expression levels or activities of biomarkers corresponding to those utilized in the combination. In some embodiments, the combination of biomarkers includes one or more biomarkers selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac. In some embodiments, measurable differences between the expression level or activity of the at least one biomarker relative to the control expression level or activity of the at least one biomarker may correspond to some or all of the increases and decreases of survivin, Smac / DIABLO, ATP1A1, cypase-cleaved CK-18 fragment, F0X01, LC3-II, BMAL1, Rev-erb- alpha, GSH, H3K9me3, and H3K9ac referred to above with respect to the various embodiments of the method for identifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk of MASH-related HCC.

[0123] In some embodiments, the method for diagnosis or prognosis of MASH-related HCC or risk of MASH-related HCC further includes steps of: detecting an amount of one or more metabolites in the biological sample; and detecting a measurable difference between the amount of the one or more metabolites in the biological sample relative to a control amount of the one or more metabolites. In such embodiments, diagnosing the subject as having MASH- related HCC or risk thereof may be further based on the detected measurable difference between the amount of the one more metabolites in the biological sample relative to the control amount of the one or more metabolites. In some embodiments, the one or more metabolites include a lipid,a carbohydrate, a peptide, or a combination thereof. In some embodiments, the one or more metabolites are selected from the lipids, carbohydrates, and peptides disclosed in Table 2 below. In some embodiments, the one or more metabolites includes oxidized glutathione, 3- ureidopropionic acid, lysine, tyrosine, zeatin, butyric acid, benzoic acid, citric acid, glucose, homovanillic acid, indole-3 -pyruvic acid, methyl beta-d galactoside, citramalic acid, malic acid, arachidonic acid, corticosterone, heptadecanoic acid, lignoceric acid, nervonic acid, oleic acid, palmitic acid, palmitoleic acid, or combinations thereof.

[0124] In some embodiments, the method for diagnosis or prognosis of MASH-related HCC or risk of MASH-related HCC may further include a step of selecting a treatment or modifying treatment for MASH-related HCC based on the measurable differences between the expression level or activity of the at least one biomarker in the biological sample relative to the control expression level or activity of the at least one biomarker. In some embodiments, treatment includes administering an antagonist of Na / K ATPase / Src receptor complex to the subject. Suitable antagonists of Na / K ATPase / Src receptor complex which may be utilized include, but are not limited to, those disclosed in U.S. Patent No. 11,958,915, which is incorporated herein by reference in its entirety.

[0125] Control expression levels or activities of survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac, and control amounts of the one or more metabolites utilized in the various methods disclosed herein can be derived from the testing of biological samples from one or more healthy control subjects which do not display symptoms associated with MASH.

[0126] In some embodiments of the methods disclosed herein, it may be desirable to determine whether the metabolic dysfunction identified in the biological sample is indicative of,or the subject has, MASH or MASH-related HCC in the subject. As noted, it has been discovered that survivin and / or GSH expression level or activity may prove useful in distinguishing between MASH and MASH-related HCC. Accordingly, in some embodiments, the biomarkers tested in the various methods disclosed herein can include survivin and / or GSH and include steps of: detecting at least one of an increase in expression level or activity of survivin and a decrease in expression level or activity of GSH in the biological sample from the subject relative to the expression level or activity of survivin and / or GSH from one or more subjects with MASH that has not yet resulted in MASH-related HCC; and identifying the subject as having MASH-related HCC subsequent to the detection of the increase in expression level or activity of survivin and / or GSH in the biological sample from the subject. In some embodiments, the subject may be identified as having MASH-related HCC based on the detection in an increase in expression level or activity of survivin and / or the detection of a decrease in expression level or activity of GSH in the biological sample relative to the expression level or activity of one or more subjects with MASH that has not yet resulted in MASH-related HCC. Accordingly, in some embodiments of the various methods disclosed herein, controls from both healthy control subjects and MASH subjects may be utilized.

[0127] The exemplary human biomarkers described herein are not intended to limit the present subject matter to human polypeptide biomarkers or mRNA biomarkers only. Rather, the present subject matter encompasses biomarkers across animal species that are associated with MASH and / or HCC.

[0128] A “biomarker” is a molecule useful as an indicator of a biologic state in a subject.With reference to the present subject matter, the biomarkers disclosed herein can be polypeptides that exhibit a change in expression level or activity, which can be correlated with the risk ofdeveloping, the presence of, or the progression of MASH and / or MASH-related HCC in a subject. In addition, the biomarkers disclosed herein are inclusive of messenger RNAs (mRNAs) encoding the biomarker polypeptides, as measurement of a change in expression of an mRNA can be correlated with changes in expression of the polypeptide encoded by the mRNA. As such, determining an amount of a biomarker in a biological sample is inclusive of determining an amount of a polypeptide biomarker and / or an amount of an mRNA encoding the polypeptide biomarker either by direct or indirect (e.g., by measure of a complementary DNA (cDNA) synthesized from the mRNA) measure of the mRNA.

[0129] The terms “diagnosing” and “diagnosis” as used herein refer to methods by which the skilled artisan can estimate and even determine whether or not a subject is suffering from a given disease or condition. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, such as for example a marker, the amount (including presence or absence) of which is indicative of the presence, severity, or absence of the condition.

[0130] Along with diagnosis, clinical disease prognosis is also an area of great concern and interest. It is important to know the stage and rapidity of advancement of the MASH and / or MASH-related HCC in order to plan the most effective therapy. If a more accurate prognosis can be made, appropriate therapy, and in some instances less severe therapy for the patient can be chosen. Measurement of combinations of biomarker levels disclosed herein can be useful in order to categorize subjects according to advancement of MASH and / or MASH-related HCC who will benefit from particular therapies and differentiate from other subjects where alternative or additional therapies can be more appropriate.

[0131] As such, “making a diagnosis” or “diagnosing”, as used herein, is further inclusive of determining a prognosis, which can provide for predicting a clinical outcome (withor without medical treatment), selecting an appropriate treatment (or whether treatment would be effective), or monitoring a current treatment and potentially changing the treatment, based on the measure of diagnostic biomarker levels disclosed herein.

[0132] The phrase "determining a prognosis" as used herein refers to methods by which the skilled artisan can predict the course or outcome of a condition in a subject. The term "prognosis" does not refer to the ability to predict the course or outcome of a condition with 100% accuracy, or even that a given course or outcome is predictably more or less likely to occur based on the presence, absence or levels of test biomarkers. Instead, the skilled artisan will understand that the term "prognosis" refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition. For example, in individuals not exhibiting the condition (e.g., not expressing the biomarker or expressing it at a reduced level), the chance of a given outcome may be about 3%. In certain embodiments, a prognosis is about a 5% chance of a given outcome, about a 7% chance, about a 10% chance, about a 12% chance, about a 15% chance, about a 20% chance, about a 25% chance, about a 30% chance, about a 40% chance, about a 50% chance, about a 60% chance, about a 75% chance, about a 90% chance, or about a 95% chance.

[0133] The skilled artisan will understand that associating a prognostic indicator with a predisposition to an adverse outcome is a statistical analysis. For example, a biomarker level (e g., quantity of expression in a sample) of greater than a control level in some embodiments can signal that a subject is more likely to suffer from or experience MASH and / or MASH-related HCC than subjects with a level less than or equal to the control level, as determined by a level of statistical significance. Additionally, a change in marker concentration from baseline levels canbe reflective of subject prognosis, and the degree of change in marker level can be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations, and determining a confidence interval and / or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983, incorporated herein by reference in its entirety. Preferred confidence intervals of the present subject matter are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while preferred p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001.

[0134] In other embodiments, a threshold degree of change in the level of a prognostic or diagnostic biomarker can be established, and the degree of change in the level of the indicator in a biological sample can simply be compared to the threshold degree of change in the level. A preferred threshold change in the level for markers of the presently disclosed subject matter is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 50%, about 75%, about 100%, and about 150%. In yet other embodiments, a "nomogram" can be established, by which a level of a prognostic or diagnostic indicator can be directly related to an associated disposition towards a given outcome. The skilled artisan is acquainted with the use of such nomograms to relate two numeric values with the understanding that the uncertainty in this measurement is the same as the uncertainty in the marker concentration because individual sample measurements are referenced, not population averages.

[0135] In some embodiments of the presently-disclosed subject matter, multiple determination of one or more diagnostic or prognostic biomarkers can be made, and a temporal change in the biomarker can be used to monitor the progression of disease and / or efficacy of appropriate therapies directed against the disease. In such an embodiment for example, one might expect to see a decrease or an increase in the biomarker(s) over time during the course ofeffective therapy. Thus, the presently disclosed subject matter provides, in some embodiments, methods for determining treatment efficacy and / or progression of MASH and / or MASH-related HCC in a subject, and thus methods for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC. In some embodiments, a method for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC includes: determining an expression level or activity of one or more biomarkers associated with MASH and / or MASH- related HCC, such as for example Smac / DIABLO, ATP 1 Al, caspase-cleaved CK-18 fragment, F0X01, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, H3K9ac, or combinations thereof, in biological samples collected from the subject at a plurality of different time points; and comparing the expression level or activity of the one or more biomarkers in the samples collected at different time points to detect a measurable change in the expression level or activity of the one or more biomarkers across the samples. For example, a first time point can be selected prior to initiation of a treatment and a second time point can be selected at some time after initiation of the treatment. In some embodiments, the one or more biomarkers is a combination of biomarkers including two or more biomarkers selected from Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, F0X01, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, H3K9ac. In some embodiments, the biomarkers utilized in the method includes at least one biomarker selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac. In some embodiments, measurable changes between the expression level or activity of the at least one biomarker relative to the control expression level or activity of the at least one biomarker may correspond to some or all of the increases and decreases of survivin, Smac / DIABLO, ATP 1 Al, cypase-cleaved CK-18 fragment, FOXO1, LC3-II, BMAL1, Rev-erb-alpha, GSH, H3K9me3, and H3K9ac referred to above with respect to the various embodiments of the method foridentifying metabolic dysfunction in a cell indicative of MASH-related HCC or risk of MASH- related HCC.

[0136] Combinations of biomarker levels can be measured in each of the samples taken from different time points and qualitative and / or quantitative differences noted. A change in the expression level or activity of the biomarker levels from the first and second samples can be correlated with determining treatment efficacy and / or progression of the disease in the subject. In some embodiments, the subject from which the biological samples are obtained from is diagnosed with MASH-related HCC. In some embodiments, the prophylaxis or treatment for which the determination to initiate or continue includes administering an antagonist of Na / K ATPase / Src receptor complex to the subject, such as that disclosed in U.S. Patent No.11,958,915, which again is incorporated herein by reference in its entirety.

[0137] In some embodiments, the method for determining whether to initiate or continue prophylaxis or treatment of MASH-related HCC in a subject further includes the steps of detecting one or more metabolites including a lipid, a carbohydrate, a peptide, or a combination thereof in the series of biological samples; and detecting a measurable difference in the amount of the one or more metabolites in the series of biological samples. In such embodiments, the determination as to whether to initiate or continue prophylaxis or treatment of MASH-related HCC in the subject may be further based on the detected measurable difference between the amount of the one or more metabolites in the series of biological samples. In some embodiments, the one or more metabolites are selected from the lipids, carbohydrates, and peptides disclosed in Table 2 below. In some embodiments, the one or more metabolites includes oxidized glutathione, 3-ureidopropionic acid, lysine, tyrosine, zeatin, butyric acid, benzoic acid, citric acid, glucose, homovanillic acid, indole-3 -pyruvic acid, methyl beta-d galactoside, citramalicacid, malic acid, arachidonic acid, corticosterone, heptadecanoic acid, lignoceric acid, nervonic acid, oleic acid, palmitic acid, palmitoleic acid, or combinations thereof.

[0138] The terms “correlated” and "correlating," as used herein in reference to the use of diagnostic and prognostic biomarkers, refers to comparing the presence or quantity of the biomarker in a subject to its presence or quantity in subjects known to suffer from, or known to be at risk of, a given condition (e.g., MASH and / or MASH-related HCC); or in subjects known to be free of a given condition, i.e. "normal individuals.” For example, a biomarker level in a biological sample can be compared to a level known to be associated with MASH and / or MASH-related HCC. The sample's biomarker level is said to have been correlated with a diagnosis; that is, the skilled artisan can use the biomarker level to determine whether the subject suffers from or is experiencing MASH and / or MASH-related HCC, and respond accordingly. Alternatively, the sample's biomarker level can be compared to a control marker level known to be associated with a good outcome (e.g., the absence of MASH and / or MASH-related HCC), such as an average level found in a population of normal subjects.

[0139] In certain embodiments, a diagnostic or prognostic biomarker is correlated to a condition or disease by merely its presence or absence. In other embodiments, a threshold level of a diagnostic or prognostic biomarker can be established, and the level of the indicator in a subject sample can simply be compared to the threshold level.

[0140] As noted, in some embodiments, multiple determinations of one or more diagnostic or prognostic biomarkers can be made, and a temporal change in the marker can be used to determine a diagnosis or prognosis. For example, a diagnostic marker can be determined at an initial time, and again at a second time. In such embodiments, an increase in the marker from the initial time to the second time can be diagnostic of MASH and / or MASH-related HCC,or a given prognosis. Likewise, a decrease in the marker from the initial time to the second time can be indicative of MASH and / or MASH-related HCC, or a given prognosis. Furthermore, in some embodiments, the degree of change of one or more markers can be related to the severity of MASH and / or MASH-related HCC and future adverse events.

[0141] The skilled artisan will also understand that, while in certain embodiments comparative measurements can be made of the same combinations of diagnostic markers at multiple time points, one can also measure a given combination at one time point, and a second combination at a second time point, and a comparison of these marker combinations can provide diagnostic information.

[0142] With regard to the step of providing a biological sample from the subject, the term “biological sample” as used herein refers to any body fluid or tissue potentially comprising the one or more biomarkers described for use herein. In some embodiments, for example, the biological sample can be a blood sample, a serum sample, a plasma sample, or sub-fractions thereof. In some embodiments, the biological sample comprises a liver tissue sample or tumor biopsy.

[0143] Turning now to the step of identifying an expression level or activity of a biomarker or combination of biomarkers in the biological sample, various methods known to those skilled in the art can be used to identify the markers in the provided biological sample. In some embodiments, determining the amount of biomarkers in samples comprises using a RNA measuring assay to measure mRNA encoding biomarker polypeptides in the sample and / or using a protein measuring assay to measure amounts of biomarker polypeptides in the sample.

[0144] In certain embodiments, expression level or activity of biomarkers can be determined by probing for mRNA of each particular biomarker in the sample using any RNAidentification assay known to those skilled in the art. Briefly, RNA can be extracted from the sample, amplified, converted to cDNA, labeled, and allowed to hybridize with probes of a known sequence, such as known RNA hybridization probes (selective for mRNAs encoding biomarker polypeptides) immobilized on a substrate, e.g., array, or microarray, or quantitated by real time PCR (e.g., quantitative real-time PCR, such as available from Bio-Rad Laboratories, Hercules, California, U.S.A.). Because the probes to which the nucleic acid molecules of the sample are bound are known, the molecules in the sample can be identified. In this regard, DNA probes for one or more biomarkers can be immobilized on a substrate and provided for use in practicing a method in accordance with the present subject matter.

[0145] In some embodiments, detecting the expression level or activity of biomarkers in samples comprises the use of mass spectrometry and / or immunoassay devices and methods to measure polypeptides in samples, although other methods are well known to those skilled in the art as well. See, e.g., U.S. Pat. Nos. 6,143,576; 6,113,855; 6,019,944; 5,985,579; 5,947,124; 5,939,272; 5,922,615; 5,885,527; 5,851,776; 5,824,799; 5,679,526; 5,525,524; and 5,480,792, each of which is hereby incorporated by reference in its entirety. Immunoassay devices and methods can utilize labeled molecules in various sandwich, competitive, or non-competitive assay formats, to generate a signal that is related to the presence or amount of an analyte of interest. Additionally, certain methods and devices, such as biosensors and optical immunoassays, can be employed to determine the presence or amount of analytes without the need for a labeled molecule. See, e.g., U.S. Pat. Nos. 5,631,171; and 5,955,377, each of which is hereby incorporated by reference in its entirety.

[0146] Thus, in certain embodiments of the presently-disclosed subject matter, peptides corresponding to some or all of the biomarkers are analyzed using an immunoassay. Thepresence or amount of some or all of the biomarkers utilized in the various embodiments disclosed herein can be determined using antibodies or fragments thereof specific for each marker and detecting specific binding. For example, in some embodiments, one antibody specifically binds survivin, which is inclusive of antibodies that bind the full-length peptide or a fragment thereof. In some embodiments, the antibody is a monoclonal antibody, such as an anti- survivin monoclonal antibody. In other embodiments, the antibody is a polyclonal antibody.

[0147] Any suitable immunoassay can be utilized, for example, enzyme-linked immunoassays (ELISA), radioimmunoassays (RIAs), competitive binding assays, and the like. Specific immunological binding of the antibody to the marker can be detected directly or indirectly. Direct labels include fluorescent or luminescent tags, metals, dyes, radionuclides, and the like, attached to the antibody. Indirect labels include various enzymes well known in the art, such as alkaline phosphatase, horseradish peroxidase and the like.

[0148] The use of immobilized antibodies or fragments thereof specific for the markers is also contemplated by the presently-disclosed subject matter. The antibodies can be immobilized onto a variety of solid supports, such as magnetic or chromatographic matrix particles, the surface of an assay plate (such as microtiter wells), pieces of a solid substrate material (such as plastic, nylon, paper), and the like. An assay strip can be prepared by coating the antibody or a plurality of antibodies in an array on a solid support. This strip can then be dipped into the test biological sample and then processed quickly through washes and detection steps to generate a measurable signal, such as for example a colored spot.

[0149] In some embodiments, mass spectrometry (MS) analysis can be used alone or in combination with other methods (e.g., immunoassays) to determine the presence and / or quantity of the one or more biomarkers of interest in a biological sample. In some embodiments, the MSanalysis comprises matrix-assisted laser desorption / ionization (MALDI) time-of-flight (TOF) MS analysis, such as for example direct-spot MALDI-TOF or liquid chromatography MALDL TOF mass spectrometry analysis. In some embodiments, the MS analysis comprises electrospray ionization (ESI) MS, such as for example liquid chromatography (LC) ESI-MS. Mass analysis can be accomplished using commercially-available spectrometers, such as for example triple quadrupole mass spectrometers. Methods for utilizing MS analysis, including MALDI-TOF MS and ESI-MS, to detect the presence and quantity of biomarker peptides in biological samples are known in the art. See for example U.S. Patents 6,925,389; 6,989,100; and 6,890,763 for further guidance, each of which is incorporated herein by this reference. In some embodiments, MS analysis comprises liquid chromatography-mass spectrometry (LC-MS). In some embodiments, LC-MS may be utilized to detect the amount of one or more metabolites in the various methods disclosed herein.[00150J With further respect to the measurement of the biomarkers described herein, in some embodiments, one or more of the biomarkers utilized in the various methods disclosed herein may be detected in the sample using a method selected from the group consisting of ELISA, Luminex, FACs, Western blot, dot blot, immunoprecipitation, immunohistochemistry, immunocytochemistry, immunofluorescence, immunodetection methods, optical spectroscopy, radioimmunoassay, mass spectrometry, HPLC, qPCR, RT-qPCR, multiplex qPCR, SAGE, RNA-seq, microarray analysis, FISH, MassARRAY technique, and combinations thereof.

[0151] In some embodiments, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay may be utilized to measure fragmentation (e.g., caspase-cleaved CK-18 fragments) associated with cell death.

[0152] Although certain embodiments of the methods only call for a qualitative assessment of the presence or absence of the combination of markers in the biological sample, other embodiments of the method call for a quantitative assessment of the amount of each of the markers in the biological sample. Such quantitative assessments can be made, for example, using one of the above mentioned methods, as will be understood by those skilled in the art.

[0153] In certain embodiments of the various methods disclosed herein, a subject is identified having MASH and / or MASH-related HCC upon identifying one or more biomarkers in a biological sample obtained from the subject. In other embodiments of the method, the identification of such one or more biomarkers in a biological sample obtained from the subject results in the subject being identified as having a risk of MASH and / or MASH-related HCC.

[0154] In certain embodiments of the various methods disclosed herein, it can be desirable to include a control sample that is analyzed concurrently with the biological sample, such that the results obtained from the biological sample can be compared to the results obtained from the control sample. Additionally, it is contemplated that standard curves can be provided, with which assay results for the biological sample can be compared. Such standard curves present levels of biomarkers as a function of assay units, i.e., fluorescent signal intensity, if a fluorescent signal is used. Using samples taken from multiple donors, standard curves can be provided for control levels of the one or more markers in normal tissue.

[0155] The analysis of markers can be carried out separately or simultaneously with additional markers within one test sample. For example, several markers can be combined into one test for efficient processing of multiple samples and for potentially providing greater diagnostic and / or prognostic accuracy. In addition, one skilled in the art would recognize the value of testing multiple samples (for example, at successive time points) from the same subject.Such testing of serial samples can allow the identification of changes in marker levels over time. Increases or decreases in marker levels, as well as the absence of change in marker levels, can provide useful information about the disease status that includes, but is not limited to, identifying the approximate time from onset of the event, the presence and amount of salvageable tissue, the appropriateness of drug therapies, the effectiveness of various therapies, and identification of the subject's outcome, including risk of future events.

[0156] The analysis of markers can be carried out in a variety of physical formats as well. For example, the use of microtiter plates or automation can be used to facilitate the processing of large numbers of test samples. Alternatively, single sample formats could be developed to facilitate immediate treatment and diagnosis in a timely fashion, for example, in ambulatory transport or emergency room settings.

[0157] As mentioned above, depending on the embodiment of the method, identification of the one or more markers can be a qualitative determination of the presence or absence of the markers, or it can be a quantitative determination of the concentration of the markers. In this regard, in some embodiments, the step of identifying the subject as having MASH-related HCC or a risk thereof requires that certain threshold measurements are made, i.e., the levels of the one or more markers in the biological sample exceed control levels. In certain embodiments of the method, the control level is any detectable level of the marker(s). In other embodiments of the method where a control sample is tested concurrently with the biological sample, the control level is the level of detection in the control sample. In other embodiments of the method, the control level is based upon and / or identified by a standard curve. In other embodiments of the method, the control level is a specifically identified concentration, or concentration range. As such, the control level can be chosen, within acceptable limits that will be apparent to thoseskilled in the art, based in part on the embodiment of the method being practiced and the desired specificity, etc.

[0158] In some embodiments of the presently-disclosed subject matter, a system, kit, or assay for diagnosing MASH and / or MASH-related HCC in a subject is provided, or a system, kit, or assay for determining whether to initiate or continue prophylaxis or treatment of MASH and / or MASH-related HCC in a subject is provided. Such systems, kits, or assays can be provided, for example, as commercial kits that can be used to test a biological sample, or series of biological samples, from a subject. For instance, in one embodiment, an assay for screening for metabolic dysfunction-associated steatohepatitis (MASH) and / or MASH-related hepatocellular carcinoma (HCC) in a subject, includes: applying one or more agents capable of detecting an expression level or activity of a combination of biomarkers including two or more biomarkers selected from survivin, second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), Na+ / K+ transporting subunit alpha 1 (ATP 1 Al), caspase-cleaved cytokeratin-18 (CK-18) fragment, forkhead box protein 01 (FOXO1), microtubule-associated protein 1 light chain 3-II (LC3-II), brain and muscle arnt-like protein- 1 (BMAL1), Rev-erb-alpha, glutathione (GSH), histone 3 lysine 9 trimethylation (H3K9me3), and histone 3 lysine 9 acetylation (H3K9ac); and detecting the expression level or activity of the combination of biomarkers in the biological sample. The system can also include certain samples for use as controls. The system can further include one or more standard curves providing levels of markers as a function of assay units.

[0159] In some embodiments, a system for the analysis of biomarkers is provided that comprises antibodies having specificity for one or more markers associated with MASH and / or MASH-related HCC. Such a system can comprise devices and reagents for the analysis of atleast one test sample. The system can further comprise instructions for using the system and conducting the analysis. Optionally the systems can contain one or more reagents or devices for converting a marker level to a diagnosis or prognosis of the subject.

[0160] The present methods can be used on a wide variety of subjects. Indeed, the term “subject” as used herein is not particularly limited. The term “subject” is inclusive of vertebrates, such as mammals, and the term “subject” can include human and veterinary subjects. Thus, the subject of the herein disclosed methods can be a human, non-human primate, horse, pig, rabbit, dog, sheep, goat, cow, cat, guinea pig, rodent, or the like. The term does not denote a particular age or sex. Thus, adult and newborn subjects, as well as fetuses, whether male or female, are intended to be covered.

[0161] The practice of the presently-disclosed subject matter can employ, unless otherwise indicated, conventional techniques of cell biology, cell culture, molecular biology, transgenic biology, microbiology, recombinant DNA, and immunology, which are within the skill of the art. Such techniques are explained fully in the literature. See e.g., Molecular Cloning A Laboratory Manual (1989), 2nd Ed., ed. by Sambrook, Fritsch and Maniatis, eds., Cold Spring Harbor Laboratory Press, Chapters 16 and 17; U.S. Pat. No. 4,683,195; DNA Cloning, Volumes I and II, Glover, ed., 1985; Oligonucleotide Synthesis, M. J. Gait, ed., 1984; Nucleic Acid Hybridization, D. Hames & S. J. Higgins, eds., 1984; Transcription and Translation, B. D. Hames & S. J. Higgins, eds., 1984; Culture Of Animal Cells, R. I. Freshney, Alan R. Liss, Inc., 1987; Immobilized Cells And Enzymes, IRL Press, 1986; Perbal (1984), A Practical Guide To Molecular Cloning; See Methods In Enzymology (Academic Press, Inc., N.Y.); Gene Transfer Vectors For Mammalian Cells, J. H. Miller and M. P. Calos, eds., Cold Spring Harbor Laboratory, 1987; Methods In Enzymology, Vols. 154 and 155, Wu et al., eds., Academic PressInc., N.Y.; Immunochemical Methods In Cell And Molecular Biology (Mayer and Walker, eds., Academic Press, London, 1987; Handbook Of Experimental Immunology, Volumes I-IV, D. M. Weir and C. C. Blackwell, eds., 1986.

[0162] The presently-disclosed subject matter is further illustrated by the following specific but non-limiting examples. Some of the following examples may include aspects that are prophetic, notwithstanding the numerical values, results and / or data referred to and contained in the examples. Further, the following examples may include compilations of data that are representative of data gathered at various times during the course of development and experimentation related to the present invention.EXAMPLES

[0163] Materials and Methods

[0164] DeterminingHalidating Plasma Liver Signature for MASH Progression for Early Detection of HCC

[0165] Developing a plasma signature that predicts early detection of HCC in high-risk populations will improve subject overall survival. Also, markers could provide signals of treatment response, residual disease, and tumor recurrence.

[0166] Human Model. For the screening of early-stage HCC detection amenable to cure options, a test should have high sensitivity (>80%), but maintain specificity, to provide a low rate of false positives. The present screening practice consists of imaging with ultrasound every 6 months, modality that yields a specificity of 50% alone, or 70% when combined with alphafetoprotein (AFP). Nevertheless, this modality may not fit the needs of all patients. Therefore, a risk stratified, and step-up screening approach has been proposed to test high risk population forHCC development. A phase two biological marker testing will be completed according to the National Cancer Institute (NCI) biomarkers development and validation guidelines. Clinical variables as obesity (BMl>30), systolic HTN (>150mm of Hg), insulin resistance (DM type 2, glucose>126mg / dL), AST, ALT, INR, bilirubin will be added to the scoring system (clinical domain) to plasma biological markers and metabolites with known markers (AFP domain) and imaging modalities (US / CT scan / MRI, imaging domain) in humans. Novel plasma biological marker domains include cell clock (brain and muscle amt-like protein- 1 (BMAL1) and Rev-erb- alpha), apoptotic (cytokeratin 18 fragments), cell autophagy activities (microtubule-associated protein 1 light chain 3-II (LC3-II)), cell oxi-redox (glutathione sp., protein carbonylation), cell survival (survivin, second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), and Na+ / K+ transporting subunit alpha 1 (ATP1A1)), and metabolics domain (lactate glycerol).[00167J Modeling for prediction of MASH, ESLD and ESLD-HCC will be performed to develop signatures for MASH progression and early HCC detection in mice models and human repositories. Performance metrics for testing biomarkers will include AUROC curve, explained variation (R2), C-index (concordance index), and Bayesian information discrimination / goodness of fit, which are important statistics for evaluating model accuracy and predictive power. The AUROC and C-index provide measures of a model's ability to correctly classify or rank individuals, while explained variation (R2) indicates how well the biomarkers account for differences in predicted probabilities. A low Bayesian Information Criterion suggests a balance between accuracy and parsimony in the model. Plasma prints will be correlated with liver known morphological changes in animals, and when possible, in humans under institutional review board (IRB) approved protocols. Our modeling will be compared to AFP and GALAD score interms of positive and negative predictive values. Human plasma will be obtained from consented patients or from human repositories. Processing protocols have been previously validated. Child- Pugh (graded encephalopathy and ascites, albumin, bilirubin, INR), MELD-Na (bilirubin, creatinine, INR, sodium), and FIB-4 (age, AST, ALT, platelets) scores will also be recorded.

[0168] Plasma Metabolites. Primary and secondary antibodies used in ELISA, western blotting, and immunostaining are displayed in Table 1. Pre-selected non-targeted serum metabolites (carbohydrates, amino acids, and lipids, n=81 metabolites, see Table 2) will be determined by liquid chromatography-tandem mass spectrometry (LC-MS / MS) at a metabolomics core facility.

[0169] Antibody Portfolio. Primary and secondary antibodies used in ELISA, Western blotting, and immunostaining are displayed on Table 1. pNaKtide is a cell-permeable peptide designed by mapping one of the sites of the NKA al / Src interaction (pNaKtide, HD Biotech., CA). The Na / K-ATPase interacts with Src through two binding motifs, namely the CO2 of the al-subunit-with-the Src SH2 domain, and the third cytosolic domain (CD3) of the al subunit- with-the Src NH2 domain. It has been established that the CD3-Src kinase binding keeps Src in its wild type (inactive) whereas in pathological states as when ouabain binds to the Na / K- ATPase, this binding is broken activating different signaling pathways including ERK cascades, PLC / PKC pathway and mitochondrial production of reactive oxygen intermediates (ROI). In addition, a “scramble” pNaKtide (TAT-scrambled peptide:GRKKRRQRRRPPQACWIQNLSRSAGATVRLFLA, with same number of amino acids but differs in amino acid sequence, pNaKtide:GRKKRRQRRRPPQSATWLALSRIAGLCNRAVFQ, with no biological effect) is run as a negative control.Table 1. Antibody and key reagents used in ELISA, western Blotting, and Immuno-confocal methods.

[0170] Demonstrating A TP 1 A l is Critical in MASH Progression By Modulating Cell CLOCK Proteins

[0171] BMAL1 and Rev-erba (Rev-erb -alpha) expression, their metabolic architects PPARy & PGCla, and cell autophagy (LC3-II) will be tested in human normal and HCC cell lines, and in MASH mice models under normal, and under down-regulated al -expression (Cre- liver specific al / Knock-out mice).

[0172] Cell Models. The hypothesis that growing uncoupled metabolism during MASH progression is driven by active ATP 1 Al signaling that promotes disturbances on cellular clock processes which further aggravate metabolic cycles will be tested under treated vs. untreated conditions, in human HCC cell lines and human normal hepatocytes for response effects under decrease or enhance al -subunit expression. Cell lines are mostly used for descriptive studies, including the metabolic effects from mitochondrial challenges on the Seahorse chamber. Normal human hepatocytes will serve as controls, and normal cells will be transfected to obtain a ATP1 Al knock-down to observe both the metabolic response and its effects of protein expressions. To fulfill Koch’s postulates (cause and effect association), ATP1A1 knock-down normal human hepatocytes transfected with NaKtide, will be tested (we predict normalization ofmetabolic effects and protein expressions). Finally, the effects of protein expression and its metabolic consequences will be further dissected by using antineoplastic agents and selective inhibitors of target pathways and determine their effects solidifying our working hypothesis.

[0173] Cell lines. A normal human hepatocyte cell line (IRM, LifeNet Health, VA) and human HCC cell lines (Hep3B and SNU 475, ATTC, Cambridge MA) will be cultured under provider recommended conditions until confluence is achieved. Human cells will be checked regularly for Mycoplasma contamination (RT-PCR kit, MycoAlert PLUS, MD) while growing in culture with high glucose DMEM media supplemented with 10% FBS and 1% Penicillin / Streptomycin (Hep3B), or with RPMI 1640 media supplemented with 10% heat inactivated FBS and 1% Penicillin / Streptomycin (SNU475) in 37°C humidified incubator in presence of CO2 at 5%. After achieving confluence, treated and untreated human cell lines will be serum-starved for at least 12 hours to achieve cell cycle synchronization and then trypsinized.

[0174] Cells Specific al (alphal) knock-down (KDO). Human cell lines will be transiently transfected with siRNA al-specific polypeptide (Sense: 5'- tcgagggtcgtcdtgatctttgatattcaagagatatcaaagatcagacgaccttttt-3'; anti-sense: 5'-ctagaaa aaggtcgtctgatctttgatatctcttgaatatcaaagatcagacgaccc-3') as previously described to generate a ATP1A1 KDO, and cultured as above detailed. Cell lines will be exposed to pNaKtide, and its metabolic effects will be assessed using the Seahorse flux analyzer. In addition to l -subunit knock-down (siRNA), cells will be rescued by transfection to over-express al -subunit (Lenti- Alb-NaKtide)

[0175] Cell Lent-Alb-NaKtide Transfection. Hepatocyte-specific lentiviral vectors expressing either eGFP or eGFP-NaKtide driven by the Albumin promoter have been constructed (Cyagen Biosciences, CA). As a control, specific scrambled NaKtide (SGLT2-scrambled NaKtide) will also be used in one subset. Lentivirus efficacy and specificity gene targeting cell lines, and in vivo in liver tissues will be analyzed by immunofluorescence staining to show GFP and NaKtide expression (using NaKtide antibody previously developed). Once cell lines have achieved 80% confluence, cell media will be exchanged to transfect cells with 50, 100, 150, and 200 MOI (multiplicity of infection) using 0.1% Polybrene. The percentage of successfully transfected cells will be documented at 48 hours.

[0176] MTT Cell Proliferation. MTT cell proliferation assay will be performed by plating 5000 cells / well in 6 wells (per condition) of 96 well plates and allowed to adhere (24 hours), when media will be replaced with media± treatment agent for another 24 hours to finally assess cell proliferation according to the manufacturer's instructions (MTT assay, ATCC®, Cambridge, MA). Furthermore, the effects of kinase inhibitors on PI3K will be assesed; Wortmannin (Selleckchem, TX) is a potent and selective inhibitor of PI3K protein with antiinflammatory and immune-suppressant effects in vivo. AG490 (Tocris, MN), is a selective inhibitor of the Janus kinase 2 (JAK2) which is a signal transducer and activator of transcription 3 (STAT3) signaling pathway. On ATP 1 Al, digoxin will be dissolved in serum free media before addition to cells at designated concentrations. Digoxin (Sigma-Aldrich, MO) is a specific inhibitor of Na / K-ATPase through the al-subunit, and it is the only FDA-approved cardiac glycoside for the treatment of mild or moderate heart failure. Other compounds to be used in cell culture and their effects include Sorafenib (Bayer AG, Leverkusen, Germany), which is an oral multi-kinase inhibitor approved by the FDA for clinical use in advanced HCC stage. It has been recognized since 2007 as a treatment option for patients with advanced unresectable HCC. Doxorubicin (Pfizer, NY) is an anthracycline and one of the most commonly used agents in trans-arterial chemo-embolization (TACE) procedure for HCC patients at the intermediate stage.PP2 (Sigma- Aldrich, MO) is a selective potent inhibitor of Src family kinases. It inhibits Lek and FynT but only weakly inhibits ZAP-70 and JZK2. Chloroquine and rapamycin will be used as cell inducers of autophagy (positive controls). They are provided by the Abeam kit (9b 139454, MA).

[0177] Evaluation on Culture Cells. Reduced glutathione (GSH) and LC3-II will be measured by ELISA, and the apoptotic activity will be assessed by the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay. Total Src (Src + pSrc), P13K, Akt, mTOR and STAT3 activity will be assessed by immune-blotting (see Table 1). Hepatocyte ATP1A1, pSrc, SMAC, and survivin assessment will be performed by confocal morphometry providing cell gee-spatial quantitation to be confirmed by immune-blotting, or ELISA. Autophagy activity will be judged by confocal microscopy using a commercial kit (Abeam, MA). Confirmatory pathways on signaling for gene activation / inhibition under different conditions will be provided by RNA sequencing (Novogene, CA).

[0178] Seahorse Flux Analyzer. Cells will be plated in Seahorse XFp cell culture plates and subjected to both the mitochondrial stress 439 test (Agilent Technologies, Cat# 103010-100) and the glycolytic stress test (Agilent Technologies, Cedar 440 Creek, TX, US, Cat #103017- 100) supply by the manufacturer, with the injection of oligomycin, 441 trifluorom ethoxy carbonylcyanidephenylhydrazone (FCCP), rotenone and antimycin A, glucose, and 2- 442 deoxyglucose (2-DG), as follows: for mitochondrial stress test injection 1 (20pl of lOpM Oligomycin- LOpM); injection 2 (22 pl of lOpM FCCP-l.OpM); and injection 3 (25 pl of 5pM Rotenone / Antimycin A-0.5pM). Glycolytic stress test will be performed as follows: for glycolytic stress test injection 1 (20pl of lOOmM Glucose- lOmM); injection 2 (22pl of lOpM Oligomycin- LOpM); and injection 3 (25pl of 500mM 2-DG-50mM). Three measurements ofoxygen consumption rate and extracellular acidification rates will be taken at baseline and after each injection. The appropriate concentration of FCCP will be determined by FCCP titration as recommended by the manufacturer, and all experiments will be performed with determined concentration of FCCP.

[0179] SCID Mice. To circumvent the lack of microenvironment on human HCC cell line studies, human HCC cell line (Hep3BxlO6 cells) will be implanted subcutaneously into the flank / back of the Nod SCID=NOD mice (model lacking an immunogenic response). The NOD (CB17-Prkdcscid / J), is an immune-deficient mouse model lacking functional T and B cells. This characteristic makes this model suitable for xenogeneic tumor-graft experiments. In addition, although some models may spontaneously develop partial immune reactivity (i.e., leakiness), this model is low on the leakiness scale, making it a suitable fit for survival studies involving malignant xenograft cells. As previously described, cells will be transfected with dual-luciferase (firefly luciferase and fluorescent-mCherry) plasmid vectors using the Lentivirus, to confer luminescence to the tumor for in vivo imaging using the IVIS system. Tumor response to systemic (IP) NSO.9% vs. pNaKtide (30mg / Kg / week, in 3 doses) will be monitored weekly by video. Animals will be euthanized at eight weeks, or when the tumor reaches 1,500mm3(avoiding animal suffering), for tissue / plasma collection to determine tumor burden, epigenetic changes, apoptotic activity, and protein levels.

[0180] Animal Models. To circumvent metabolic confounding factors due to well established propensity of male mice to develop malignancy in the MASH model, females will be used exclusively for the MASH model while males will be used exclusively for the MASH-HCC model. Our hepatocyte specific KO mouse model is operational in both sexes. It is aimed to determine the response from liver ATP1A1 signaling in liver tissue i) on survivin expression,epigenetic changes, and autophagy / apoptotic activities and their correlation with cell clock protein expression, metabolic disturbances, and hepatic morphology, and ii) to determine their correlation with respective protein levels in plasma, for the prediction of liver cells evolution. Studies will be conducted in our WT animals (wild type C57BL / 6J mice) and in our rodent models (i) the MASH model, (ii) the MASH-HCC model, (iii) the liver specific ATP1A1 KO mice (Cre-Lox approach), (iv) the enhanced liver al -subunit expression by transfection with our albumin promoter Lenti-virus vector NaKtide, and (v) the ATP1 Al KO mice rescued by the Lenti-Alb-NaKtide transfection. We are conscious of the limitations our models may have, mainly to detect critical times for the very specific chain of events that occur during MASH progression and the nest of malignancy. Control groups (NMcneg and HFDP05) and intervention (pNaKtide, exercise, or transfection) groups will follow animal protocols approved by an institutional animal care and use committee (IACCU). Animals will be sacrificed at the end of the study period for organs and blood procurement.

[0181] MASH Model. Seven-week-old C57BL / 6J female mice (Jackson Laboratory, Farmington, CT) will be housed following a 12h: 12h light-dark cycle under a temperature and humidity-controlled environment. Following acclimation, mice will be fed with a standard mouse chow (NMC, Bio-Serv, NJ) or a Western diet consisting of a high-fat diet (HFD, Bio- Serv, NJ, 60% of calories from fat) complemented with 55% fructose-in-water ad libitum for 12 weeks. Mice develop MASH after 12 weeks with no visible tumors. The study design for this MASH model includes randomization of the mice at 12 Weeks into treated (pNaKtide lOmg / kg of BW, exercise, or NaKtide transfection) with study time periods at 16, 20 and 24 weeks.

[0182] MASH-HCC Mouse Model. (STAMTM mice, in collaboration with SM Labs,Tokyo, Japan) is generated by injecting Streptozotocin (200pg STZ, Sigma, MO) to neonatalC57BL / 6J male mice 2 days after birth, and after 4weeks of age injected animals will be exposed to a high-fat diet ad libitum. The mice develop MASH at 12 weeks with the consistent presence of HCC by 16-20 weeks of age. The study design for this MASH-HCC model includes randomization of the mice at 12 weeks (early HCC) or 16 weeks (late HCC) into treated with pNaKtide (at low dose= 5mg / Kg BW, or high dose= 15mg / kg of BW), or NaKtide transfection with study conclusion at 24 weeks (early HCC) and 28 weeks (late HCC).

[0183] Enhanced a-subunit (alpha-subunit) Expression Mice. It is proposed to explore the likely protective metabolic effects of enhanced alpha- 1 expression by hepatocyte transfection with already developed albumin promoter lentivirus-vector- NaKtide (22aa). Cell Transfection of isolates (2-5x106 cells) will be Lenti-NaKtide transfected to assess protein expression and how it effects in-vitro after stressor exposure. One week after mice are born, C57BL / 6J mice will be injected through the tail vein with lentivirus (lOOpl, 2xlO9TU / ml in NSO.9%) containing NaKtide or its counterpart GFP, where the albumin promoter drives expression. Similar treatment will be provided two weeks later (lOOpl, lxlO9TU / ml in NSO.9%). Animals from the MASH model and from the MASH-HCC model will be transfected at OW (no MASH, preventive of MASH development) and at 12 weeks (MASH established, prevention of HCC development). Preliminary results on mice exposed to HFD for 12 weeks showed that transfected mice had a significantly decreased lipid deposition, and a paucity of liver inflammation.

[0184] Suppressed ATP a-subunit Expression Mice. The effect of ATP 1 Al normalization on metabolism, signaling pathways, and morphologic domains will be performed in vivo under hepatocyte-specific suppressed al -subunit conditions using the Floxed mice ATP1A1 (which encodes the al isoform). This conditional Atplal knockout mouse is designed to have exon 15-18 excised when crossed with a Cre mouse. To achieve specific hepaticdeletion, floxed Atplal mice were crossed with Albumin promoter-driven Cre transgenic mice (Alb-Cre, Stock No: 003574, Jackson Laboratory). The feasibility of making cell-specific al KO and rescue has been successfully demonstrated (FIG. 13). The established male / female floxed ATP1A1 and transgenic line is physically healthy and overall normal at born. We have also demonstrated the effectiveness and feasibility of this approach as well in the skeletal muscle and renal proximal tubules of the mouse. (85, 89) Therefore, we believe that the proposed studies are supported by solid preliminary studies. We expect at least 90% reduction in the expression of ATP1 Al based on our prior KO models. Study design: For the ATP1 Al KO mice five groups will be tested: (i) wild type (WT=RPTal+ / +); (ii) floxed homozygous (Atplal KO, FM=RPTal- / -) mice; (iii) floxed heterozygous (Atplal KO, FM=RPTal+ / -) mice; (iv) mutant rescue mice (MR= RPT al -+ / -+), and (v) transfected mice (TM= RPT al++ / ++). C57BL / 6J mice will be randomized to NMC or HFD enriched with fructose for 10 weeks as describe (mice models), when the study will be concluded.

[0185] Distribution and Degradation Studies are also conducted in the wild type mice. Studies in wild type animals on pNaKtide distribution and degradation are performed in the mouse under an equal female: male ratio. pNaKtide in solution on Normal Saline (NS) is administered IP with escalating doses of lOmg —> 20mg —> 30mg (every 2 weeks) / kg BW three times per week (FIG. 8). In a second group of animals, pNaKtide is given at a dose of lOmg (for 2 weeks) 30mg (for 2 weeks) / Kg BW per week. At the conclusion of the study, tissue homogenates (heart, brain, skeletal muscle, liver, kidney, large / small intestines, and fat) as well as body fluids (plasma and urine) are tested for pNaKtide levels using three different methods: ELISA assay, HPLC-MS / MS, and immuno-bloating. pNaKtide volume distribution and degradation curves are made from another set of mice exposed to our construct at increasingconcentrations (10, 30, and 60mg / Kg BW) by IP injection at 0.5, 1, 4, 24, and 48h time points post injection (FIG. 8 and FIG. 6).

[0186] For the distribution and degradation studies in the MASH and MASH-related HCC models, after 12 weeks, mice are randomized into control and treatment groups (n =10 per group) and the study continued for an additional 12 to 16 weeks as follows: 1) HFD with no treatment (2 sub-groups, NSO.9%, and ‘scramble’ pNaKtide), 2) HFD treated with pNaKtide. While the MASH animals will be treated at a fixed dose of pNaKtide (25 mg / kg TBW dissolved in lOOpl of 0.9% Normal Saline (NS, IP once a week), the MASH-HCC rodents will be provided with pNaKtide at a low dose (2mg / kg TBWX3 a week), or high dose (lOmg / Kg BWX3 a week). All mice will be fed HFD ad libitum throughout the experimental period that will last 24 weeks for the MASH and MASH related HCC. Animals are euthanized at the end of the study period for organ harvesting (including liver, heart, muscle, kidney, small bowel, brain, and fat), and blood collection. Organs are washed with 0.9% NS at room temperature and sharply divided before being snap-frozen in liquid nitrogen to be stored at -80°C or fixed at 4°C (formaldehyde at 10%). Animals’ care will be provided following guidelines of a university IACUC under approved protocols.

[0187] To evaluate the results, at day 0 and a week before study conclusion, animals under general anesthesia will have i) MRI to determine total body weight (TBW), body water content, lean body mass, and body fat mass (EchoMRI-lOOH Body Composition Analyzer, Houston, TX); ii) an echocardiogram (Vivo 1100 US System, Visual Sonics, Toronto, ON, Canada). Animal heart rate (HR per minute), systolic and diastolic blood pressure (d / s BP in mm of Hg), left ventricular ejection fraction (LVEF as % of total LV capacity, FIG. 7), cardiac index (cardiac in 1 / min output over body surface area in m2); iii) skeletal functional performance bystandardized exercise protocol (30 minutes on an endless rotating wheel motivated by electroshock, five times / week), and iv) histological evaluation by organs hematoxylin-eosin (H- E) evaluation among groups.

[0188] The studies proposed herein are well supported by the preliminary data and feasibility described herein and reflected in the drawings. It is expected that the studies will confirm that the Src null mutant rescue mice and the transfected mice will be protected against MASH and MASH-induced HCC. However, the possibility that ATP1A1 dysregulation in nonhepatocytes could be involved is also considered. In this regard, an additional hepatic and extra hepatic cell-specific lentiviral and Cre / Lox protocols (Cre inducible expression at different time points) to investigate additional cross-talks between cell types can be implemented.

[0189] Determining the Sequence of Cell Events in MASH Progression

[0190] Cell redox status (GSH), epigenetic modifications (H3K9me3 & H3K9ac), protein expressions from cell clock processes (BMAL1, Rev-erb-alpha, PPARy, PGCla, LC3- II), cell division (Survivin & SMAC), apoptotic activity (CK-18 fragments), and cell metabolic prints, will be determined weekly in Cre-liver specific al / KO mice (where livers with <10% ATP1 Al expression developed MASH with progression to HCC within 7-10 weeks, FIGS. 14A-14B). These findings are of paramount importance because they not only validate prior in- vitro and in-vivo results in preclinical models, but it is the first unequivocal evidence that ATP1 Al signalosome is mechanistically involved in the metabolic disturbances that drives MASH progression to malignancy. To further explore the sequence of events in MASH progression, it is proposed to study signaling ramifications for the cell clock (mitochondrial 13- lipid oxidation and TCA cycle modulators), and for cell autophagy and apoptotic activities, todetermine mechanistically the apoptotic switch that concludes into uncontrolled cell division (survivin).

[0191] Statistical Analyses of End Points

[0192] Biological variability will be minimized by rigorous adherence to protocols, achieving degrees of freedom required for statistical analyses. Considering cell lines studies, where very low to no attrition is expected, but some degree of biological variability, it is planned to have a minimum of n=7-9 observations per study to lower any type 1 or 2 errors. Preliminary results from in-vivo studies on liver samples showed a mean difference on apoptotic and autophagy activities, treated vs. untreated groups of 80% (60-90 range). Thus, at 8=80 aiming a power (P) of 0.8 and a significance (a) at 0.05, the number of observations required is >4. If 8 is changed to 60 or 40% (30 to 60, range) as observed on survivin and ATP 1 Al expressions, the number of observations required are >5 and 7, respectively. To overcome variability, n=10 will be aimed for at each data point. Differences among groups will be determined by parametric (i.e., ANOVA, and t-test) using GraphPad Prism version 9.0.1 (GraphPad, San Diego, CA), and non-parametric methods (Turkey's Post hoc test, Fisher exact test) using SPSS (Chicago, IL). The likelihood of less than 0.05% chance will be considered statistically significant.

[0193] Omics has merged as profiling tools to provide important insight into the biology of various cancers. For gene and metabolite expressions, a Bayesian ANOVA approach will be performed following principal component analyses methods. To detect differential expression between experimental conditions when the number of variables greatly exceeds the number of observations or samples (p » n paradigm), conventional regression techniques literally fall apart or are inappropriate at best. It is thus proposed to take advantage of the fact that the problem of differential expression can be cast into a variable selection problem in a regression setting bydoing a principal component analysis. Bayesian model selection methods were proven to be the most reasonable approaches to detect differentially expressed genes / metabolites in high dimensional datasets. Bayesian ANOVA is a parameter estimation technique that relies on the so-called 'spike and slab' Bayesian hierarchical model used in model selection. It is a special type of inferential regularization (i.e., borrowing information across variables), which builds a parsimonious model by selectively shrinking to only those coefficients of variables that truly do not enter in the model. This is an ideal property guaranteeing which genes / proteins / metabolites will enter the model and which will not, allowing for optimal balancing of the detection error rates (i.e., false positive and false negative rates, or total misclassification errors), thereby leading to a more accurate and parsimonious model of truly differentially expressed genes and metabolites. Bayesian ANOVA is superior to the conventional one-at-a-time (univariate) hypothesis testing procedures, because it will simultaneously control for false negative errors, and therefore miss less subtle changes of varying gen expressions / metabolites. This method also eliminates the problem of specifying arbitrarily False Discovery Rate (FDR) cutoff values, and the drawback of excessive conservativeness. In addition, Bayesian ANOVA does not ignore the problem of dependency between the genes or metabolites, which is always present within large biological "omics" datasets. Major difficulties in the management and statistical analyses of the data is not expected. Results will also be displayed as heat maps, volcano plots, or 3D associations (FIGS. 15C-15D).

[0194] Though the results from 'omics' output did not provide us with a mechanistic signaling, it showed from a different altitude the overall impact of interventions on disease progression in gene regulation and its metabolic modulation environment. It also enlightens us in possible check-points for disease targeting therapy. Preliminary results suggest that theconcentrations of certain metabolites and proteins in serum differ significantly across healthy versus cancerous livers. This leads us to believe that our data could be used to support the development of a noninvasive means of predicting malignancy. We aim to develop a predictive scoring system for cancer detection based on metabolomic and protein concentrations in the blood. To ensure that our model is maximally generalizable, it will use readily quantifiable and objective measures that apply to diverse patients. (Univariate analyses, including t-tests and simple regression models, will be used to assess significant association between each predictor variable and cancer to identify candidate variables for inclusion in subsequent analyses.Variables identified as significant will be used to develop a multivariate predictive model. To do this, the data will be partitioned into a training set for building the model and a validation set for assessing the model's predictive power on new data. Backward elimination will be used to select for features by systematically removing the variables least significantly associated with cancer, determined by their p-value, until a critical subset of significant predictors is obtained. The resulting model equation corresponds to our scoring system. The random assignment of data to training and validation groups can inadvertently lead to unusually positive or unusually negative predictive results. (93) Employing k-fold cross-validation, a technique that involves training a model multiple times using a different subset of the data as the validation set in each iteration, will ensure that the developed scoring system will have robust predictive power in real-world situations. A receiver operating characteristic (ROC), a curve that represents the true positive rate versus the false positive rate for each discrimination threshold, can be constructed for each fold. The area under the ROC (AUROC) curve will be used as the primary metric for predictive power. The mean AUROC represents the average estimate of the model's performance across different partitions of the data. The target mean AUROC for the final model will be set at 0.9,indicating that our scoring system is a very good classifier. As we have mentioned and in summary, performance metrics for testing biomarkers will include AUROC curve, explained variation (R2), C-index (concordance index), and Bayesian information on the discrimination / goodness of fit. These are important statistics for evaluating model accuracy and predictive power. The AUROC and C-index provide measures of a model's ability to correctly classify or rank individuals, while explained variation (R2) indicates how well the biomarkers account for differences in predicted probabilities. A low Bayesian Information Criterion suggests a balance between accuracy and parsimony in the model. Biostatistical analyses will be performed using SPSS (Chicago, IL).

[0195] Example 1: Early Detection and Diagnosis of MASH-related HCC

[0196] Hepatocellular carcinoma (HCC) is the most common type of primary liver malignancy, constituting about 90-95% of all hepatic cancers. HCC is also the fastest-growing and second leading cause of cancer- related death worldwide and has become a major and steadily increasing global health challenge mainly due to the metabolic cellular disturbances promoted by the epidemic of obesity and a paucity of biomarkers for its early detection, resulting in over 50% of HCCs being diagnosed at a late stage.

[0197] To date, alpha-fetoprotein (AFP) is the only biomarker that has been approved for diagnosis and surveillance of HCC in combination with ultrasonography and multiphasic computed tomography or magnetic resonance, in patients and high-risk communities. AFP, however, has a low sensitivity and poor specificity in detecting early HCC, thus warranting the ongoing search for better biomarkers for early detection, diagnosis and surveillance of HCC in patients and high-risk communities.

[0198] With that in mind, it was believed that proteins that play roles in the molecular pathogenesis of metabolic dysfunction-associated steatohepatitis (MASH)-related HCC could serve as putative plasma biomarkers for early detection, diagnosis and surveillance of MASH- related HCC. Those proteins included those involved in: apoptosis and autophagy processes (survivin, Smac-DIABLO, ATP1A1, caspase-cleaved cytokeratin-18 fragment, LC3-11); metabolites (Carbohydrates, Lipids and Protein derivatives); cell redox status markers (protein carbonyls and glutathione); and epigenetic modifications markers (H3K9 tri-methylation). Accordingly, a combination of the above biomarker candidates, integrated with AFP level was investigated to determine whether such a profile could be used to develop a liquid Biopsy panel (a scoring system) for effective early diagnosis, prognosis and surveillance of MASH-related HCC in high-risk population. Without wishing to be bound by any particular theory or mechanism, it was believed that the scoring system would allow for effective discrimination of the presence of HCC in livers of patients with advanced fibrosis (cirrhosis).

[0199] In view of the foregoing, experiments were thus undertaken to assess the plasma concentration of proteins and assay those proteins via Enzyme-linked immunosorbent assay (ELISA). Plasma metabolites (metabolomics) were measured using Liquid Chromatography- Mass Spectrometry (LC-MS) and apoptosis by TUNEL assay. The results are shown, in part, as box-whisker charts, in which whisker chart data is presented as median (central line), first and third quartiles (bottom and top of boxes, respectively), and whiskers (extreme values) from independent biological experiments. Differences between groups were determined by Analyses of Variance (ANOVA), followed by Turkey’s Post hoc test or t-test, Principal Component Analyses, and P<0.05 was considered statistically significant.

[0200] Apoptotic-associated Proteins

[0201] Liver Tissue Samples. Both survivin and Smac / DIABLO were differently expressed in human liver tissues of healthy control subjects, MASH subjects, HCC subjects, and subjects with liver metastases (FIGS. 1A, 4, and 10A-10C). Survivin in HCC subjects was significantly increased relative to healthy control subjects, MASH subjects, and subjects with liver metastases (p<0.01, ANOVA) (FIGS. 10A and 10C), suggesting that survivin expression in liver tissue may serve as an effective marker to discriminate between subjects with MASH or liver metastases and HCC subjects. Smac-DIABLO was significantly decreased in HCC subjects relative to healthy control subject (p<0.01, ANOVA), slightly but not statistically significantly decreased relative to MASH subjects, and significantly increased relative to subjects with liver metastases (p<0.01, ANOVA) (FIGS. 10A and 10B), respectively.

[0202] Plasma Serum Samples. A significant increase in the expression of survivin was observed in the serum samples of human HCC subjects relative to that of both human MASH and human healthy control subjects (p<0.05, ANOVA) (FIG. IB), suggesting that survivin expression in plasma may serve as an effective biomarker to discriminate between subjects with MASH and subjects with HCC. A significant increase in the expression of Smac-DIABLO in the serum samples of human MASH subjects relative to human healthy controls subjects (p<0.05, ANOVA), and a slight but not statistically relevant decrease in the expression of Smac-DIABLO in the serum samples of human MASH subjects relative to human HCC subjects was observed (FIG. 1C). Caspase-cleaved CK-18 fragment was significantly increased in the serum samples of both human MASH subjects and human HCC subjects relative to that of human healthy control subjects (p<0.05, ANOVA), suggesting that caspase-cleaved CK-18 fragment can be utilized to distinguish between healthy subjects and subjects with either MASH or HCC (FIG. ID)

[0203] As reflected above, the detection of an apoptotic to non-apoptotic switch in cells of a biological sample, as detected by variations in the expression of apoptosis inhibitor, apoptosis potentiator Smac / DIABLO, and / or apopotosis marker caspase-cleaved CK-18 fragment, may prove useful in the identification of cells indicative of MASH (and thus indicative of potential risk of future MASH-related HCC) and MASH-related HCC.

[0204] Cell Autophagy-associated Proteins

[0205] LC3-II was significantly reduced in the serum samples of both human MASH subjects (p<0.01, ANOVA) and human HCC subjects (p<0.05, ANOVA) relative to human healthy control subjects. A slight, but not statistically relevant, increase in LC3-II expression was observed in the serum samples of human HCC subjects relative to human MASH subjects (FIG.IE).

[0206] As reflected above, the detection of downregulation in cell autophagy in cells of a biological sample, as detected by variations in the expression of LC3-II or other protein markers of cell autophagy, may prove useful in the identification of cells indicative of MASH (and thus indicative of potential risk of future MASH-related HCC) and MASH-related HCC.

[0207] Oxidative Stress-associated Proteins and Oxidative Stress-related Epigenetic Changes

[0208] As shown in FIG. 1G, a significant decrease in glutathione (GSH) expression in the serum samples of human HCC subjects was observed relative to that of both human MASH (p<0.05, ANOVA) and human healthy control subjects (p<0.01, ANOVA), suggesting that GSH expression in plasma may serve as an effective biomarker to discriminate between subjects with MASH and subjects with HCC. A significant increase in the epigenetic modification of histone 3 lysine 9 trimethylation (H3K9me3) expression, which can result from increased oxidative stress,was observed in the serum samples of both human MASH subjects (p<0.01, ANOVA) and human HCC subjects (p<0.05, ANOVA) relative to healthy control samples. Although not tested, as histone 3 lysine 9 acetylation (H3K9ac) can also result from increased oxidative stress, it is believed that H3K9ac expression would similarly be increased in the serum samples of both MASH subjects and HCC subjects relative to healthy controls.

[0209] As reflected above, the detection of an increase oxidative stress in cells of a biological sample, as detected by variations in the expression of GSH and / or the epigenetic histone modifications of H3K9me3 and / or H3K9ac, may prove useful in the identification of cells indicative of MASH (and thus indicative of potential risk of future MASH-related HCC) and MASH-related HCC.

[0210] Metabolomics

[0211] As shown in FIGS. 16A-18I and Table 2, significant differences were observed in certain lipids, carbohydrates, and amino acids between normal mouse chow (NMC), high-fat diet (HFD), and intervention group (pNaKtide and exercise) MASH mice subjects.Table 2. Plasma metabolomics data for 24-week MASH NMC, HFD, and intervention (pNaKtide or exercise) mouse models.

[0212] Amino Acids. As shown in FIGS. 16A-16E, Oxidized glutathione, 3-ureidopropionic acid, lysine, tyrosine, and zeatin were differently expressed across the 24-week MASH NMC,HFD, pNaKtide-treated HFD, and exercise-treated HFD groups. In this regard, a significant increase in oxidized glutathione was observed in untreated HFD MASH mice relative to untreated NMC MASH mice, and a significant decrease in oxidized glutathione was observed in untreated HFD MASH mice relative to exercise-treated HFD MASH mice (FIG. 16A). A significant increase in 3-ureidopropionic acid in pNaKtide-treated HFD MASH mice was observed relative to both untreated NMC and untreated HFD MASH mice (FIG. 16B). A significant increase in lysine in pNaKtide-treated HFD MASH mice was observed relative to untreated NMC MASH mice (FIG. 16C). A significant increase in lysine was also observed in exercise-treated HFD MASH mice relative to pNaKtide-treated HFD MASH mice, untreated HFD MASH mice, and untreated NMC MASH mice (FIG. 16C). A significant increase in tyrosine was observed in exercise-treated MASH mice relative to untreated HFD MASH mice (FIG. 16D). A decrease in zeatin in untreated HFD MASH mice, pNaKtide-treated HFD MASH mice, and exercise-treated HFD MASH mice was also observed relative to untreated NMC MASH mice.

[0213] Carbohydrates. Butyric acid, benzoic acid, citric acid, glucose, homovanillic acid, indole-3 -pyruvic acid, methyl beta-d-galactoside, citramalic acid, and malic acid were differently expressed across the 24-week MASH NMC, HFD, pNaKtide-treated HFD, and exercise-treated HFD groups (FIGS. 17A-17I). An increase in butyric acid in untreated HFD MASH mice, pNaKtide-treated HFD MASH mice, and exercise-treated HFD MASH mice was observed relative to untreated NMC MASH mice (FIG. 17A). A significant increase in butyric acid in exercise- treated HFD MASH mice was also observed relative to untreated HFD MASH mice (FIG. 17A). A significant increase in benzoic acid in untreated HFD MASH mice was observed relative to untreated NMC MASH mice, pNaKtide-treated HFD MASH mice, and exercise-treated HFD MASH mice (FIG. 17B). A significant increase in citric acid was observed in untreated HFDMASH mice relative to untreated NMC MASH mice, and a significant decrease in citric acid was observed in untreated HFD MASH mice relative to both pNaKtide-treated and exercise-treated MASH mice (FIG. 17C). An increase in glucose in untreated HFD MASH mice was observed relative to untreated NMC, pNaKtide-treated HFD, and exercise-treated HFD MASH mice (FIG. 17D) A significant increase in homovanillic acid in untreated NMC MASH mice was observed relative to HFD, pNaKtide-treated HFD, and exercise-treated HFD MASH mice (FIG. 17E). A significant increase in indole-3-pyruvic acid in pNaKtide-treated HFD MASH mice was observed relative to both untreated HFD and NMC MASH mice (FIG. 17F). A significant decrease in methyl beta-d-galactoside in untreated HFD, pNaKtide-treated HFD, and exercise-treated HFD MASH mice was observed relative to untreated NMC MASH mice (FIG. 17G). An increase in citramalic acid in untreated HFD, pNaKtide-treated HFD, and exercise-treated HFD MASH mice relative to untreated NMC MASH mice (FIG. 17H). An increase in malic acid in untreated HFD, pNaKtide-treated HFD, and exercise-treated HFD MASH mice was also observed relative to untreated NMC MASH mice (FIG. 17H).

[0214] Lipids. As shown in FIGS. 18A-18I, arachidonic acid, corticosterone, heptadecanoic acid, lignoceric acid, linoleic acid, nervonic acid, oleic acid, palmitic acid, and palmitoleic acid were differently expressed across the 24-week MASH NMC, HFD, pNaKtide-treated HFD, and exercise-treated HFD groups. A significant increase in arachidonic acid in untreated HFD MASH mice was observed relative to untreated NMC and exercise-treated HFD MASH mice (FIG. 18A). A significant increase in corticosterone in untreated HFD MASH mice was also observed relative to untreated NMC and exercise-treated HFD MASH mice (FIG. 18B). A significant increase in in heptadecanoic acid in pNaKtide-treated HFD MASH mice was observed relative to untreated NMC, untreated HFD, and exercise-treated MASH mice (FIG. 18C). A significant increase inheptadecanoic acid in exercise-treated HFD MASH mice relative to untreated NMC MASH mice was also observed (FIG. 18C). A significant increase in lignoceric acid in pNaKtide-treated HFD MASH mice was observed relative to exercise-treated HFD, untreated HFD, and untreated NMCMASH mice (FIG. 18D). A significant increase in linoleic acid in pNaKtide-treated HFD MASH mice was observed relative to exercise-treated HFD and untreated HFD MASH mice (FIG. 18E). A significant increase in nervonic acid in pNaKtide-treated HFD MASH mice was observed relative to exercise-treated HFD MASH mice (FIG. 18F). Exercise-treated HFD MASH mice were also observed to have decreased nervonic acid relative to untreated NMC MASH mice (FIG 18F) A significant increase in oleic acid in pNaKtide-treated HFD MASH mice was observed relative to exercise-treated HFD, untreated HFD, and untreated NMC MASH mice (FIG. 18G). Palmitic acid was also observed to be increased in pNaKtide-treated HFD MASH mice relative to exercise-treated HFD, untreated HFD, and untreated NMC MASH mice (FIG. 18H). A significant increase in palmitoleic acid was observed in pNaKtide-treated HFD MASH mice relative to exercise-treated HFD, untreated HFD, and untreated NMC MASH mice (FIG. 181).

[0215] In addition to indicating that plasma survivin level could effectively discriminate between MASH and HCC subjects, the results reflected in FIGS. 1A-1G, 4, 10A-10C, and 16A- 181 and Table 2 also indicate that survivin, Smac-DIABLO, Caspase-cleaved CK18-fragment, LC3-II, H3K9me3, certain metabolites found in Table 2 and / or other proteins and metabolites may indeed be useful in the development of a scoring system (liquid biopsy panel) for the diagnosis and assessment of liver disease progression. Such a panel could be integrated with AFP, for early detection, diagnosis and surveillance of MASH-related hepatocellular in patients and high-risk population.

[0216] Example 2: pNaKtide Treatment of MASH-related HCC Models

[0217] Accepted morphological changes during MASH progression are linked to cell energy and metabolic disturbances. Histological findings at liver biopsy in MASH include the abnormal accumulation of lipids in a micro and macro-vesicular pattern with cell ballooning, inflammatory cell infiltrates, and progressive collagen deposition. Weight loss and exercise are probed maneuvers to achieve consistently a paucity on MASH progression. Adjuvant exercise is not a practical approach in heavy subjects with associated morbidities, like cardiac and peripheral vascular conditions with associated joint dysfunction.

[0218] A variety of medications have been or are in the final stages for FDA approval for treatment of MASH, and immune checkpoint inhibitors and molecular targeted agents are the preferred option as first-line therapy (atezolizumab plus bevacizumab) for advanced HCC. Nevertheless, there is a gap of knowledge in the molecular changes that underpin MASH progression to end-stage liver disease (ESLD) and related HCC, and the search for more effective therapies with lower off-target effects is intense. A newly synthesized peptide (pNaKtide (GRKKRRQRRRPPQSATWLALSRIAGLCNRAVFQ); see, e g., U.S. Patent No. 11,958,915, which is incorporated herein by reference in its entirety) that exhibits its effects on the al-subunit of the Na / K-ATPase (ATP1A1, abundant in the liver), may hold an advantage not only in the prevention but treatment of MASH and HCC. ATP1A1 normalization by pNaKtide as a lid of the ROI feed-forward amplification loop or as a tumor suppressor, has no effects on pump activity on normal hepatocytes. Establishing a causative link between the proposed chronic hepatic glutathione / ATPl Al / Src / ROI feed-forward loop, the malignantATP1 Al / SMAC / survivin tumor suppressor mechanism, and the observed therapeutic effect of pNaKtide on cellular hepatic disarrays is the next critical step towards a translational pre- investigational new drug application (Pre-IND) approach. Therefore, experiments wereundertaken to begin to define the PK and PD of pNaKtide to determine its off-target effects prior to its clinical unveiling. In addition, there was a paucity of specific and sensitive markers for the screening for early-stage HCC detection. Although non-invasive liver scores to predict fibrosis have been developed and validated, i.e., FIB-4, MRE-FIB4, no reliable minimally invasive screening systems have been identified for the early-stage detection of HCC. Furthermore, clinical liver monitoring and response to treatment would be facilitated by a plasma signature based on biological markers that precede cell histological changes, reflecting metabolic disturbances. A liver liquid construct can also be used as a longitudinal screening tool in high- risk populations, i.e., subjects with MASH progression enhancing the detection of early-stage HCC.

[0219] In view of the foregoing, experiments are undertaken i) to determine in human plasma, a signature from biological markers and metabolites predictive of MASH progression and early-stage HCC detection, since it may increase patient’s overall survival, and ii) to define pNaKtide pharmaco-kinetics (PK) and dynamics (PD) prior to its clinical introduction. As a rationale for the studies, it was noted that the incidence of liver disease is rising in North America due in part to the metabolic disturbances from an epidemic of obesity with its concomitant insulin-resistant status. Cell cycle arrest is a central cellular disarrangement present in >80% of cells from chronically diseased livers, and its evolution to apoptosis is linked to MASH progression. In lower cell-redox balance conditions, such as those observed in MASH sustained activation of the ATP1 Al ROI feed-forward loop may occur. In addition to the regulatory signaling for cell metabolism, silent ATP1 Al suppresses tumor development through a SMAC / survivin feedback mechanism. Critically, similar protein expressions for SMAC and survivin were observed in subjects with HCC, and proteins expression significantly differedfrom normal subjects, patients with MASH, or patients with liver metastases (FIG. 4). Preclinical studies also indicate that pNaKtide effectively restores physiological protein expression. Furthermore, pNaKtide rescinded epigenetic changes and re-established cell autophagy activity changes that occurred early in MASH progression to HCC.

[0220] pNaKtide Treatmetn in MASH-related HCC Mouse Models

[0221] As shown in FIG. 2A, 2B, and 5B pNaKtide treatment in MASH-related HCC mice resulted in a significant decrease (p<0.05, ANOVA) in plasma concentration of survivin relative to untreated control MASH-related HCC mice and to levels below that observed in MASH mice on a high-fat diet. As shown in FIG. 2C, a slight, but not statistically relevant decrease in plasma concentration of Smac / DIABLO was observed between MASH mice on a normal mouse chow (NMC) diet and a high-fat diet (HFD). As shown in FIG. 2D a slight, but not statistically relevant decrease in plasma concentration of Smac / DIABLO was observed between pNaKtide-treated MASH-HCC mice and MASH-HCC mice on a HFD. As shown in FIG. 5A, MASH-HCC mice fed a NMC diet were observed to have a significantly lower concentration of Smac / DIABLO in plasma as compared to both untreated HFD and pNaKtide- treated MASH-HCC mice.

[0222] pNaKtide Treatment in SCID Mice with HCC Cells Xenograft

[0223] As shown in FIG. 3A, a significant decrease in plasma concentration of survivin was observed in the pNaKtide-treated HCC tumor growth SCID mice relative to both untreated, HCC tumor growth SCID mice (p<0.01, ANOVA) and untreated SCID mice with no HCC tumor growth (p<0.05, ANOVA). As shown in FIG. 3B, a significant decrease in plasma concentration of Smac / DIABLO was observed between untreated SCID mice with no tumor growth and untreated SCID mice with tumor growth (p<0.05, ANOVA). A slight, but notstatistically relevant, increase in plasma concentration of Smac / DIABLO was observed between untreated SCID mice with tumor growth and pNaKtide-treated SCID mice with tumor growth. As shown in FIG. 3C, a significant increase in caspase-cleaved CK-18 fragments was observed in pNaKtide-treated SCID mice with tumor growth relative to untreated SCID mice with tumor growth (p<0.05, ANOVA). As shown in FIGS. 3D and 3E, pNaKtide treatment was found to significantly increase (p<0.01, ANOVA) the percentage of apoptotic cells in SCID mice with tumor growth relative to untreated SCID mice with tumor growth controls.

[0224] Example 3: Growing Uncoupled Metabolism During MASH Progression is Driven by an Active ATP1A1 Signaling that Promotes Disturbances on Cellular Clock Processes Which Further Aggravate Metabolic Cycles

[0225] The global incidence and prevalence of chronic liver disease and its sequelae endstage liver disease (ESLD) and HCC are increasing, due mainly to a continuous spread of the lipid dysmetabolism from an obesity epidemic. MASH-ESLD is the second most common cause for liver transplantation in the US, affecting preferentially racial / ethnic minorities (African Americans and Hispanic), veterans, and low socio-economic populations with health access disparities. Similar disparities among minorities and veterans influence HCC survival, the fastest rising malignancy with the highest cancer-related death rate in the US. The gap in outcomes may obey, besides the largely unknown molecular mechanisms underpinning the progression of MASH, to the lack of reliable biological markers for early malignant disease detection. Most patients with ESLD and / or HCC had advanced disease at diagnosis, i.e. decompensated cirrhosis or advanced malignancy suffering a lower than 15% overall survival (OS) at 3 years.

[0226] While exploring the role of Src kinase (Src) at the ATP 1 Al, in vitro and in vivo upregulation of the anti-apoptotic protein survivin and downregulation of the mitochondrial pro-'llapoptotic protein second mitochondria-derived activator of caspase / direct inhibitor of apoptosisbinding protein with low pl (Smac / DIABLO) was observed in MASH progression to HCC. Protein expressions were driven by epigenetic changes that in turn downregulated cell autophagy. In addition, a cellular apoptotic switch (on-to-off) occurred through the activation of the P13K^Akt pro-survival pathway with concomitant FoxO3 nuclear extrusion favoring metabolic disarray, cell division, and primary liver carcinogenesis. Critically, a similar protein pattern was observed in livers from patients with HCC, that significantly differed from normal subjects, patients with MASH, or liver metastases. It was thus theorized that such an apoptotic switch may be detected in plasma by measuring SMAC and survivin, as a marker for the screening of HCC. Furthermore, and as a pivotal therapeutic lead, preclinical evidence has been obtained that blocking the ATP1 Al signalosome at its kinase domain using pNaKtide (see U.S. Patent No. 11,958,915, which again is incorporated herein by reference in its entirety), an antagonist of Na / K ATPase / Src receptor complex, reverses the metabolic uncoupling, restores cell autophagy activity, and turns the apoptotic switch back-on in malignant cells arresting concurrently MASH progression. In addition, pNaKtide treatment consistently regressed tumor burden, and in non-malignant hepatocytes rescinded apoptosis, normalizing ATP1A1 signal activity independent of its Na / K -ion transport function. Because ATP1 Al signaling is cell specific with downstream branches that include ROI amplification and tumor suppression, establishing the therapeutic effect of pNaKtide on cellular hepatic disarrangement, is the next critical step towards a translational approach. Based on our preliminary data, where a preclinical correlation of ATP1A1 signaling with cell circadian clock protein expression and metabolic disarrangements was observed, it is proposed that growing uncoupled metabolism during MASH progression is driven by an active ATP1A signaling that promotes disturbances on cellular clockprocesses further aggravating metabolic cycles. Respiratory mitochondrial uncoupling from lipid overload and its associated decreased cell-oxi-status (glutathione sp.), may affect cell clock processes, i.e., lipid and glucose metabolisms and autophagy promoting progression to apoptosis. It is therefore hypothesize that liver cell changes during MASH progression from metabolic disturbances are magnified by altered cell clock processes conducive of an apoptotic switch to cell division.

[0227] Mitochondrial dysfunction and insulin resistance decrease cell oxi-defense mechanisms. A “two-hit” hypothesis is an accepted paradigm to explain the progression from simple steatosis (MAFLD) to its inflammatory state (MASH). While a first hit involves dysregulated hepatic lipid accumulation (steatosis), the second hit includes oxidative / metabolic stress aggravated by an insulin-resistant status that overwhelms hepatocyte oxi-defensive mechanisms, i.e., glutathione leading to hepatocyte / stellate cells senescence and inflammation. Further lowering of the oxi-states promote the opening of the mitochondrial permeability transition pores offering a leak of both Cytochrome-C and the second mitochondria-derived activator of caspases / direct inhibitor of apoptosis-binding protein (SMAC), potent activators of programmed cell death through the caspase-cascade. It has been proposed that hepatocyte evolution from cell arrest to apoptosis is a critical “third hit” that drives the progression from MASH to advance fibrosis with ESLD. Serum cytokeratin-18 fragments in human subjects has previously been measured and demonstrated as having a strong correlation with the severity of MASH.

[0228] Cellular senescence is associated to metabolic disturbances and epigenetic changes. A central cellular state of chronic liver disease is cell cycle arrest, and its evolution to apoptosis is thus linked to MASH progression. In the liver, cellular senescence is associated witha fibrogenic environment, and the association between advanced liver fibrosis and shortening of the cell telomere is consistent. In addition, cells can be induced to a senescence status by a variety of cellular stressors such as an increase of ROI (enhanced in MASH), conducive of a low oxidative state (senescence). Insulin resistance is associated with further aggravation of the cell metabolic turmoil enhancing senescence cell expression and disturbances on glutathione sp. Thus, metabolic prints have been documented in animal models and humans with chronic liver disease and HCC. Furthermore, epigenetic changes (histones acetylation-H3K9ac and trimethyl ation-H3K9me3) that result from oxidative cellular stress have been emphasized as a critical part of the changes favoring tumorigenesis in MASH. ATP1 Al-signal normalization rescinded epigenetic changes, regressed liver fibrosis, and resolved insulin resistance through a nuclear FOXO1 mechanism (FIGS. 9A-9F).

[0229] Altered cell circadian protein expression is associated with further metabolic disturbances and decreased cell autophagy. The circadian rhythm in the liver is an internal timing system that is rhythmically adapted and proactive in the response to recurring environmental changes, affecting cell differentiation through metabolic regulation that involves cell autophagy and apoptotic activities. A feedback loop of gene transcription and translation generates circadian rhythms through a core set of "clock genes" CLOCK, BMAL1 (ARNTL), and Npas2, which are regulated by cryptochromes, CRY1 and CRY2 and the period genes PERI, PER2, and PER3, and Rev-erb-alpha and 13. The former genes regulate mitochondrial 13-lipid oxidation and glycolysis through the modulation of the cofactors PPARy / PGCla. The deletion of BMAL1 in the mice liver leads to ROS accumulation, mRNA methylation, and downregulation of PPARy transcription, affecting 13 -lipid oxidation.

[0230] Cell epigenetic changes and protein cell autophagy are disturbed early in MASH progression to HCC. Epigenetic factors are involved in the regulation of hepatic lipid metabolism, insulin resistance, mitochondrial damage, oxidative stress response, and inflammation, and they have been implicated in the development and progression of MASH to HCC. Several methylation and acetylation residues in HCC has been described including trimethylation of lysine 9 in histone 3 (H3K9me3), acetylation of histone 3 at lysine on position 18 (H3K18Ac), and histone H4 at lysine 20 (H4K20), among others. Alterations in the cell autophagic machinery are present in several disease conditions, including aging and cancer. There was a link between the autophagosome-lysosomal pathway and MASH where decreased autophagic function may be implicated in the initial development of hepatic steatosis and progression of MASH to HCC. Normalization of the ATP1A1 signaling rescinded epigenetic changes and re-established cell autophagy activity in- vitro and in-vivo models.

[0231] A switch from apoptosis to uncontrolled cell division occurs in the progression of MASH to HCC. The predominant cell apoptotic pathway in MASH reflects an organelle origin, where leakage of respiratory chain proteins and SMAC (expressed in the inner mitochondrial membrane) renders cell fate. Furthermore, SMAC as it translates to the cytosol creates a feedback mechanism on cell apoptotic activity by the awaking of the inhibitor of apoptosis proteins family, i.e., survivin. However, as the SMAC / survivin ratio decreases, either by downregulation of SMAC or by up-regulation of survivin or both, the excess of survivin translates to the nucleus where it exercises a potent stimulus for cell division. Normalization of the ATP1A1 signal by pNaKtide not only rescinded the progression of MASH mediated by oxidative stress, restoring normal liver histology, and promoting an off-apoptotic switch, but also restored apoptotic activity in malignant cells (on-apoptotic switch), arresting tumor growth. Indeed,dysregulated expressions of survivin and Caspase-3 have been documented in human HCC. ATP1 Al exercises a tumor suppressor role in MASH related HCC through two circuits (P13K & Fox03) that regulates survivin expression. Normalization of the ATP1A1 signaling inhibits tumor cell growth by inducing apoptosis in a dose- dependent manner in two human HCC cell lines, associated with upregulation of nuclear FoxO3. In turn, FoxO3 downregulates the transcription and expression of the anti-apoptotic protein survivin. Studies have shown that the inhibition of the PI3K-Akt pathway resulted in FoxO3 translocation to the nucleus, leading to survivin repression, and FoxO3-induced apoptosis was enhanced upon survivin knockdown in cells.

[0232] ATP1 Al Expression and Signalsome. The Na / K adenosine triphosphatase (P-type ATPase), has been shown to signal oxidative stress through mechanisms distinct from its well- understood ion-pumping function. The Na / K -ATPase has two subunits, a and 0, allowing for its distinct non-ionic and ionic pumping functions. The al-subunit (ATP1A1) is expressed by liver cells (hepatocytes, Kupffer cells, and hepatic stellate cells), and serves a scaffolding function with sarcoma-related kinase (Src). Not only the al-subunit of the Na / K- ATPase is significantly downregulated, but the Na / K-pump activity is also decreased in livers from our preclinical models of MASH and MASH-HCC. Thus, changes in the conformation of the Na / K- ATPase allow al to modulate Src, and ROI are not only generated from the ATP 1 Al signaling cascade but can also initiate it, creating a feed-forward oxidant amplification loop that has been characterized. Nevertheless, the mechanism as how the signalosome loop exercises mitochondrial uncoupling of the metabolism remains elusive. In addition to the regulatory signaling for cell metabolism, the ATP1 Al portraits as a tumor suppressor in MASH-HCC by involving SMAC / survivin proteins through the PI3K-Akt and FoxO3 pathways. pNaKtide is afirst in class synthetic peptide that mimics an active binding sequence of the al-subunit to Src. This sequence that interfaces with the Src kinase catalytic domain, was synthesized (NaKtide = 20 amino acids) to be subsequently merged with a leader sequence (13aa), establishing cell permeability. Consistently, this synthetic peptide blocks the formation of the ATPIAI / Src receptor, without affecting Src activity regulated by IGF1 but re- establishes physiological signalosome state by critically preventing excessive Src phosphorylation. In support of this model, we have obtained evidence that the predicted alteration of liver al-hydrolytic activity and signaling occurs in a preclinical mouse model of HFD-induced MASH-HCC. Indeed, normalization of the ATP1A1 signalosome by pNaKtide has been observed in vitro to abrogate malignant cell replication, and in vivo to decrease tumor burden through a mechanism specific to its intrinsic amino sequence since 'scramble' pNaKtide (same amino acids but if random sequence) exercise no cell effects. pNaKtide increases al-subunit expression in two human HCC cell lines and normalized Na / K-pump activity in livers from mice with MASH.

[0233] Based on preliminary data where it was noted that a mechanistic association of MASH progression Cell Clock proteins during the characterization of Cre / ATPl Al hepatocyte specific KO mice, it is proposed that growing uncoupled metabolisum during MASH progression is driven by an active ATP1A1 signaling promoting associated disturbances of cellular clock processes. Mitochondrial lipid overload may generate progressive larger amounts of ROI from respiratory uncoupling, depleting GSH with further decay of cell oxi-status that promotes increasing ATP1A1 activation with a double and concurrent line of response: one that increases PI3K— >Akt & FoxOl pathways (promoting survivin upregulation), and a second one altering the cell clock by downregulating BMAL1 & Rev-erb-alpha expression that in turn affect the expression of PPARy / PGCla (main cofactors in [3-lipid oxidation and TCA glycolysis,respectively through, at least in part, a FoxO3 pathway) mitigating further mitochondrial energy and aggravating pore leakage. Furthermore, clinical liver monitoring and response to treatment would be facilitated by a plasma signature based on biological markers that precede cell histological changes, reflecting metabolic disturbances. A liver liquid construct also can be used as a screening tool in high-risk populations for the detection of early-stage HCC. Establishing a causative link between the ATP 1 Al and the proposed chronic hepatic glutathione / BMALl / LC3- II / ROI feed-forward loop, the malignant ATPlAl / SMAC / survivin tumor suppressor mechanism, and the observed therapeutic effect of pNaKtide on cellular hepatic disarrays is the next critical step towards a translational pre-IND approach.

[0234] Effect of pNaKtide Treatment on F0X01 in Human HCC Cell Line

[0235] As shown in FIGS. 9A and 9B, the normalization of Na+ / K+ transporting subunit alpha 1 (ATP 1 Al) via pNaKtide administration has no effect on normal hepatocytes but inhibited HCC in SNU475 human cell line. As shown in FIGS. 9C and 9E, pNaKtide treatment was found to restore nuclear FOXO1 in SNU475 HCC cell line.

[0236] ATP 1 Al is Important for MASH Progression by Modulating Cell Clock Proteins

[0237] To demonstrate that ATP1A1 is important in MASH progression by modulating cell clock proteins. BMAL1 and Rev-erb-alpha expression were tested under down-regulated al- expression (Cre-liver specific al / Knock-out (KO) mice). It was found that ATP1A1 KO has a lower expression of the al-subunit in liver tissue homogenates from male and female (female data not shown) mice when compared with the same animal kidney tissue homogenates, and mice controls. ATP 1 Al expression, by confocal microscopy and western blotting was associated with upregulation of survivin and downregulation of BMAL1 and Rev-erb-alpha proteins (*p<0.05, **p<0.01, ANOVA) (FIGS. IIA-IIH). Genetic verification is needed to validate themechanism of action of pNaKtide providing unequivocal evidence that ATP 1 Al signal can be targeted to develop new classes of therapeutics. Accordingly, the foregoing findings are relevant because for the first time we reproduced our prior findings in both human HCC cell lines uncovering ATP1A1 signalosome association with cell division (survivin) and cell clock proteins (metabolism modulator) for primary carcinogenesis, as a feasible checkpoint target for tumor therapy.

[0238] Sequence of Cellular Events in MASH Progression

[0239] To determine the sequence of cell events in MASH progression, plasma glucose and cell redox status (GS sp.) varied in time in our diet induced MASH mice model. HFD mice were diabetic with high levels of GSH / GS:SG by week 24 when compared with mice treated with pNaKtide or exercise (FIGS. 12B and 12C). In contrast, ]3-lipid oxidation, and TCA cycle modulators (PPAR-y / PGCl-a cofactors) were upregulated by pNaKtide, and exercise by week 16, and FoxOl expression peaked at week 24 (FIGS. 12D-12F). These observations are critical because for the first time we established cell redox changes occurred concurrently with (1) downregulation of cell clock metabolic modulators; (2) that normalization of the ATP1A1 signaling restored plasma glucose and GSH sp. in association with an upregulation of cell clock modulators; (3) that such response occurred 4 weeks after interventions during MASH progression, and (4) that metabolic restoration preceded survivin signaling downregulation (FOXO1) by 8 weeks in our diet induced MASH animal model. While Our MASH-HCC model has a down time from induction to experimentation of 16 to 20 weeks for the development of HCC, our Cre-ATPl Al / hepatocyte specific KNO mice consistently develop tumors by 7-10 weeks. Therefore, it is planned to determine weekly the sequential changes of the ATP 1 Alsignaling and its association with cell clock and cell survival protein expressions in our Cre- hepatocyte specific al / KO mice where livers express <10% ATP1A1.

[0240] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference, including the references set forth in the following list:REFERENCES1. Baffy, G. MicroRNAs in Nonalcoholic Fatty Liver Disease. J. Clin. Med. 2015, 4, 1977- 1988. https: / / doi.org / 10.3390 / jcm41219532. Philips C A, Rajesh S, Nair D C, et al. (November 05, 2021) Hepatocellular Carcinoma in 2021: An Exhaustive Update. Cureus 13(11): el9274. doi: 10.7759 / cureus.19274.3. Udoh, U.-A.S.; Banerjee, M.; Rajan, P.K.; Sanabria, J.D.; Smith, G.; Schade, M.;Sanabria, J. A.; Nakafuku, Y.; Sodhi, K.; Pierre, S.V.; Shapiro, J. I.; Sanabria, J.R. Tumor- Suppressor Role of the al-Na / K-ATPase Signalosome in NASH Related Hepatocellular Carcinoma. Int. J. Mol. Sci. 2022, 23, 7359. https: / / doi.org / 10.3390 / ijms231373594. Grattagliano I, Montezinho LP, Oliveira PJ, Fruhbeck G, Gomez -Ambrosi J, Montecucco F, et al. Targeting mitochondria to oppose the progression of nonalcoholic fatty liver disease. Biochem Pharmacol. 2019;160:34-45.5. Karagozian R, Derdak Z, Baffy G. Obesity-associated mechanisms of hepatocarcinogenesis. Metabolism. 2014;63(5):607-17.6. Solis Herruzo JA, Garcia Ruiz I, Perez Carreras M, Munoz Yague MT. Non-alcoholic fatty liver disease. From insulin resistance to mitochondrial dysfunction. Rev Esp Enferm Dig. 2006;98(l l):844-74.Tokushige K, Ikejima K, Ono M, Eguchi Y, Kamada Y, Itoh Y, et al. Evidence-based clinical practice guidelines for nonalcoholic fatty liver disease / nonalcoholic steatohepatitis 2020. J Gastroenterol. 2021;56(l 1):951-63. Casadei-Gardini A, Rimini M, Kudo M, Shimose S, Tada T, Suda G, et al. Real Life Study of Lenvatinib Therapy for Hepatocellular Carcinoma: RELEVANT Study. Liver Cancer. 2022; 1 l(6):527-39. Casadei-Gardini A, Rimini M, Tada T, Suda G, Shimose S, Kudo M, et al. Atezolizumab plus bevacizumab versus lenvatinib for unresectable hepatocellular carcinoma: a large real-life worldwide population. Eur J Cancer. 2023;180:9-20. Orlandi P, Solini A, Banchi M, Brunetio MR, Cioni D, Ghiadoni L, Bocci G.Anti angiogenic Drugs in NASH: Evidence of a Possible New Therapeu c Approach. Pharmaceuticals (Basel). 2021;14(10). Peng Y, Wong CC, Yu J. The paradox of immunotherapy in NASH-HCC. Signal Transduct Target Ther. 2021;6(l):228. Rimini M, Rimassa L, Ueshima K, Burgio V, Shigeo S, Tada T, et al. Atezolizumab plus bevacizumab versus lenvatinib or sorafenib in non-viral unresectable hepatocellular carcinoma: an international propensity score matching analysis. ESMO Open.2022;7(6): 100591. Satiya J, Snyder HS, Singh SP, Satapathy SK. Narrative review of current and emerging pharmacological therapies for nonalcoholic steatohepatitis. Transl Gastroenterol Hepatol. 2021;6:60. Shimose S, Hiraoka A, Nakano M, Iwamoto H, Tanaka M, Tanaka T, et al. First-line sorafenib sequential therapy and liver disease etiology for unresectable hepatocellular carcinoma using inverse probability weighting: A multicenter retrospective study. Cancer Med. 2021;10(23):8530-41.Kawata N, Takahashi H, Iwane S, Inoue K, Kojima M, Kohno M, et al. FIB-4 indexbased surveillance for advanced liver fibrosis in diabetes patients. Diabetol Int. 2021;12(l):l 18-25. Neagoe CD, Farmazon AS, Amzolini AM, Singer CE, lanosi SL, Tutunaru CV, et al. The role of non-invasive scores in determining the liver fibrosis in NAFLD and psoriatic patients. Rom J Morphol Embryol. 2020;61(2):503-l 1. Ajmera V, Kim BK, Yang K, Majzoub AM, Nayfeh T, Tamaki N, et al. Liver Stiffness on Magnetic Resonance Elastography and the MEFIB Index and Liver-Related Outcomes in Nonalcoholic Fa y Liver Disease: A Systematic Review and Meta-Analysis of Individual Participants. Gastroenterology. 2022;163(4): 1079-89 e5. Sung H, Ferlay J, Siegel RL, Laversanne M, Soeijomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49. Collaborators GO, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377(l): 13-27. Collaborators GOaO. Health Effects of Overweight and Obesity in 195 countries over 25 years. N Engl J Med 2017;377( 1 ): 13-27. Global Burden of Disease Cancer C, Fitzmaurice C, Akinyemiju TF, Al Lami FH, Alam T, Alizadeh-Navaei R, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability- Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2018;4(11): 1553-68. Global Burden of Disease Cancer C, Fitzmaurice C, Allen C, Barber RM, Barregard L, Bhutia ZA, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability- Adjusted Life- years for 32Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2016. Global Burden of Disease Cancer C, Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, et al. The Global Burden of Cancer 2013. JAMA Oncol. 2015;l(4):505-27. Stanaway JD, Flaxman AD, Naghavi M, Fitzmaurice C, Vos T, Abubakar I, et al. The global burden of viral hepatitis from 1990 to 2013: findings from the Global Burden of Disease Study 2013. Lancet. 2016;388(10049): 1081-8. Brunner N, Bruggmann P. Trends of the Global Hepatitis C Disease Burden: Strategies to Achieve Elimination. J Prev Med Public Health. 2021;54(4):251-8. Polanco PM, Ju MR, Chansard M, Mathew Augustine M, Meier J, Mortensen E, et al. Trends and Disparities in Treatment Utilization for Early-Stage Hepatocellular Carcinoma in the Veteran Population. Ann Surg Oncol. 2022;29(9):5488-97. Reynolds J, Hashimi S, Nguyen N, Infield J, Weir A, Khatiak A. The Effect of Race on Outcomes in Veterans With Hepatocellular Carcinoma at a Single Center. Fed Pract. 2022;39(Suppl 3):S64-S7a. Robinson A, Hirode G, Wong RJ. Ethnicity and Insurance-Specific Disparities in the Model for End-Stage Liver Disease Score at Time of Liver Transplant Waitlist Registration and its Impact on Mortality. J Clin Exp Hepatol. 2021 ; 11(2): 188- 94. Wong RJ, Saab S, Konyn P, Sundaram V, Khalili M. Rural-Urban Geographical Disparities in Hepatocellular Carcinoma Incidence Among US Adults, 2004-2017. Am J Gastroenterol . 2021 ; 116(2) : 401 -6. Danford CJ, Sanchez JE, Corey KE. Managing the Burden of Non-NASH NAFLD. Curr Hepatol Rep. 2017;16(4):326-34.Barzi A, Zhou K, Wang S, Dodge JL, El-Khoueiry A, Setiawan VW. Etiology and Outcomes of Hepatocellular Carcinoma in an Ethnically Diverse Population: The Multiethnic Cohort. Cancers (Basel). 2021 ; 13(14). Flores YN, Datia GD, Yang L, Corona E, Devineni D, Glenn BA, et al. Disparities in Hepatocellular Carcinoma Incidence, Stage, and Survival: A Large Population-Based Study. Cancer Epidemiol Biomarkers Prev. 2021;30(6): 1193-9. Mazumder NR, Simpson D. Disparities persist for Black liver transplant recipients despite years of data collection: What is missing? Am J Transplant. 2021;21(12):3821-2. Mazumder NR, Simpson D, Atiemo K, Jackson K, Zhao L, Daud A, et al. Black Patients With Cirrhosis Have Higher Mortality and Lower Transplant Rates: Results From a Metropolitan Cohort Study. Hepatology. 2021;74(2):926-36. Poulson MR, Blanco BA, Geary AD, Kenzik KM, McAneny DB, Tseng JF, Sachs TE. The role of racial segregation in treatment and outcomes among patients with hepatocellular carcinoma. HPB (Oxford). 2021;23(6):854-60. Sada YH, da Costa WL, Jr., Kramer JR, Chiao EY, Zafeiropoulou E, Dong Y, et al. Survival outcomes in Veterans with hepatocellular carcinoma, with and without HIV infection. AIDS. 2023. Wong RJ, Kim D, Ahmed A, Singal AK. Patients with hepatocellular carcinoma from more rural and lower-income households have more advanced tumor stage at diagnosis and significantly higher mortality. Cancer. 2021;127(l):45-55. Udoh US, Baneijee M, Rajan PK, Sanabria JD, Smith G, Schade M, et al. Tumor- Suppressor Role of the alpha 1-Na / K-ATPase Signalosome in NASH Related Hepatocellular Carcinoma. Int J Mol Sci. 2022;23(13). Sanabria JR, Kombu RS, Zhang GF, Sandlers Y, Ai J, Ibarra RA, et al. Glutathione species and metabolomic prints in subjects with liver disease as biological markers for the detection of hepatocellular carcinoma. HPB (Oxford). 2016;18(12):979-90.Singal AG, Haaland B, Parikh ND, Ozbay AB, Kirshner C, Chakankar S, et al. Comparison of a multitarget blood test to ultrasound and alpha-fetoprotein for hepatocellular carcinoma surveillance: Results of a network meta-analysis. Hepatol Commun. 2022;6(10):2925-36. Nathani P, Gopal P, Rich N, Yopp A, Yokoo T, John B, et al. Hepatocellular carcinoma tumour volume doubling time: a systematic review and meta-analysis. Gut.2021;70(2):401-7. Lee YT, Fujiwara N, Yang JD, Hoshida Y. Risk stratification and early detection biomarkers for precision HCC screening. Hepatology. 2023;78(l):319-62. Pepe MS, Etzioni R, Feng Z, Potier JD, Thompson ML, Thomquist M, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst.2001;93(14):1054-61. Best J, Bechmann LP, Sowa JP, Sydor S, Dechene A, Pflanz K, et al. GALAD Score Detects Early Hepatocellular Carcinoma in an International Cohort of Patients With Nonalcoholic Steatohepatitis. Clin Gastroenterol Hepatol. 2020;18(3):728-35 e4. Schotten C, Ostertag B, Sowa JP, Manka P, Bechmann LP, Hilgard G, et al. GALAD Score Detects Early-Stage Hepatocellular Carcinoma in a European Cohort of Chronic Hepatitis B and C Patients. Pharmaceuticals (Basel). 2021; 14(8). Ugonabo, et al., The Current Status of the Liver Liquid Biopsy in MASH Related HCC: Overview and future directions. Biomolecules 2023, 13, 1369. https: / / doi.org / 10.3390 / bioml3091369. Udoh, etl al., Tumor-Supressor Role of the al-Na / K-ATPase Signalosome in NASH Related Hepatocellular Carcinoma. It. J. Mol. Sc. 2022, 23, 7359. https: / / doi.org / 10.3390 / ijms23137359. Sanabria, et al., Glutathione species and metabolomics prints in subjects with liver disease as biological markers for the detection of hepatocellular carcinoma. HPB, Vol. 18, Issue 12, 979-990 (2016).49. Rajan, et al., The Biological Clock of Liver Metabolism in Metabolyic Dysfunction- Associated Steatohepatitis Progression to Hepatocellular Carcinoma. Biomedicines 2024, 12, 1961.

[0241] It will be understood that various details of the presently disclosed subject matter can be changed without departing from the scope of the subject matter disclosed herein.Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.

Claims

CLAIMSWhat is claimed is:

1. A method for identifying metabolic dysfunction in a cell indicative of metabolic dysfunction-associated steatohepatitis (MASH)-related hepatocellular carcinoma (HCC) or risk of MASH-related HCC, comprising: obtaining a biological sample from a subject, the biological sample including one or more cells; assaying the biological sample to detect an expression level or activity of a combination of biomarkers in the one or more cells of the biological sample, the combination of biomarkers including two or more biomarkers selected from survivin, second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), Na+ / K+ transporting subunit alpha 1 (ATP1A1), caspase-cleaved cytokeratin-18 (CK-18) fragment, forkhead box protein 01 (F0X01), microtubule-associated protein 1 light chain 3-II (LC3-II), brain and muscle arnt-like protein-1 (BMAL1), Rev-erb-alpha, glutathione (GSH), histone 3 lysine 9 trimethylation (H3K9me3), and histone 3 lysine 9 acetylation (H3K9ac); detecting a biological switch in the one or more cells of the biological sample based on the detected expression level or activity of the combination of biomarkers relative to a control expression level or activity of the combination of biomarkers; and identifying the biological sample as including one or more metabolically dysfunctional cells indicative of MASH-related HCC or risk of MASH-related HCC subsequent to the detection of the biological switch in the one or more cells of the biological sample.

2. The method of claim 1, wherein detecting the biological switch includes detecting a downregulation of cell autophagy in the one or more cells of the biological sample based on the detected expression level or activity of the combination of biomarkers in the one or more cells of the biological sample.

3. The method of claim 2, wherein the combination of biomarkers includes at least one of LC3-II, BMAL1, and Rev-erb-alpha, and wherein detecting the downregulation of cell autophagy in the one or more cells of the biological sample includes detecting a decrease in expression level or activity of LC3-II relative to a control expression level or activity of LC3-II, detecting a decrease in expression level or activity of BMAL1 relative to a control expression level or activity of BMAL1, detecting a decrease in expression level or activity of Rev-erb-alpha relative to a control expression level or activity of Rev-erb-alpha, or combinations thereof in the one or more cells of the biological sample.

4. The method of claim 3, wherein the combination of biomarkers includes at least one of GSH, H3K9me3, H3K9ac, wherein detecting the biological switch includes detecting an increase in oxidative stress in the one or more cells of the biological sample based on the detected expression level or activity of the combination of biomarkers in the one or more cells of the biological sample, and wherein detecting the increase in oxidative stress includes detecting a decrease in expression level or activity of GSH relative to a control expression level or activity of GSH, detecting an increase in expression level or activity of H3K9me3 relative to a control expressionlevel or activity of H3K9me3, detecting an increase in expression level or activity of H3K9ac relative to a control expression level or activity of H3K9ac, or combinations thereof in the one or more cells of the biological sample..

5. The method of claim 3, wherein the combination of biomarkers includes at least one of survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, and F0X01, wherein detecting the biological switch includes detecting an apoptotic to non-apoptotic switch in the one or more cells of the biological sample based on the detected expression level or activity of the combination of biomarkers in the one or more cells of the biological sample, and wherein detecting the apoptotic to non-apoptotic switch includes detecting an increase in expression level or activity of survivin relative to a control expression level or activity of survivin, detecting an increase expression level or activity of Smac / DIABLO relative to a control expression level or activity of Smac / DIABLO, detecting an increase in expression level or activity of ATP1 Al relative to a control expression level or activity of ATP1 A, detecting an increase in expression level or activity of caspase-cleaved CK-18 fragment relative to a control expression level or activity of caspase-cleaved CK-18 fragment, detecting a decrease in expression level or activity of F0X01 relative to a control expression level or activity of FOXOl, or combinations thereof in the one or more cells of the biological sample.

6. The method of claim 1, wherein detecting the biological switch includes detecting an increase in oxidative stress in the one or more cells of the biological sample based on the detected expression level or activity of the combination of biomarkers in the one or more cells of the biological sample.

7. The method of claim 6, wherein the combination of biomarkers includes at least one ofGSH, H3K9me3, and H3K9ac, and wherein detecting the increase in oxidative stress includes detecting a decrease in expression level or activity of GSH relative to a control expression level or activity of GSH, detecting an increase in expression level or activity of H3K9me3 relative to a control expression level or activity of H3K9me3, detecting an increase in expression level or activity of H3K9ac relative to a control expression level or activity of H3K9ac, or combinations thereof in the one or more cells of the biological sample.

8. The method of claim 7, wherein the combination of biomarkers includes at least one of survivin, Smac / DIABLO, ATP1A1, caspase-cleaved CK-18 fragment, and F0X01, wherein detecting the biological switch includes detecting an apoptotic to non-apoptotic switch in the one or more cells of the biological sample based on the detected expression level or activity of the combination of biomarkers in the one or more cells of the biological sample, and wherein detecting the apoptotic to non-apoptotic switch includes detecting an increase in expression level or activity of survivin relative to a control expression level or activity of survivin, detecting an increase in expression level or activity of Smac / DIABLO relative to a control expression level or activity of Smac / DIABLO, detecting an increase in expression level or activity of ATP1A1 relative to a control expression level or activity of ATP1A, detecting an increase in expression level or activity of caspase-cleaved CK-18 fragment relative to a control expression level or activity of caspase-cleaved CK-18 fragment, detecting a decrease inexpression level or activity of FOXO1 relative to control expression level or activity of FOXOl, or combinations thereof in the one or more cells of the biological sample.

9. The method of claim 1, wherein detecting the biological switch includes detecting an apoptotic to non-apoptotic switch, a downregulation of cell autophagy, and an increase in oxidative stress in the one or more cells based on the detected expression level or activity of the combination of biomarkers in the one or more cells of the biological sample.

10. The method of claim 9, wherein the combination of biomarkers includes(i) one or more biomarkers selected from survivin, Smac / DIABLO, ATP1A1, caspase- cleaved CK-18 fragment, and FOXO1,(ii) one or more biomarkers selected from LC3-II, BMAL1, and Rev-erb-alpha, and(iii) one or more biomarkers selected from GSH, H3K9me3, and H3K9ac.

11. The method of claim 10, wherein the combination of biomarkers includes survivin, Smac / DIABLO, caspase-cleaved CK-18 fragment, LC3-II, BMAL1, Rev-erb-alpha, GSH, and H3K9me3.

12. The method of claim 1, wherein the biological sample includes blood, plasma, serum, or a tumor biopsy.

13. The method of claim 1, further comprising steps of:assaying the biological sample to detect an amount of one or more metabolites including a lipid, a carbohydrate, a peptide, or a combination thereof in the one or more cells of the biological sample; and detecting a difference in the amount of the one or more metabolites in the one or more cells of the biological sample relative to a control amount of the one or metabolites; wherein identifying the biological sample as including one or more metabolically dysfunctional cells indicative of MASH-related HCC or risk of MASH-related HCC occurs subsequent to detecting the difference in the amount of the one or more metabolites in the one or more cells of the biological sample relative to the control amount of the one or more metabolites.

14. The method of claim 1, wherein assaying the biological sample includes assaying the biological sample using mass spectrometry (MS) analysis, immunoassay analysis, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay, or a combination thereof.

15. A method for diagnosis or prognosis of metabolic dysfunction-associated steatohepatitis (MASH)-related hepatocellular carcinoma (HCC) or risk of MASH-related HCC in a subject, comprising: obtaining a biological sample from the subject; detecting an expression level or activity in the biological sample of a combination of biomarkers including two or more biomarkers selected from survivin, second mitochondria- derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), Na+ / K+ transporting subunit alpha 1 (ATP1A1), caspase-cleaved cytokeratin- 18 (CK-18) fragment, forkhead box protein 01 (F0X01), microtubule-associated protein 1 lightchain 3-II (LC3-II), brain and muscle amt-like protein-1 (BMAL1), Rev-erb-alpha, glutathione (GSH), histone 3 lysine 9 trimethylation (H3K9me3), and histone 3 lysine 9 acetylation (H3K9ac); detecting a measurable difference between the expression level or activity of the combination of biomarkers in the biological sample relative to a control expression level or activity of the combination of biomarkers; and diagnosing the subject as having MASH-related HCC or risk thereof based on the detected measurable difference between the expression level or activity of the combination of biomarkers in the biological sample relative to the control expression level or activity level of the combination of markers.

16. The method of claim 15, wherein the combination of biomarkers includes one or more biomarkers selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac.

17. The method of claim 15, further comprising selecting a treatment or modifying a treatment for MASH-related HCC based on the detected expression level or activity of the combination of biomarkers in the biological sample relative to the control expression level or activity of the combination of biomarkers.

18. The method of claim 15, and further comprising steps of: detecting an amount of one or more metabolites including a lipid, a carbohydrate, a peptide, or a combination thereof in the biological sample; anddetecting a measurable difference between the amount of the one or more metabolites in the biological sample relative to a control amount of the one or more metabolites; wherein diagnosing the subject as having MASH-related HCC or risk thereof is further based on the detected measurable difference between the amount of the one or more metabolites in the biological sample relative to the control amount of the one or more metabolites.

19. A method for determining whether to initiate or continue prophylaxis or treatment of metabolic dysfunction-associated steatohepatitis (MASH)-related hepatocellular carcinoma (HCC) in a subject, comprising: obtaining a series of biological samples taken over a period of time from the subject; assaying the series of biological samples to determine an expression level or activity in each of the biological samples of a combination of biomarkers including two or more biomarkers selected from survivin, second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), Na+ / K+ transporting subunit alpha 1 (ATP1A1), caspase-cleaved cytokeratin-18 (CK-18) fragment, forkhead box protein 01 (FOXO1), microtubule-associated protein 1 light chain 3-II (LC3-II), brain and muscle arnt-like protein-1 (BMAL1), Rev-erb-alpha, glutathione (GSH), histone 3 lysine 9 trimethylation (H3K9me3), and histone 3 lysine 9 acetylation (H3K9ac); and detecting a measurable change in the expression level or activity of the combination of biomarkers in the series of biological samples to thereby determine whether to initiate or continue the prophylaxis or treatment of MASH-related HCC in the subject.

20. The method of claim 19, wherein the combination of biomarkers includes one or more biomarkers selected from LC3-II, BMAL1, Rev-erb-alpha, H3K9me3, and H3K9ac.

21. The method of claim 19, wherein the prophylaxis or treatment includes administration of an antagonist of a Na / K ATPase / Src receptor complex to the subject.

22. An assay for screening for metabolic dysfunction-associated steatohepatitis (MASH)- related hepatocellular carcinoma (HCC) or risk of MASH-related HCC in a subject, comprising: applying, to a biological sample from the subject, one or more agents capable of detecting an expression level or activity of a combination of biomarkers including two or more biomarkers selected from survivin, second mitochondria-derived activator of caspase / direct inhibitor of apoptosis-binding protein with low pl (Smac / DIABLO), Na+ / K+ transporting subunit alpha 1 (ATP1A1), caspase-cleaved cytokeratin-18 (CK-18) fragment, forkhead box protein 01 (FOXO1), microtubule-associated protein 1 light chain 3-II (LC3-II), brain and muscle arnt-like protein- 1 (BMAL1), Rev-erb-alpha, glutathione (GSH), histone 3 lysine 9 trimethylation (H3K9me3), and histone 3 lysine 9 acetylation (H3K9ac); and detecting the expression level or activity of the combination of biomarkers in the biological sample.

Images