Gastric cancer RNA biomarkers and uses thereof

Abstract

Methods of diagnosing, monitoring, or treating gastric cancer (e.g., early gastric cancer) in a patient are accomplished by detecting the expression levels of RNA biomarkers (e.g., microRNA biomarkers) in a biological sample. Such RNA biomarkers include, for example, miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR- 431-5p, miR-1246. miR-192-3p. miR-196a-5p, miR-183-5p, and miR-135b-5p. The RNA biomarkers can be cell-free miRNA, exosomal miRNA, or a combination thereof, such as cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR- 27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21- 3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p.

Description

GASTRIC CANCER RNA BIOMARKERS AND USES THEREOFCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to US Application No. 63 / 649,687 filed May 20, 2024, the disclosure of which is incorporated by reference herein in its entirety.BACKGROUND

[0002] Gastric cancer (GC) ranks as the fifth most common cancer and the third leading cause of cancer-related deaths globally. According to the annual GLOBOCAN 2020 report, there were more than one million newly diagnosed GC cases and an estimated 750,000 cancer-related deaths in 2020. Since gastric cancer is often asymptomatic in its early stages, approximately 60% of patients with GC are diagnosed at an advanced stage and thus are ineligible for curative treatment, resulting in early metastasis and poor prognosis. To address this significant clinical challenge, mass screening using photofluorography or endoscopy in people aged 40 years and older has been implemented in regions where GC is more prevalent such as Japan and Korea. While these early detection screening methods have improved prognoses of GC patients, these approaches are not feasible in most of the world due to lower disease prevalence, risks associated with their invasiveness, and healthcare expenses. Serological tumor markers used for noninvasive diagnostics including carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9), CA724, CA125, and alpha-fetoprotein (AFP), are inadequate for GC detection owing to low specificity and sensitivity, especially for early-stage cancers. As a result, there is an unmet clinical need for cost-effective and less invasive approaches to the early detection of patients with GC.

[0003] Liquid biopsies, including circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), RNAs, exosomes, and tumor-educated platelets, have shown tremendous potential for cancer diagnosis, recurrence prediction, and therapeutic response monitoring. MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play an important role in cellular differentiation, proliferation, and survival. In addition to their stability in serum and plasma, the aberrant expression of miRNAs has frequently been shown to correlate with tumor initiation and progression, making miRNAs promising and attractive substrates for biomarker development. Nevertheless, the origin of circulating cell-free miRNAs (cf-miRNAs) remains controversial, as it is debated that these may not always originate exclusively from the tumor cells and might be contributed by the inflammatory and immune cells within the tumor microenvironment (TME). Therefore, it is essential to identify and develop more cancer-specific biomarker candidates for the clinical translation as disease biomarkers. The disclosure is directed to these, as well asother, important ends.BRIEF SUMMARY

[0004] Provided herein are methods of detecting RNA biomarkers in a patient having, or suspected of having, gastric cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p. miR-335-3p. miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p. miR-183-5p. miR-135b-5p, or a combination of two or more thereof.

[0005] Provided herein are methods of treating gastric cancer in a patient in need thereof, the method comprising: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from a patient, wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent.

[0006] Provided herein are methods of diagnosing a patient with gastric cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, thereby diagnosing the patient with gastric cancer; wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

[0007] Provided herein are methods of monitoring a patient at risk for developing gastric cancer, the method comprising: (i) detecting an expression level of RNA in a biological sample obtained from the patient at a first point in time; (ii) detecting an expression level of RNA in a biological sample obtained from the patient at a second point in time, wherein the second point in time is later than the first point in time; wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

[0008] These and other embodiments of the disclosure are described herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIGS. 1A-1G: Genome-wide discovery7of miRNA candidates for detection of gastric cancer. FIG. 1A: Volcano plots depict differentially expressed miRNAs (DEMs) between GC tissue and matched adjacent normal (AN) tissues log2foldchange (log2FC)| > 0.5 and p < 0.01). FIG. IB: Volcano plots depict differentially expressed cell-free (cf) miRNAs between GC andnon-disease controls (NDCs) (log2FC > 1 and p < 0.01). FIG. 1C: Volcano plots depict differentially expressed exosomal (exo) miRNAs between GC and NDCs (log2FC > 1 and p < 0.01). FIG. ID: Venn diagram shows the overlapping DEMs among tissue, cell-free, and exosomes. FIG. IE: The heatmap depicts the 8 overlapping upregulated miRNAs between tissue and cell-free. FIG. IF: The heatmap depicts the 10 overlapping upregulated miRNAs between tissue and exosomes. FIG. 1G: Receiver operating characteristic curve (ROC) curve analysis evaluates the performance of 8 cf-miRNA panels and 10 exo-miRNA panels that robustly distinguished GC from the NDCs. ROC curves are shown as 95% Cis. NDCs: Nondisease controls; GC: Gastric cancer; AUC: Area under the curve

[0010] FIGS. 2A-2F : Development of a non-invasive miRNA-based signature for detection of patients with GC. FIG. 2A: ROC curve analysis evaluates the performance of the 6 cf-miRNA panel and 7 exo-miRNA panel in the training cohort. ROC curves are shown as 95% Cis. FIG. 2B: The risk score calculated from the 6 cf-miRNA (top) and 7 exo-miRNA panel (bottom) was shown in the validation cohort. FIG. 2C: ROC analysis evaluates the performance of 6 cf- miRNA panel and 7 exo-miRNA panel in the validation cohort. ROC curves are shown as 95% Cis. FIG. 2D: ROC curve analysis illustrates the performance of the 13-miRNA combination signature in the training and validation cohort. ROC curves are shown as 95% Cis. FIG. 2E: The sensitivity and specificity of 6 cf-miRNA panels, 7 exo-miRNA panels, and their combination signature were shown in the validation cohort. FIG. 2F: The decision curve shows the net benefit for the cf-miRNA, exo-miRNA, and combination signature in patients with GC from the validation cohort. * p < 0.05 ** p < 0.01 *** p < 0.001 cf-miRNA: cell-free miRNA; exo-miRNA: exosomal miRNA

[0011] FIGS. 3A-3F: Construction of a clinically feasible signature for non-invasive detection of patients with GC. FIG. 3A: ROC curve analysis illustrates the performance of DESTINEX in the training and validation cohort. FIG. 3B: Confusion matrix of DESTINEX in the validation cohort. FIG. 3C: The sensitivity and specificity of 5 cf-miRNA panels, 5 exo-miRNA panels, and DESTINEX were shown in the validation cohort. FIG. 3D: The comparison of AUC values and accuracy between DESTINEX and 13-miRNA combination signature was shown in the validation cohort. FIG. 3E: Decision curve shows the net benefit for DESTINEX and 13- miRNA combination signature in patients with GC from the validation cohort. FIG. 3F: Calibration curve of DESTINEX and 13-miRNA signature in patients with GC from validation cohort. The dashed grey line is the ideal line. The triangle sign indicates the grouped observations. The short line above and below the horizontal axis represents the positive and negative cases. DESTINEX: Detection of gastric cancer by combining 5 cf- and exo-miRNAs;** p < 0.01, *** p < 0.001, ns: not significant

[0012] FIGS. 4A-4G: The non-invasive DESTINEX efficiently identifies patients with early gastric cancer. FIG. 4A: Risk scores calculated from DESTINEX are analyzed in NDCs. early- stage, and advanced-stage GC patients from the validation cohort. FIG. 4B: ROC curve analysis reveals the performance of DESTINEX in patients with the pTl stage and pT2-4 stages from the validation cohort. FIG. 4C: Risk score levels of DESTINEX in NDCs, I-II stage, and III-IV stage GC patients from the validation cohort. FIG. 4D: ROC curve analysis reveals the performance of DESTINEX in patients with the I-II stage and III-IV stage from the validation cohort. ROC curves are shown as 95% Cis. FIG. 4E: The forest plot details the subgroup analysis of sensitivity and 95% Cis of DESTINEX. The green-filled squares represent point estimates of sensitivity, and horizontal lines indicate the 95% Cis. FIG. 4F: Assessment of risk probability based on DESTINEX between pre- and post-surgery GC samples. FIG. 4G: ROC curve analysis shows the performance of DESTINEX in different gastrointestinal malignancies (GC, CRC, PDAC, ESCC, ICC, HCC). DESTINEX: Detection of gastric cancer by combining 5 cf- and exo-miRNAs; LVI: Lymphovascular invasion: CRC: Colorectal cancer; PDAC: Pancreatic ductal adenocarcinomas cancer; ESCC: Esophageal squamous cell carcinoma; ICC: Intrahepatic cholangiocarcinoma; HCC: Hepatocellular carcinoma; *** p < 0.001

[0013] FIG. 5: Workflow of the study design for developing a non-invasive miRNA panel for the detection of GC. GC: Gastric cancer; NDCs: Non-disease

[0014] FIGS. 6A-6C: Construction of a non-invasive miRNA-based signature for the identification of patients with GC. FIG. 6A: The risk score calculated from the 6 cf-miRNA (top) and 7 exo-miRNA panel (bottom) was shown in the training cohort. FIG. 6B: The sensitivity and specificity of 6 cf-miRNA panel. 7 exo-miRNA panel, and their combination signature were shown in the training cohort. FIG. 6C: The waterfall plot exhibits the risk probability distribution of the combined 13-miRNA signature between serum samples from GC patients and NDCs in the training (top) and validation cohort (bottom).

[0015] FIGS. 7A-7D: Development of clinically feasible cell-free and exosomal panel for non-invasive detection of patients with GC. FIG. 7A: The risk score calculated from the 5 cf- miRNA (top) and 5 exo-miRNA panel (bottom) was shown in the training cohort. FIG. 7B: The risk score calculated from the 5 cf-miRNA (top) and 5 exo-miRNA panel (bottom) was shown in the validation cohort. FIG. 7C: ROC curve analysis evaluates the performance of the 5 cf- miRNA panel and 5 exo-miRNA panel in the training cohort. ROC curves are shown as 95% Cis. FIG. 7D: ROC curve analysis evaluates the performance of the 5 cf-miRNA panel and 5 exo-miRNA panel in the validation cohort. ROC curves are shown as 95% Cis. *** p < 0.001

[0016] FIGS. 8A-8C: Construction of a clinically viable signature for non-invasive detection of patients with GC. FIG. 8A: The waterfall plot depicts the risk probability distribution of DESTINEX between serum samples from GC patients and NDCs in the training (top) and validation cohort (bottom). FIG. 8B: The sensitivity and specificity of 5 cf-miRNA panels, 5 exo-miRNA panels, and DESTINEX were shown in the training cohort. FIG. 8C: The comparison of AUC values and accuracy between DESTINEX and 13-miRNA combination signature was shown in the training cohort. DESTINEX: Detection of gastric cancer by combining 5 cf- and exo-miRNAs; ** p < 0.01 *** p < 0.001 ns: not significant.

[0017] FIG. 9: The 10 individual miRNAs exhibited robust specificity for detecting patients with GC. Expression levels of miRNA candidates in pre- and post-surgery serum samples from an independent cohort. * p < 0.05 ** p < 0.01 *** > < 0.001 ns: not significant

[0018] FIGS. 10A-10F: Development of a non-invasive miRNA-based signature for detection of patients with GC. FIG. 10A. The density plots of the cf- and exo-miRNA panels for GC (in red) and the non-disease controls (NDCs, in blue) demonstrate an apparent clustering of the GC cases on the top-right comer of the diagram, while controls clustered in the bottom-left comer. FIG. 10B: ROC analysis evaluates the performance of 8 cf-miRNA panel and 9 exo-miRNA panel in the training cohort. ROC curves are shown as 95% Cis. FIGS. 10C-10D: The risk score calculated from the 17-miRNA signature, based on the machine learning model, was shown in the training (c, left) and validation cohort (d, left). ROC curve analysis illustrates the performance of the 17-miRNA combination signature in the training (c, right) and validation cohort (d, right). ROC curves are shown as 95% Cis. FIG. 10E: Odds ratios for GC with restricted cubic splines as a function of risk score values (95% confidence intervals for Odds Ratios are presented as dashed lines). FIG. 10F: The decision curve shows the net benefit for the cf-miRNA. exo-miRNA. and combination signature in patients with GC from the validation cohort. * p < 0.05 ** p < 0.01 *** p < 0.001 cf-miRNA: cell-free miRNA; exo-miRNA: exosomal miRNA

[0019] FIGS. 11A-11G: Construction of a clinically feasible signature for non-invasive detection of patients with GC. FIG. 11A: Shapley Additive Explanations (SHAP) summary plot for the extreme Gradient Boosting (XGBoost) model illustrates the feature importance ranked in descending order on the Y-axis, with the SHAP values represented on the X-axis. FIGS. 11B- 11C: ROC curve analysis illustrates the performance of DESTINEX in the training (b) and validation cohort (c). FIG. 11D: The sensitivity and specificity of 5 cf-miRNA panel, 5 exo- miRNA panel, and DESTINEX were shown in the validation cohort. FIG. HE: The comparison of accuracy, precision, recall, and Fl score between DESTINEX and the 17-miRNAcombination signature is shown in the validation cohort. FIG. 11F: The decision curve analysis reveals the net benefit of DESTINEX and 17-miRNA combination signatures in patients with GC from the validation cohort. FIG. 11G: Calibration curves for DESTINEX and 17-miRNA signature in patients wi th GC from validation cohort. The dashed grey line is the ideal line. The triangle sign indicates the grouped observations. The short line above and below the horizontal axis represents the positive and negative cases. DESTINEX: Detection of gastric cancer by combining 5 cf- and exo-miRNAs; ** p < 0.01. *** p < 0.001, ns: not significant

[0020] FIGS. 12-12F: The non-invasive DESTINEX efficiently identifies patients with early gastric cancer. FIG. 12A: Risk scores calculated from DESTINEX are analyzed in NDCs, early-stage, and advanced-stage GC patients from the validation cohort. FIG. 12B: ROC curve analysis reveals the performance of DESTINEX in patients with the pTl stage and pT2-4 stages from the validation cohort. FIG. 12C: Risk score levels of DESTINEX in NDCs, I-II stage, and III-IV stage GC patients from the validation cohort. FIG. 12D: ROC curve analysis reveals the performance of DESTINEX in patients with the I-II stage and III-IV stage from the validation cohort. ROC curves are shown as 95% Cis. FIG. 12E: Assessment of risk probability based on DESTINEX between pre- and post-surgery GC samples. FIG. 12F: ROC curve analysis shows the performance of DESTINEX in different gastrointestinal malignancies (GC, CRC, PDAC, ESCC, ICC, HCC). DESTINEX: Detection of gastric cancer by combining 5 cf- and exo- miRNAs; LVI: Lymphovascular invasion; CRC: Colorectal cancer; PDAC: Pancreatic ductal adenocarcinomas cancer; ESCC: Esophageal squamous cell carcinoma; ICC: Intrahepatic cholangiocarcinoma; HCC: Hepatocellular carcinoma; *** p < 0.001

[0021] FIGS. 13A-13B: construction of a non-invasive miRNA-based signature for the identification of patients with GC. The waterfall plot exhibits the risk probability distribution of the combined 17-miRNA signature between serum samples from GC patients and NDCs in the training (FIG. 13A) and validation cohort (FIG. 13B).

[0022] FIG. 14: the forest plot details the subgroup analysis of sensitivity and 95% Cis of DESTINEX. The squares represent point estimates of sensitivity7, and horizontal lines indicate the 95% Cis

[0023] FIGS. 15A-15B are a summary of the diagnostic performance of individual cell free miRNAs (FIG. 15A) and exosomal miRNAs (FIG. 15B) in the training cohort described in the example in Analysis A.

[0024] FIGS. 16A-16B are a summary of the diagnostic performance of individual cell free miRNAs (FIG. 16A) and exosomal miRNAs (FIG. 16B) in the training cohort described in the example in Analysis B.

[0025] FIG. 17 is a table showing the clinicopathological characteristics enrolled patients in the training and validation cohorts for Analysis A and Analysis B.DETAILED DESCRIPTION

[0026] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art. See, e.g., Singleton et al., Dictionary of Microbiology and Molecular Biology. 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al., Molecular Cloning. A Laboratory Manual. Cold Springs Harbor Press (Cold Springs Harbor, NY 1989). Any methods, devices and materials similar or equivalent to those described herein can be used in the practice of this disclosure. The following definitions are provided to facilitate understanding of certain terms used frequently herein and are not meant to limit the scope of the present disclosure.

[0027] The term '‘gastric cancer" is known in the art and alternatively referred to as stomach cancer. Gastric cancer can be Tl, T2, T3, or T4 based on the American Joint Committee on Cancer TNM system. The T category describes the extent of the main (primary) tumor, including how far it has grown into the layers of the stomach wall and if it has reached nearby structures or organs. The N category describes any cancer spread to nearby lymph nodes. The M category describes any spread (metastasis) to distant parts of the body, such as the liver or lungs. In embodiments, the gastric cancer is gastric adenocarcinoma.

[0028] The term “early gastric cancer” or “early GC” or “Tl gastric cancer” refers to a gastric cancer that only invades the mucosal or submucosal layer (stage Tl). regardless of whether lymph node metastasis is present. In embodiments, early gastric cancer is gastric cancer that only invades the mucosal layer (Tl a gastric cancer). In embodiments, early gastric cancer is Tla stage gastric cancer. In embodiments, early gastric cancer is gastric cancer that only invades the submucosal layer (Tib gastric cancer). In embodiments, early gastric cancer is Tib stage gastric cancer. In embodiments, the gastric cancer is gastric adenocarcinoma. In embodiments, the early gastric cancer is early gastric cancer with lymph node metastasis. In embodiments, the early gastric cancer is early gastric cancer without lymph node metastasis.

[0029] As used herein, the term “tumor-derived exosome” or “exosome” refers to a small (between 20-300 nm in diameter) vesicle comprising a lipid bilayer membrane that encloses an internal space, and which is generated from a cancer cell by direct plasma membrane budding or by fusion of the late endosome w ith the plasma membrane. The components of tumor-derived exosomes include proteins, DNA, mRNA. microRNA, long noncoding RNA, circular RNA, and the like, which play a role in regulating tumor growth, metastasis, and angiogenesis in the process of cancer development.

[0030] “Exosomal RNA” refers to RNA within a tumor-derived exosome or RNA obtained from within a tumor-derived exosome. In embodiments, "exosomal RNA" is exosomal miRNA. Exosomal RNA can be detected and measured by methods known in the art, such as those described herein.

[0031] “Cell-free RNA” or “cf-RNA” refers to RNA that is not within a tumor-derived exosome or RNA that has not been obtained from within a tumor-derived exosome. In embodiments, “cell-free RNA” is cell-free miRNA. Cell-free RNA can be detected and measured by methods known in the art, such as those described herein.

[0032] A “cell” refers to a cell carrying out metabolic or other function sufficient to preserve or replicate its genomic DNA. A cell can be identified by well-known methods in the art including, for example, presence of an intact membrane, staining by a particular dye. ability to produce progeny or, in the case of a gamete, ability to combine w ith a second gamete to produce a viable offspring. Cells may include prokaryotic and eukaryotic cells. Prokaryotic cells include but are not limited to bacteria. Eukaryotic cells include but are not limited to yeast cells and cells derived from plants and animals, for example mammalian (e.g. human) cells. Cells may be useful when they are naturally nonadherent or have been treated not to adhere to surfaces, for example by trypsinization.

[0033] “Nucleic acid” refers to nucleotides (e.g., deoxyribonucleotides or ribonucleotides) and polymers thereof in either single-, double- or multiple-stranded form, or complements thereof; or nucleosides (e.g., deoxyribonucleosides or ribonucleosides). In embodiments, “nucleic acid” does not include nucleosides. The terms “polynucleotide,” “oligonucleotide,” “oligo” or the like refer, in the usual and customary' sense, to a linear sequence of nucleotides. The term “nucleoside” refers, in the usual and customary sense, to a glycosylamine including a nucleobase and a five-carbon sugar (ribose or deoxyribose). Non limiting examples, of nucleosides include, cytidine, uridine, adenosine, guanosine, thymidine and inosine. The term “nucleotide” refers, in the usual and customary sense, to a single unit of a polynucleotide, i.e., a monomer. Nucleotides can be ribonucleotides, deoxyribonucleotides, or modified versions thereof. Examples of polynucleotides contemplated herein include single and double stranded DNA, single and double stranded RNA, and hybrid molecules having mixtures of single and double stranded DNA and RNA. Examples of nucleic acid, e.g. polynucleotides, contemplated herein include any ty pes of RNA, e.g. mRNA, siRNA, miRNA, and guide RNA and any types of DNA, genomic DNA. plasmid DNA, and minicircle DNA, and any fragments thereof. The term “duplex” in the context of poly nucleotides refers, in the usual and customary sense, to double strandedness. Nucleic acids can be linear or branched. For example, nucleic acids can be a linearchain of nucleotides or the nucleic acids can be branched, e g., such that the nucleic acids comprise one or more arms or branches of nucleotides. Optionally, the branched nucleic acids are repetitively branched to form higher ordered structures such as dendrimers and the like.

[0034] A polynucleotide is typically composed of a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); and thymine (T) (uracil (U) for thymine (T) when the polynucleotide is RNA). Thus, the term “polynucleotide sequence” is the alphabetical representation of a polynucleotide molecule; alternatively, the term may be applied to the polynucleotide molecule itself. This alphabetical representation can be input into databases in a computer having a central processing unit and used for bioinformatics applications such as functional genomics and homology searching. Polynucleotides may optionally include one or more non-standard nucleotide(s), nucleotide analog(s) and / or modified nucleotides.

[0035] A “microRNA,” “microRNA nucleic acid sequence,” “miR,” “miRNA” as used herein, refers to a nucleic acid that functions in RNA silencing and post-transcriptional regulation of gene expression. The term includes all forms of a miRNA, such as the pri-, pre-, and mature forms of the miRNA. In embodiments, microRNAs (miRNAs) are short (20-24 nt) non-coding RNAs that are involved in post-transcriptional regulation of gene expression in multicellular organisms by affecting both the stability and translation of mRNAs. miRNAs are transcribed by RNA polymerase II as part of capped and polyadenylated primary transcripts (pri-miRNAs) that can be either protein-coding or non-coding. The primary’ transcript is cleaved by the Drosha ribonuclease III enzyme to produce an approximately 70-nt stem-loop precursor miRNA (pre- miRNA), which is further cleaved by the cytoplasmic Dicer ribonuclease to generate the mature miRNA and antisense miRNA star (miRNA*) products. The mature miRNA is incorporated into a RNA-induced silencing complex (RISC), which recognizes target mRNAs through imperfect base pairing with the miRNA and most commonly results in translational inhibition or destabilization of the target mRNA. In embodiments of the methods described herein, the microRNA is human microRNA, which can be abbreviated as h-miR. When the patient is human, the methods described herein are for detecting h-miR and the RNA comprises h-miR.

[0036] The term “complement,” as used herein, refers to a nucleotide (e.g.. RNA or DNA) or a sequence of nucleotides capable of base pairing with a complementary nucleotide or sequence of nucleotides. As described herein and commonly known in the art the complementary (matching) nucleotide of adenosine is thymidine and the complementary (matching) nucleotide of guanosine is cytosine. Thus, a complement may include a sequence of nucleotides that base pair with corresponding complementary nucleotides of a second nucleic acid sequence. The nucleotides of a complement may partially or completely match the nucleotides of the secondnucleic acid sequence. Where the nucleotides of the complement completely match each nucleotide of the second nucleic acid sequence, the complement forms base pairs with each nucleotide of the second nucleic acid sequence. Where the nucleotides of the complement partially match the nucleotides of the second nucleic acid sequence only some of the nucleotides of the complement form base pairs with nucleotides of the second nucleic acid sequence. Examples of complementary sequences include coding and a non-coding sequences, wherein the non-coding sequence contains complementary nucleotides to the coding sequence and thus forms the complement of the coding sequence.

[0037] The term “gene” means the segment of DNA involved in producing a protein; it includes regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons). The leader, the trailer as well as the introns include regulatory elements that are necessary during the transcription and the translation of a gene. Further, a “protein gene product” is a protein expressed from a particular gene.

[0038] The word “expression” or “expressed” as used herein in reference to a gene means the transcriptional and / or translational product of that gene. The level of expression of a DNA molecule in a cell may be determined on the basis of either the amount of corresponding RNA that is present within the cell or the amount of protein encoded by that DNA produced by the cell. The level of expression of non-coding nucleic acid molecules (e.g., miRNA, mRNA) may be detected by standard PCR or Northern blot methods well known in the art. See, Sambrook et al., 1989 Molecular Cloning: A Laboratory Manual, 18.1-18.88.

[0039] The terms “expression level,” “amount,” or “level” of a biomarker is a detectable level in a biological sample. “Expression” generally refers to the process by which information (e.g., gene-encoded and / or epigenetic) is converted into the structures present and operating in the cell. Therefore, “expression” may refer to transcription into a polynucleotide, translation into a polypeptide, or even polynucleotide and / or polypeptide modifications (e.g., posttranslational modification of a polypeptide). Fragments of the transcribed polynucleotide, the translated polypeptide, or polynucleotide and / or polypeptide modifications (e.g., post-translational modification of a polypeptide) shall also be regarded as expressed whether they originate from a transcript generated by alternative splicing or a degraded transcript, or from a post-translational processing of the polypeptide, e.g., by proteolysis. “Expressed genes” include those that are transcribed into a polynucleotide as mRNA and then translated into a polypeptide, and also those that are transcribed into RNA but not translated into a polypeptide (for example, miRNA, transfer RNA, ribosomal RNA, non-coding RNA). Expression levels can be measured bymethods known to one skilled in the art and also disclosed herein. The expression level or amount of a biomarker (e.g., RNA, miRNA) can be used to diagnose, treat, and / or monitor a subject with gastric cancer.

[0040] The terms an “elevated expression level” or “elevated level” of biomarker expression is an expression level of the biomarker that is higher than the expression level of the biomarker in a control. The control may be any suitable control, as described herein. In embodiments, an “elevated expression level” of the biomarker compared to the control (when the expression level of the biomarker is greater than the corresponding control) is, for example, an increase in the expression level of about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98% or 99% or greater relative to the control. In embodiments, an “elevated expression level” of the biomarker is an amount that is statistically significantly greater than the expression level of the control.

[0041] The terms “does not have an elevated expression level” or an expression level that is “not elevated” is an expression level of the gene that is about the same as (or lower than) the expression level of the biomarker in a control. The control may be any suitable control, as described herein. In embodiments, “about the same as” is + / - 25% of the expression level of the biomarker in a control. In embodiments, “about the same as” is + / - 20% of the expression level of the biomarker in a control. In embodiments, “about the same as” is + / - 15% of the expression level of the biomarker in a control. In embodiments, “about the same as” is + / - 10% of the expression level of the biomarker in a control. In embodiments, “about the same as” is + / - 5% of the expression level of the biomarker in a control. In embodiments, an expression level of the biomarker that is “not elevated” or “unelevated” is an amount that is not statistically significantly different than the expression level of the control.

[0042] The term “biomarkef ’ is used in accordance with its plain and ordinary meaning. In embodiments, a biomarker is a gene or a set of genes (i.e., a biomarker gene). Biomarkers include, but are not limited to, polynucleotides (e.g., DNA, and / or RNA), polynucleotide copy number alterations (e.g., DNA copy numbers), polypeptides, or polypeptide and polynucleotide modifications (e.g., posttranslational modifications). In embodiments, a biomarker refers to RNA (e.g., miRNA), the expression level of which is associated with a particular biological state, particularly a state associated with gastric cancer. In embodiments, a biomarker refers to miRNA.

[0043] Biomarker levels may be detected at either the protein or gene expression level. Proteins expressed by biomarkers can be quantified by immunohistochemistry (IHC) or flow cytometry with an antibody that detects the proteins. Biomarker expression can be quantified bymultiple platforms such as real-time polymerase chain reaction (rtPCR), Nanostring, RNAseq, or in situ hybridization. There is a range of biomarker expression across as measured by Nanostring. In embodiments, quantitative rtPCR, Nanostring, RNAseq, and in situ hybridization are platforms to quantitate biomarker gene expression. For Nanostring, RNA is extracted from a biological sample and a known quantity of RNA is placed on the Nanostring machine for gene expression detection using gene specific probes. The number of counts of biomarkers within a sample is determined and normalized to a set of housekeeping genes. To determine a threshold for increased or decreased biomarker levels, one skilled in the art could assess biomarker levels in a control group of samples and select the 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80thor 90thpercentile of biomarker gene expression. In embodiments, the increased or decreased expression of biomarkers may be determined by calculating the H-score for the expression of the biomarkers. Thus, the increased or decreased expression of biomarkers may have an H-score. As used herein, an “H-score” or “Histoscore” is a numerical value determined by a semi- quantitative method commonly known for immunohistochemically evaluating protein expression in tumor samples. The H-score may be calculated using the following formula: [1 x (% cells 1+) + 2 x (% cells 2+) + 3 x (% cells 3+)]. According to this formula, the H-score is calculated by determining the percentage of cells having a given staining intensity level (i.e., level 1+, 2+, or 3+ from lowest to highest intensity level), w eighting the percentage of cells having the given intensity level by multiplying the cell percentage by a factor (e.g., 1, 2, or 3) that gives more relative weight to cells with higher-intensity membrane staining, and summing the results to obtain a H-score. Commonly H-scores range from 0 to 300. Further description on the determination of H-scores in tumor cells can be found in Hirsch et al, J Clin Oncol 21: 3798- 3807, 2003 and John et al, Oncogene 28:S14-S23. 2009. IHC or other methods known in the art may be used for detecting biomarker expression.

[0044] ‘ ‘Control” is used in accordance with its plain ordinary meaning and refers to an assay, comparison, or experiment in which the subj ects or reagents of the experiment are treated as in a parallel experiment except for omission of a procedure, reagent, or variable of the experiment. In embodiments, the control is used as a standard of comparison in evaluating experimental effects. In embodiments, a control is the measurement of the activity or level of RNA. In embodiments, a control is a healthy patient or a healthy population of patients. In embodiments, a control is an average value from a population of similar patients, e.g., healthy patients with a similar medical background, age. w eight, etc. In embodiments, the control is a healthy patient or a population of healthy patients. In embodiments, a healthy patient can be referred to as a non-diseased patient or non-diseased control. In embodiments, the control is a population of non-diseased patients. Inembodiments, a non-diseased patient is a patient that does not have cancer. In embodiments, a non-diseased patient is a patient that does not have gastric cancer. In embodiments, the control is a patient that does not have cancer or a population of patients that do not have cancer. In embodiments, the control is a patient that does not have gastric cancer or a population of patients that do not have gastric cancer. In embodiments, the control is a patient that does not have gastric cancer or a population of patients that do not have gastric cancer. In embodiments, the control is an average value from population of healthy patients. A control can also be obtained from the same patient, e.g., from an earlier-obtained sample, prior to disease, or prior to treatment. One of skill will recognize that controls can be designed for assessment of any number of parameters. In embodiments, a control is a negative control. In embodiments, such as some embodiments relating to detecting the level of expression of a gene / protein or a subset of genes / proteins, a control comprises the average amount of expression (e g., protein or miRNA) in a population of subjects (e.g., with cancer) or in a healthy or general population. In embodiments, the control comprises an average amount (e.g. amount of expression) in a population in which the number of subjects (n) is 5 or more, 20 or more, 50 or more, 100 or more, 1,000 or more, and the like. In embodiments, a control is a level of expression of the biomarker (e.g., RNA, miRNA) that has been correlated with the diagnosis of gastric cancer in a subject. In embodiments, a control is a level of expression of the biomarker (e.g., RNA, miRNA) that has been correlated with ahealthy subject (i.e., a subject that does not have gastric cancer). One of skill in the art will understand which controls are valuable in a given situation and be able to analyze data based on comparisons to control values. Controls are also valuable for determining the significance of data. For example, if values for a given parameter are w idely variant in controls, variation in test samples will not be considered as significant.

[0045] The term “NDC” or "non-diseased control" or "healthy patient” refer to a non-diseased patient. In embodiments, a healthy patient is a patient that does not have cancer. In embodiments, a healthy patient is a patient that does not have gastric cancer. In embodiments, a control is a non-diseased control.

[0046] The phrase “specifically (or selectively) binds” to an antibody or “specifically (or selectively) immunoreactive with,” when referring to a protein or peptide refers to a binding reaction that is determinative of the presence of the protein, often in a heterogeneous population of proteins and other biologies. Thus, under designated immunoassay conditions, the specified antibodies bind to a particular protein at least two times the background and more typically more than 10 to 100 times background. Specific binding to an antibody under such conditions requires an antibody that is selected for its specificity for a particular protein. For example, polyclonalantibodies can be selected to obtain only a subset of antibodies that are specifically immunoreactive with the selected antigen and not with other proteins. This selection may be achieved by subtracting out antibodies that cross-react with other molecules. A variety of immunoassay formats may be used to select antibodies specifically immunoreactive with a particular protein. For example, solid-phase ELISA immunoassays are routinely used to select antibodies specifically immunoreactive with a protein (see, e.g., Harlow & Lane. Using Antibodies, A Laboratory Manual (1998) for a descnption of immunoassay formats and conditions that can be used to determine specific immunoreactivity).

[0047] The terms “isolate” or “isolated”, when applied to a nucleic acid, virus, or protein, denotes that the nucleic acid, virus, or protein is essentially free of other cellular components with which it is associated in the natural state. It can be. for example, in a homogeneous state and may be in either a dry or aqueous solution. Purity and homogeneity are typically determined using analytical chemistry techniques such as polyacrylamide gel electrophoresis or high performance liquid chromatography. An RNA that is the predominant species present in a preparation is substantially purified.

[0048] The term “about” means a range of values including the specified value, which a person of ordinary skill in the art would consider reasonably similar to the specified value. In embodiments, “about” means within a standard deviation using measurements generally acceptable in the art. In embodiments, “about” means a range extending to + / - 10% of the specified value. In embodiments, “about” includes the specified value.

[0049] The singular terms “a,” “an,” and “the” include the plural reference unless the context clearly indicates otherwise.

[0050] A “therapeutic agent” or “anticancer agent” as used herein refer to an agent (e.g., compound, pharmaceutical composition) that when administered to a subject will have the intended therapeutic effect, e.g., treatment or amelioration of gastric cancer, or their symptoms including any objective or subjective parameter of treatment such as abatement; remission; diminishing of symptoms or making the cancer more tolerable to the patient; slowing in the rate of degeneration or decline; making the final point of degeneration less debilitating; or improving a patient’s physical or mental well-being.

[0051] “Biological sample” or “sample” refer to materials obtained from or derived from a subject or patient. A biological sample includes sections of tissues such as biopsy and autopsy samples, and frozen sections taken for histological purposes. Such samples include bodily fluids such as blood and blood fractions or products (e.g., serum, plasma, platelets, red blood cells, and the tike), sputum, tissue, cultured cells (e g., primary cultures, explants, and transformed cells)stool, urine, synovial fluid, joint tissue, synovial tissue, synoviocytes, fibroblast-like synoviocytes, macrophage-like synoviocytes, immune cells, hematopoietic cells, fibroblasts, macrophages, T cells, etc. In embodiments, a biological sample is blood. In embodiments, a biological sample is a serum sample (e.g., the fluid and solute component of blood without the clotting factors). In embodiments, a biological sample is a plasma sample (e.g, the liquid portion of blood). In embodiments, a biological sample is cell-free RNA obtained from blood. In embodiments, a biological sample is an exosome obtained from a blood sample, wherein the exosome comprises RNA. In embodiments, a biological sample is an exosome obtained from a serum sample, wherein the exosome comprises RNA. In embodiments, a biological sample is an exosome obtained from a plasma sample, wherein the exosome comprises RNA.

[0052] “Liquid biological sample’7refers to liquid materials obtained or derived from a subject or patient. Liquid biological samples include bodily fluids such as blood and blood fractions or products (e.g., serum, plasma, platelets, red blood cells, and the like), sputum, urine, synovial fluid, and the like. In embodiments, a liquid biological sample is a blood sample.

[0053] The term “diagnosis” is used in accordance with its plain and ordinary meaning and refers to an identification or likelihood of the presence of a disease (e.g., gastric cancer) or outcome in a subject.

[0054] “Image-based screening” refers to methods using imaging technology to detect a cancer or tumor in a patient. Exemplary types of image-based screening include x-rays, computed tomography (CT), magnetic resonance imaging (MRI). positron emission tomography (PET), and ultrasound. In embodiments of the methods described herein, the image-based screening is CT, MRI, or ultrasound. In embodiments, the ultrasound is endoscopic ultrasonography (EUS). In embodiments of the methods described herein, the image-based screening is CT, MRI, or EUS. In embodiments of the methods described herein, the imagebased screening is MRI or EUS. In embodiments of the methods described herein, the imagebased screening is CT. In embodiments of the methods described herein, the image-based screening is MRI. In embodiments of the methods described herein, the image-based screening is EUS.

[0055] The terms “treating” or “treatment” are used in accordance with their plain and ordinary meaning and broadly includes any approach for obtaining beneficial or desired results in a subject’s condition, including clinical results. Beneficial or desired clinical results can include, but are not limited to, alleviation or amelioration of one or more symptoms or conditions, diminishment of the extent of a disease, stabilizing (i.e.. not worsening) the state of disease, delay or slowing of disease progression, amelioration or palliation of the disease state.and remission, whether partial or total and whether detectable or undetectable. Treatment may inhibit the disease’s spread; relieve the disease’s symptoms, fully or partially remove the disease’s underlying cause, shorten a disease’s duration, or do a combination of these things. Treatment methods include administering to a subject a therapeutically effective amount of an active agent. The term “treating” does not including preventing.

[0056] An “effective amount” is an amount sufficient to accomplish a stated purpose (e.g. achieve the effect for which it is administered, treat a disease). An example of an “effective amount” is an amount sufficient to contribute to the treatment, prevention, or reduction of a symptom or symptoms of a disease, which could also be referred to as a “therapeutically effective amount.” A “reduction” of a symptom or symptoms (and grammatical equivalents of this phrase) means decreasing of the severity or frequency of the symptom(s), or elimination of the symptom(s). The exact amounts will depend on the purpose of the treatment, and will be ascertainable by one skilled in the art using known techniques. In embodiments, “therapeutically effective amount” refers to the amount of the therapeutic agent sufficient to treat or ameliorate gastric cancer, as described above. For any therapeutic agent described herein, the therapeutically effective amount can be initially determined from cell culture assays. Target concentrations will be those concentrations of active compound(s) that are capable of achieving the methods described herein, as measured using the methods described herein or known in the art. As is well known in the art, therapeutically effective amounts for use in humans can also be determined from animal models. For example, a dose for humans can be formulated to achieve a concentration that has been found to be effective in animals. The dosage in humans can be adjusted by monitoring compounds effectiveness and adjusting the dosage upwards or downwards, as described above. Adjusting the dose to achieve maximal efficacy in humans based on the methods described above and other methods is well within the capabilities of the ordinarily skilled artisan. Dosages may be varied depending upon the requirements of the patient and the therapeutic agent being employed. The dose administered to a patient should be sufficient to effect a beneficial therapeutic response in the patient over time. The size of the dose also will be determined by the existence, nature, and extent of any adverse side-effects. Determination of the proper dosage for a particular situation is within the skill of the practitioner. Generally, treatment is initiated with smaller dosages which are less than the optimum dose of the compound. Thereafter, the dosage is increased by small increments until the optimum effect under circumstances is reached. Dosage amounts and intervals can be adjusted individually to provide levels of the administered compound effective for the particular clinical indication being treated. This will provide a therapeutic regimen that is commensuratewith the severity of the patient's disease state. A “therapeutically effective amount'’ can also be found on the label or Prescribing Information for commercially available therapeutic agents.

[0057] The term “administering” means oral administration, administration as a suppository, topical contact, intravenous, parenteral, intraperitoneal, intramuscular, intralesional, intrathecal, intranasal or subcutaneous administration, or the implantation of a slow-release device, e.g., a mini-osmotic pump, to a subject. Administration is by any route, including parenteral and transmucosal (e.g.. buccal, sublingual, palatal, gingival, nasal, vaginal, rectal, or transdermal). Parenteral administration includes, e.g., intravenous, intramuscular, intra-arteriole, intradermal, subcutaneous, intraperitoneal, intraventricular, and intracranial. Other modes of delivery7include, but are not limited to, the use of liposomal formulations, intravenous infusion, transdermal patches, etc. In embodiments, the administering does not include administration of any active agent other than the recited active agent.

[0058] The terms “patient” or “subject” are used in accordance with its plain and ordinary meaning and refer to a living organism suffering from or prone to a disease that can be treated by administration of a pharmaceutical composition, such as anti-cancer agents and chemotherapeutic agents. Non-limiting examples include humans, other mammals, bovines, rats, mice, dogs, cats, monkeys, and other non-mammalian animals. In embodiments, a patient is human. In embodiments, the human patient has gastric cancer. In embodiments, the human patient has early gastric cancer. In embodiments, the human patient has a family history of gastric cancer.

[0059] The terms “metastasis,” “metastatic,” and “metastatic cancer” can be used interchangeably and refer to the spread of a proliferative disease or disorder, e.g., cancer, from one organ or another non-adjacent organ or body part. Cancer occurs at an originating site, e.g., stomach, which site is referred to as a primary tumor, e.g., primary gastric cancer. Some cancer cells in the primary tumor or originating site acquire the ability to penetrate and infiltrate surrounding normal tissue in the local area and / or the ability to penetrate the walls of the lymphatic system or vascular system circulating through the system to other sites and tissues in the body. A second clinically detectable tumor formed from cancer cells of a primary7tumor is referred to as a metastatic or secondary tumor. When cancer cells metastasize, the metastatic tumor and its cells are presumed to be similar to those of the original tumor. Thus, if gastric cancer metastasizes to the lymph nodes, the secondary tumor at the site of the lymph nodes consist of gastric cancer cells and not abnormal lymph node cells. The secondary tumor in the lymph nodes is referred to as lymph node metastasis. Thus, the phrase metastatic cancer refers to a disease in which a subject has or had a primary tumor and has one or more secondary tumors.The phrases non-metastatic cancer or subjects with cancer that is not metastatic refers to diseases in which subjects have a primary’ tumor but not one or more secondary tumors.

[0060] Methods of Detection

[0061] Provided herein is a method of detecting an RNA biomarker in a patient having, or suspected of having, gastric cancer comprising detecting RNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95-3p. cell-free miR- 18 lb-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR- 1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95- 3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof. In embodiments, the method is for detecting an RNA biomarker in a patient having gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient having early gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having early gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient having gastric cancer with lymph node metastasis. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having gastric cancer with lymph node metastasis. In embodiments, the method is for detecting an RNA biomarker in a patient having early gastric cancer with lymph node metastasis. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having early gastric cancer with lymph node metastasis. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335- 3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p. exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell- free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR- 27a-3p. cell-free miR-95-3p. cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21- 3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR- 21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodimetns, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR- 95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p. exosomal miR-27a- 3p, and exosomal miR-95-3p. In embodiments, the RNA biomarkers are as described in any of the embodiments herein. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample. In embodiments, the blood sample is a plasma sample.

[0062] Provided herein are methods of detecting an RNA biomarker in a patient having, or suspected of having, gastric cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR- 335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p. cell-free miR-431- 5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p. exosomal miR-135b-5p, or a combination of two or more thereof. In embodiments, the method is for detecting an RNA biomarker in a patient having gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient having early gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having early gastric cancer. In embodiments, the method is for detecting an RNA biomarker in a patient having gastric cancer with lymph node metastasis. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having gastric cancer with lymph node metastasis. In embodiments, the method is for detecting an RNA biomarker in a patient having early gastric cancer with lymph node metastasis. In embodiments, the method is for detecting an RNA biomarker in a patient suspected of having early gastric cancer with lymph node metastasis. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p. exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215- 5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p. exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p. exosomal miR-27a-3p, exosomal miR-95-3p. exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR- 27a-3p. cell-free miR-95-3p. exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodimetns, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR- 215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p. exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA biomarkers are as described in any of the embodiments herein. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample. In embodiments, the blood sample is a plasma sample. In embodiments, the control is the average expression level of RNA in a population of healthy patients.

[0063] Methods of Treatment

[0064] Provided herein are methods of treating gastric cancer in a patient in need thereof, the method comprising administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof; wherein a biological sample obtained from the patient comprises an elevated expression level, relative to a control, of RNA; wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335- 3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p. exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof. In embodiments, the gastric cancer is early gastric cancer. In embodiments, the gastric cancer is early gastric cancer with lymph node metastasis. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR- 335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431- 5p, exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell- free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR- 27a-3p. cell-free miR-95-3p. cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21- 3p. exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p. exosomal miR-27a-3p. exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95-3p, exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodimetns, the RNA comprises cell- free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR- 95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a- 3p. and exosomal miR-95-3p. In embodiments, the RNA biomarkers are as described in any of the embodiments herein. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample. In embodiments, the blood sample is a plasma sample. In embodiments, the control is the average expression level of RNA in a population of healthy patients.

[0065] Methods of Detecting and Treating

[0066] Provided herein are methods of treating gastric cancer in a patient in need thereof, the method comprising (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from a patient; wherein the RNA comprises cell-free miR-21-3p. cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR- 21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a- 3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b- 5p. or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof. In embodiments, the gastric cancer is early gastric cancer. In embodiments, the gastric cancer is early gastric cancer with lymph node metastasis. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent. In embodiments, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95- 3p. cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p,exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR- 21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a- 3p, and exosomal miR-95-3p. In embodimetns, the RNA comprises cell-free miR-21-3p, cell- free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-215-5p. exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA biomarkers are as described in any of the embodiments herein. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample. In embodiments, the blood sample is a plasma sample. In embodiments, the control is the average expression level of RNA in a population of healthy patients.

[0067] Methods of Diagnosis

[0068] Provided herein are methods of diagnosing a patient with gastric cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95- 3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof. In embodiments, the gastric cancer is early gastric cancer. In embodiments, the gastric cancer is early gastric cancer with lymph node metastasis. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215- 5p, cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95-3p, cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR- 431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell- free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodimetns, the RNA comprises cell-free miR-21-3p. cell-free miR-21-5p, cell-free miR-215- 5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA biomarkers are as described in any of the embodiments herein. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample. In embodiments, the blood sample is a plasma sample. In embodiments, the control is the average expression level of RNA in a population of healthy patients.

[0069] Methods of Monitoring

[0070] Provided herein are methods of monitoring a patient at risk for developing gastric cancer, the method comprising (i) detecting an expression level of RNA in a biological sample obtained from the patient at a first point in time; (ii) detecting an expression level of RNA in a biological sample obtained from the patient at a second point in time, wherein the second point in time is later than the first point in time; wherein the RNA comprises cell-free miR-21-3p, cell- free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-18 lb-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR- 21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a- 3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b- 5p, or a combination of two or more thereof. In embodiments, the gastric cancer is early gastric cancer. In embodiments, the gastric cancer is early gastric cancer with lymph node metastasis. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215- 5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p. exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR- 431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises cell-free miR-21-3p, cell- free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR- I92-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodimetns, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA biomarkers are as described in any of the embodiments herein. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample. In embodiments, the blood sample is a plasma sample. In embodiments, the control is the average expression level of RNA in a population of healthy patients.

[0071] In embodiments of the methods of monitoring described herein, the patient is in remission from gastric cancer at the first point in time, wherein the expression level of RNA in the biological sample at the first point in time is not elevated when compared to the expression level of RNA at the second point in time, thereby indicating that the patient is still in remission at the second point in time. In embodiments, the patient is in remission from gastric cancer at the first point in time, wherein an increased expression level of RNA in the biological sample at the second point in time compared to the expression level of RNA at the first point in time indicates that the patient has a recurrence of gastric cancer. In embodiments, an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has an increased risk of developing gastric cancer. In embodiments, an expression level of RNA at the second point in time that is not elevated when compared to the expression level of RNA at the first point in time indicates that the patient does not have an increased risk of developing gastric cancer. In embodiments, a diagnosis of gastric cancer is made when the patient as elevated expression levels of the RNA.

[0072] Methods of Treatment

[0073] Provided herein is a method of treating gastric cancer in a patient in need thereof comprising: (i) selecting a patient having a diagnosis of gastric cancer based on a gastric cancer risk score or an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21- 5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell- free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p. exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-I96a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof; and (ii) treating the patient from step (i) by administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically- removing all or a portion of the stomach of the patient, or a combination of two or more thereof. In embodiments, step (ii) comprises administering to the patient an effective amount of an anti-cancer agent. In embodiments, the gastric cancer is early gastric cancer. In embodiments, the gastric cancer is early gastric cancer with lymph node metastasis.

[0074] Provided herein is a method of treating gastric cancer in a patient in need thereof comprising: (i) receiving a gastric cancer risk score or an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95-3p, cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, exosomal miR-135b-5p, or a combination of two or more thereof; and (ii) diagnosing a patient with gastric cancer based on the gastric cancer risk score or the elevated expression level of RNA, monitoring a patient who is at risk of developing gastric cancer based on the gastric cancer risk score or the elevated expression level of RNA, monitoring efficacy of treatment for gastric cancer in a patient based on the gastric cancer risk score or the elevated expression level of RNA; and (iii) treating the patient from step (ii) by administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof. In embodiments, the gastric cancer is early gastric cancer In embodiments, step (iii) comprises administering to the patient an effective amount of an anti-cancer agent. In embodiments, the gastric cancer is early- gastric cancer. In embodiments, the gastric cancer is early gastric cancer with lymph node metastasis.

[0075] Provided herein is a method of treating gastric cancer in a patient in need thereof comprising: (i) receiving a gastric cancer risk score or an elevated expression level of RNA, wherein the gastric cancer risk score or elevated expression level of RNA is produced by a non- transitory computer-readable storage medium having instructions stored thereon which, when executed by a processor, causes the processor to perform an operation comprising applying an algorithm to the results of a method which comprises detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335- 3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p. exosomal miR-27a-3p, exosomal miR-95-3p. exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof; and(ii) diagnosing a patient with gastric cancer based on the gastric cancer risk score or the elevated expression level of RNA, monitoring a patient who is at risk of developing gastric cancer based on the gastric cancer risk score or the elevated expression level of RNA, monitoring efficacy of treatment for gastric cancer in a patient based on the gastric cancer risk score or the elevated expression level of RNA; and (iii) treating the patient from step (ii) by administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof. In embodiments, step (iii) comprises administering to the patient an effective amount of an anticancer agent. In embodiments, the gastric cancer is early gastric cancer. In embodiments, the gastric cancer is early gastric cancer with lymph node metastasis.

[0076] Provided herein are methods of processing data generated from the RNA levels in the biological sample obtained from a patient for establishing a gastric cancer risk score (composite risk score), e g., a score indicative gastric cancer. In embodiments, the method comprises the steps of (i) normalizing and / or scaling numeric values of the RNA level data, (ii) refining the discriminatory power of individual RNA by statistically weighting some of the numeric values associated therewith, and (iii) summating the numeric values obtained from step (ii) to provide a composite risk score. In embodiments, the composite risk score obtained from step (iii) is compared to a control and the comparison allows the sample to be designated as positive or negative for gastric cancer or a scale of likelihood of gastric cancer. In embodiments, the composite risk score is normalized. In embodiments, the composite risk score is scaled. In embodiments, the composite risk score is weighted. Weighted refers to the relevant value being adjusted to more appropriately reflect its contribution to the risk score. The gastric cancer risk score can be based on a comparison to a control, such as a healthy patient, a population of healthy patients, a patient with gastric cancer, or a population of patients with gastric cancer.

[0077] In embodiments, the gastric cancer risk score is produced by a non-transitory computer-readable storage medium having instructions stored thereon which, when executed by a processor, causes the processor to perform an operation comprising applying an algorithm to the protein levels.

[0078] In embodiments of the methods described herein, a medical provider instructs a patient to obtain laboratory tests. The laboratory analyzes a biological sample provided by the patient to produce test results, e.g.. levels of RNA and / or gastric cancer risk scores. The medical provider receives the test results from the laboratory so that the medical provider can use the test results to treat a patient with gastric cancer, diagnose a patient with gastric cancer, monitor a patientwho is at risk of developing gastric cancer, or monitoring efficacy of treatment for gastric cancer in a patient. Medical provider refers to any person or entity that provides medical services to a patient. In embodiments, the medical provider is a medical doctor, a nurse, a nurse practitioner, a physician’s assistant, a hospital, a doctor’s office, and the like.

[0079] RNA Biomarkers

[0080] In embodiments of the methods described herein, the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p. miR-27a-3p. miR-95-3p, miR-181b-5p. miR-431-5p. miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof. In embodiments, the RNA is cell-free RNA, exosomal RNA, or a combination thereof. In embodiments, the RNA is cell-free RNA. In embodiments, the RNA is exosomal RNA. In embodiments, the RNA comprises cell-free RNA and exosomal RNA.

[0081] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, or a combination of two or more thereof.

[0082] In embodiments of the methods described herein, the RNA comprises exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, exosomal miR-135b-5p, or a combination of two or more thereof.

[0083] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p. exosomal miR-27a-3p. exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof.

[0084] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p. and cell-free miR-431-5p.

[0085] In embodiments of the methods described herein, the RNA consists of cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, and cell-free miR-431-5p.

[0086] In embodiments of the methods described herein, the RNA comprises exosomal miR- 21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p.

[0087] In embodiments of the methods described herein, the RNA consists of exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, and exosomal miR-135b-5p.

[0088] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p.

[0089] In embodiments of the methods described herein, the RNA consists of cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p. exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p.

[0090] In embodiments of the methods described herein, the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-196a-5p, miR-183-5p, and miR-135b-5p. In embodiments, the RNA is cell-free RNA, exosomal RNA, or a combination thereof. In embodiments, the RNA is cell-free RNA. In embodiments, the RNA is exosomal RNA. In embodiments, the RNA comprises cell-free RNA and exosomal RNA.

[0091] In embodiments of the methods described herein, the RNA comprises exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a- 3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR- 135b-5p.

[0092] In embodiments of the methods described herein, the RNA consists of exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a- 3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR- 135b-5p.

[0093] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p,exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p.

[0094] In embodiments of the methods described herein, the RNA consists of cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p. exosomal miR-27a-3p. exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p.

[0095] In embodiments of the methods described herein, the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p. miR-95-3p, miR-1246, and miR-192-3p. In embodiments, the RNA is cell-free RNA, exosomal RNA, or a combination thereof. In embodiments, the RNA is cell-free RNA. In embodiments, the RNA is exosomal RNA. In embodiments, the RNA comprises cell-free RNA and exosomal RNA.

[0096] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-2I-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, and cell-free miR-95-3p.

[0097] In embodiments of the methods described herein, the RNA consists of cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p, and cell-free miR-95-3p.

[0098] In embodiments of the methods described herein, the RNA comprises exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0099] In embodiments of the methods described herein, the RNA consists of exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0100] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0101] In embodiments of the methods described herein, the RNA consists of cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p. exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0102] In embodiments of the methods described herein, the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-27a-3p. and miR-95-3p. In embodiments, the RNA is cell-free RNA, exosomal RNA, or a combination thereof. In embodiments, the RNA is cell-free RNA. In embodiments, the RNA is exosomal RNA. In embodiments, the RNA comprises cell-free RNA and exosomal RNA.

[0103] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-27a-3p, and cell-free miR-95- 3p.

[0104] In embodiments of the methods described herein, the RNA consists of cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-27a-3p, and cell-free miR-95- 3p.

[0105] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, or a combination of two or more thereof. In embodiments, the RNA comprises at least two miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR- 215-5p, cell-free miR-27a-3p, and cell-free miR-95-3p. In embodiments, the RNA comprises at least three miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21- 5p, cell-free miR-215-5p, cell-free miR-27a-3p, and cell-free miR-95-3p. In embodiments, the RNA comprises at least four miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p. cell-free miR-215-5p, cell-free miR-27a-3p. and cell-free miR-95-3p. In embodiments, the RNA comprises two miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, and cell-free miR- 95-3p. In embodiments, the RNA comprises three miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, and cell- free miR-95-3p. In embodiments, the RNA comprises four miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a- 3p, and cell-free miR-95-3p. In embodiments, the RNA comprises cell-free miR-21-3p. In embodiments, the RNA comprises cell-free miR-21-5p. In embodiments, the RNA comprises cell-free miR-215-5p. In embodiments, the RNA comprises cell-free miR-27a-3p. In embodiments, the RNA comprises cell-free miR-95-3p.

[0106] In embodiments of the methods described herein, the RNA comprises exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR- 95-3p.

[0107] In embodiments of the methods described herein, the RNA consists of exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR- 95-3p.

[0108] In embodiments of the methods described herein, the RNA comprises: (a) cell-free miR-21-3p or exosomal miR-21-3p, (b) cell-free miR-21-5p or exosomal miR-21-5p, (c) cell- free miR-215-5p or exosomal miR-215-5p, (d) cell-free miR-27a-3p or exosomal miR-27a-39, and (e) cell-free miR-95-3p or exosomal miR-27a-39. In other words, the RNA comprises one miRNA from (a), one miRNA from (b). one miRNA from (c), one miRNA from (d). and one miRNA from (e). Thus, the RNA can comprise 5 miRNA of which 0-5 of the miRNA are cell- free miRNA and 5-0 of the miRNA are exosomal miRNA.

[0109] In embodiments of the methods described herein, the RNA comprises cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0110] In embodiments of the methods described herein, the RNA consists of cell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.[OHl] In embodiments of the methods described herein, the RNA comprises at least two miRNA selected from the group consisting of exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least three miRNA selected from the group consisting of exosomal miR-21- 3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95- 3p. In embodiments, the RNA comprises at least four miRNA selected from the group consisting of exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises two miRNA selected from the group consisting of exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises three miRNA selected from the group consisting of exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises four miRNA selected from the group consisting of exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises exosomal miR-21-3p. In embodiments, the RNA comprises exosomal miR-21-5p. In embodiments, the RNA comprises exosomal miR-215-5p. In embodiments, the RNA comprises exosomal exosomal miR-27a-3p. In embodiments, the RNAcomprises exosomal miR-95-3p.

[0112] In embodiments of the methods described herein, the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof, wherein the miR are cell-free miR, exosomal miR, or a combination thereof. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215- 5p. cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95-3p. cell-free miR-181b-5p. and cell-free miR-431-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR- 192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR- I96a-5p, exosomal miR-183-5p. exosomal miR-135b-5p. or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR- 181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR- 1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95- 3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-21-3p. In embodiments, the RNA comprises cell-free miR-21-5p. In embodiments, the RNA comprises cell-free miR- 215-5p. In embodiments, the RNA comprises cell-free miR-335-3p. In embodiments, the RNA comprises cell-free miR-27a-3p. In embodiments, the RNA comprises cell-free miR-95-3p. In embodiments, the RNA comprises cell-free miR-181b-5p. In embodiments, the RNA comprises cell-free miR-431-5p. In embodiments, the RNA comprises exosomal miR-21-3p. In embodiments, the RNA comprises exosomal miR-21-5p. In embodiments, the RNA comprises exosomal miR-1246. In embodiments, the RNA comprises exosomal miR-192-3p. In embodiments, the RNA comprises exosomal miR-215-5p. In embodiments, the RNA comprises exosomal miR-27a-3p. In embodiments, the RNA comprises exosomal miR-95-3p. In embodiments, the RNA comprises exosomal miR-196a-5p. In embodiments, the RNA comprises exosomal miR-183-5p. In embodiments, the RNA comprises exosomal miR-135b-5p.

[0113] In embodiments of the methods described herein, the RNA comprises at least two miR selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR- 215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least three miR selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomalmiR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least four miR selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a- 3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least five miR selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell- free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least six miR selected from the group consisting of cell- free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-27a-3p, cell-free miR- 95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p. exosomal miR-27a- 3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least seven miR selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least eight miR selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises at least nine miR selected from the group consisting of cell-free miR-21-3p. cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a- 3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95- 3p, exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p. In embodiments, the RNA consists of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21- 3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95- 3p-

[0114] In embodiments of the methods described herein, the RNA comprises at least two miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR- 18 Ib-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR- 1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95- 3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. Inembodiments, the RNA comprises at least three miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p. cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least four miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR- 215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b- 5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p. and exosomal miR-135b-5p. In embodiments, the RNA comprises at least five miRNA selected from the group consisting of cell-free miR-21- 3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell- free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR- 27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least six miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335- 3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p. exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least seven miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21- 5p, cell-free miR-215-5p, cell-free miR-335-3p. cell-free miR-27a-3p, cell-free miR-95-3p, cell- free miR-18Ib-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least eight miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p. and exosomal miR-135b-5p. In embodiments, the RNA comprises at least nine miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b- 5p, cell-free miR-431-5p, exosomal miR-21-3p. exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least ten miRNA selected from the group consisting of cell-free miR-21- 3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell- free miR-95-3p, cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR-21-3p. exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR- 27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least eleven miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p. cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR- 431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192- 3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a- 5p, exosomal miR-183-5p. and exosomal miR-135b-5p. In embodiments, the RNA comprises at least twelve miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR- 21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p. In embodiments, the RNA comprises at least thirteen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335- 3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p. exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least fourteen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR- 21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-I81b-5p, cell-free miR-431-5p, exosomal miR-2I-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p. In embodiments, the RNA comprises at least fifteen miRNA selected from the group consisting of cell-free miR-21-3p. cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335- 3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p,exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises at least sixteen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR- 21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p. In embodiments, the RNA comprises at least seventeen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335- 3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p. exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p. and exosomal miR-135b-5p.

[0115] In embodiments of the methods described herein, the RNA comprises two miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR- 215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b- 5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p. and exosomal miR-135b-5p. In embodiments, the RNA comprises three miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR- 21-5p, exosomal miR-1246, exosomal miR-192-3p. exosomal miR-215-5p, exosomal miR-27a- 3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR- 135b-5p. In embodiments, the RNA comprises four miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell- free miR-27a-3p. cell-free miR-95-3p. cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR- 215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR- 183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises five miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95-3p. cell-free miR-181b-5p, cell- free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomalmiR-192-3p, exosomal miR-215-5p. exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises six miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR- 21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p. In embodiments, the RNA comprises seven miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR- 21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises eight miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95-3p, cell-free miR-181b-5p, cell- free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises nine miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p. cell-free miR-95- 3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p. In embodiments, the RNA comprises ten miRNA selected from the group consisting of cell- free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR- 27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21- 3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p. exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises eleven miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR- 431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192- 3p. exosomal miR-215-5p. exosomal miR-27a-3p, exosomal miR-95-3p. exosomal miR-196a- 5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprisestwelve miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p. cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises thirteen miRNA selected from the group consisting of cell- free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR- 27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21- 3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises fourteen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell- free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p. exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises fifteen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95- 3p, cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p. In embodiments, the RNA comprises sixteen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p. cell-free miR-431-5p, exosomal miR- 21-3p, exosomal miR-2I-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215- 5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183- 5p, and exosomal miR-135b-5p. In embodiments, the RNA comprises seventeen miRNA selected from the group consisting of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR- 215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b- 5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0116] In embodiments, the RNA comprises miR-21-5p, miR-215-5p, and miR-335-3p. In embodiments, the RNA comprises cell-free miR-21-5p. cell-free miR-215-5p, and cell free miR- 335-3p. In embodiment, the RNA comprises miR-21-3p and miR-215. In embodiments, themiRNA comprises exosomal miR-21-3p and exosomal miR-215. In embodiments, the RNA comprises cell-free miR-21-5p, cell-free miR-215-5p. cell free miR-335-3p, exosomal miR-21- 3p and exosomal miR-215. In embodiments, the RNA comprises cell-free miR-21-5p, cell-free miR-215-5p, cell free miR-335-3p, exosomal miR-21-3p, exosomal miR-215, and exosomal miR-1246. In embodiments, the RNA comprises cell-free miR-21-5p, cell-free miR-215-5p, cell free miR-335-3p, exosomal miR-21-3p, exosomal miR-215, exosomal miR-1246, exosomal miR-21-5p, exosomal miR-192-3p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0117] miR Controls

[0118] In embodiments, the methods described herein (including embodiments thereof) further comprise detecting the expression level of RUN6B, miR-16-5p, or a combination thereof in the biological sample obtained from the patient. In embodiments, the methods described herein further comprising detecting the expression level of RUN6B in the biological sample obtained from the patient. In embodiments, the methods described herein further comprising detecting the expression level of miR-16-5p in the biological sample obtained from the patient. In embodiments, the methods described herein further comprising detecting the expression level of RUN6B and miR-16-5p in the biological sample obtained from the patient. In embodiments, the methods described herein (including embodiments thereof) further comprise normalizing the expression levels of the miRNA to the expression level of RUN6B, miR-16-5p, or a combination thereof. The expression level of RUN6B and / or miR-16-5p are used as a control to the expression level of miR of interest.

[0119] Anti-Cancer Agents

[0120] In embodiments, the methods described herein comprise administering to a patient an effective amount of an anti-cancer agent. The anticancer treatment can be any drug known in the art as useful for treating cancer, such as chemotherapy, immunotherapy, or a combination thereof. In embodiments, the anti-cancer agent is a chemotherapeutic agent. In embodiments, the anti-cancer agent is an immunotherapeutic agent, such as a checkpoint inhibitor. In embodiments, the anti-cancer agent comprises a chemotherapeutic agent and an immunotherapeutic agent, such as a checkpoint inhibitor. In embodiments, the immunotherapeutic agent is a PD-1 inhibitor, a PD-L1 inhibitor, a CTLA-4 inhibitor, or a combination thereof. In embodiments, the anti-cancer agent comprises avelumab, nivolumab, ipilimumab, tremelimumab, trastuzumab, pembrolizumab, mitomycin, ramucirumab, everolimus, erlotinib, olaparib, mitomycin, sunitinib, gemcitabine, 5-fluorouracil, irinotecan, oxaliplatin, paclitaxel, capecitabine. cisplatin, irinotecan, docetaxel, doxorubicin, trastuzumab- deruxtecan, leucovorin, epirubicin, zolbetuximab, or a combination of two or more thereof.

[0121] In embodiments, the chemotherapeutic agent is an alkylating agent, an antimetabolite compound, an anthracycline compound, an antitumor antibiotic, a platinum compound, a topoisomerase inhibitor, a vinca alkaloid, a taxane compound, an epothilone compound, or a combination of two or more thereof. In embodiments, the alkylating agent is carboplatin, chlorambucil, cyclophosphamide, melphalan, mechlorethamine, procarbazine, or thiotepa. In embodiments, the antimetabolite compound is azacitidine, capecitabine, cytarabine, gemcitabine, doxifluridme, hydroxyurea, methotrexate, pemetrexed, 6-thioguanme, 5- fluorouracil, or 6-mercaptopurine. In embodiments, the anthracycline compound is daunorubicin, doxorubicin, idarubicin, epirubicin, or mitoxantrone. In embodiments, the antitumor antibiotic is actinomycin, bleomycin, mitomycin, or valrubicin. In embodiments, the platinum compound is cisplatin or oxaliplatin. In embodiments, the topoisomerase inhibitor is irinotecan, topotecan, amsacrine, etoposide, teniposide, or eribulin. In embodiments, the vinca alkaloid is vincristine, vinblastine, vinorelbine, or vindesine. In embodiments, the taxane compound is paclitaxel or docetaxel. In embodiments, the epothilone compound is epothilone, ixabepilone, patupilone, or sagopilone.

[0122] '‘Chemotherapeutic” or “chemotherapeutic agent” is used in accordance with its plain ordinary meaning and refers to a chemical composition or compound having antineoplastic properties or the ability to inhibit the grow th or proliferation of cells.

[0123] “Anti-cancer agent” is used in accordance with its plain ordinary meaning and refers to a composition (e.g. compound, drug, antagonist, inhibitor, modulator) having antineoplastic properties or the ability to inhibit the grow th or proliferation of cells. In some embodiments, an anti-cancer agent is a chemotherapeutic. In embodiments, an anti-cancer agent is an agent approved by the FDA or similar regulatory' agency of a country other than the USA, for treating cancer. Examples of anti-cancer agents include, but are not limited to, MEK (e.g. MEK1, MEK2, or MEK1 and MEK2) inhibitors (e g., XL518, CI-1040, PD035901, selumetinib, trametinib, GDC-0973, ARRY-162, ARRY-300, AZD8330, PD0325901, U0126, PD98059, TAK-733, PD318088, AS703026, BAY 869766), alky lating agents (e.g., cyclophosphamide, ifosfamide. chlorambucil, busulfan, melphalan, mechlorethamine, uramustine, thiotepa, nitrosoureas, nitrogen mustards (e.g., mechloroethamine, cyclophosphamide, chlorambucil, meiphalan), ethylenimine and methylmelamines (e.g., hexamethlymelamine, thiotepa), alkyl sulfonates (e.g., busulfan), nitrosoureas (e.g., carmustine, lomusitne, semustine, streptozocin), triazenes (decarbazine)), anti-metabolites (e g., 5- azathioprine, leucovorin, capecitabine, fludarabine, gemcitabine, pemetrexed. raltitrexed. folic acid analog (e.g.. methotrexate), or pyrimidine analogs (e g., fluorouracil, floxouridine, cytarabine), purine analogs (e.g..mercaptopurine, thioguanine, pentostatin). etc.), plant alkaloids (e.g., vincristine, vinblastine, vinorelbine, vindesine. podophyllotoxin, paclitaxel, docetaxel, etc.), topoisomerase inhibitors (e.g., irinotecan, topotecan, amsacrine, etoposide, etoposide phosphate, teniposide, etc.), antitumor antibiotics (e.g., doxorubicin, adriamycin, daunorubicin, epirubicin, actinomycin, bleomycin, mitomycin, mitoxantrone, plicamycin, etc.), platinum-based compounds (e.g. cisplatin, oxaloplatin, carboplatin), anthracenedione (e.g., mitoxantrone), substituted urea (e.g., hydroxyurea), methyl hydrazine derivative (e.g., procarbazine), adrenocortical suppressant (e.g.. mitotane, aminoglutethimide), epipodophyllotoxins (e.g., etoposide), antibiotics (e g., daunorubicin, doxorubicin, bleomycin), enzymes (e.g., L-asparaginase), inhibitors of mitogen- activated protein kinase signaling (e.g., U0126, PD98059, PD184352, PD0325901, ARRY- 142886, SB239063, SP600125, BAY 43-9006, wortmannin, or LY294002), mTOR inhibitors, antibodies (e g., rituxan), 5-aza-2'-deoxycytidine, doxorubicin, vincristine, etoposide, gemcitabine, imatinib, geldanamycin, 17-N-allylamino-17-demethoxygeldanamycin, bortezomib, trastuzumab, anastrozole; angiogenesis inhibitors; antiandrogen, antiestrogen; antisense oligonucleotides; apoptosis gene modulators; apoptosis regulators; arginine deaminase; BCR / ABL antagonists; beta lactam derivatives; bFGF inhibitor; bicalutamide; camptothecin derivatives; casein kinase inhibitors; clomifene analogues; cytarabine dacliximab; dexamethasone; estrogen agonists; estrogen antagonists; etanidazole; etoposide phosphate; exemestane; fadrozole; finasteride; fludarabine; fluorodaunorunicin hydrochloride; gadolinium texaphyrin; gallium nitrate; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; immunostimulant peptides; insulin-like growth factor- 1 receptor inhibitor; interferon agonists; interferons; interleukins; letrozole; leukemia inhibiting factor; leukocyte alpha interferon; leuprolide+estrogen+progesterone; leuprorelin; matrilysin inhibitors; matrix metalloproteinase inhibitors; MIF inhibitor; mifepristone; mismatched double stranded RNA; monoclonal antibody,; mycobacterial cell wall extract; nitric oxide modulators; oxaliplatin; panomifene; pentrozole; phosphatase inhibitors; plasminogen activator inhibitor; platinum complex; platinum compounds; prednisone; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor; protein kinase C inhibitors, protein tyrosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; ras famesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; ribozymes; signal transduction inhibitors; signal transduction modulators; single chain antigen-binding protein; stem cell inhibitor; stem-cell division inhibitors; stromelysin inhibitors; synthetic glycosaminoglycans; tamoxifen methiodide; telomerase inhibitors; thyroid stimulating hormone; translation inhibitors; tyrosine kinase inhibitors; urokinase receptor antagonists; steroids (e.g., dexamethasone), finasteride, aromatase inhibitors,gonadotropin-releasing hormone agonists, such as goserelin or leuprolide, adrenocorticosteroids (e.g., prednisone), progestins (e.g., hydroxy progesterone caproate, megestrol acetate, medroxyprogesterone acetate), estrogens (e.g., di ethly stilbestrol, ethinyl estradiol), antiestrogen (e.g., tamoxifen), androgens (e.g., testosterone propionate, fluoxymesterone), antiandrogen (e.g., flutamide), immunostimulants (e.g., Bacillus Calmette-Guerin, levamisole, interleukin-2, alphainterferon, etc.), monoclonal antibodies (e.g., anti-CD20, anti-HER2, anti-CD52, anti-HLA-DR, anti-VEGF monoclonal antibodies), immunotoxins (e.g., anti-CD33 monoclonal antibody- calicheamicin conjugate, anti-CD22 monoclonal antibody-pseudomonas exotoxin conjugate, etc.), radioimmunotherapy (e.g., anti-CD20 monoclonal antibody conjugated toniIn,90Y, or1'1I, etc.), triptolide, homoharringtonine. dactinomycin, doxorubicin, epirubicin, topotecan, itraconazole, vindesine, cerivastatin. vincristine, deoxyadenosine, sertraline, pitavastatin, irinotecan, clofazimine, 5-nonyloxytryptamine, vemurafenib, dabrafenib, erlotinib, gefitinib, EGFR inhibitors, epidermal grow th factor receptor-targeted therapy or therapeutic (e.g. gefitinib, erlotinib, cetuximab, lapatinib. panitumumab, vandetanib, afatinib, canertinib, neratinib, CP- 724714, TAK-285. AST-1306, ARRY334543, ARRY-380, AG-1478, dacomitinib, desmethyl erlotinib, AZD8931, AEE788, pelitimb, CUDC-10I, WZ8040, WZ4002, WZ3146, AG-490, XL647, PD153035, BMS-599626), sorafenib, imatinib, sunitinib, dasatinib, or the like.

[0124] Gene Expression

[0125] In embodiments of the methods described herein, the elevated level of gene expression is an elevated level of RNA (e.g., miRNA) expression. Levels of gene expression can be determined by methods known in the art, such as those described herein. In embodiments, the RNA is miRNA. In embodiments, RNA expression is detected by direct digital counting of nucleic acids, RNA sequencing (RNA-seq), quantitative reverse transcriptase polymerase chain reaction (RT-qPCR). quantitative polymerase chain reaction (qPCR). multiplex qPCR. microarray analysis, or a combination thereof. In embodiments, RNA expression is detected by RNA sequencing. RNA sequencing is a sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample. In embodiments, the gene expression level is an average of the gene expression level of the biomarker genes. In embodiments, the average of the gene expression level of the biomarker genes is an average of the normalized gene expression level of the biomarker genes. In embodiments, the gene expression level of the biomarker genes is a median of the gene expression level of the biomarker genes. In embodiments, the median of the gene expression level of the biomarker genes is a median of a normalized gene expression level of the biomarker genes. In embodiments, the gene expression level of the biomarker genes is the gene expressionlevel of the biomarker genes normalized to a reference gene.

[0126] In embodiments of the methods described herein, the individual elevated expression level of the RNA described herein are used. In embodiments, the individual elevated expression level of the exosomal RNA described herein are used. In embodiments, the individual elevated expression level of the cell-free RNA described herein are used. In embodiments, the individual elevated expression level of the cell-free RNA and exosomal RNA described herein are used. In embodiments, the elevated expression levels of the RNA are combined to form a risk score. In embodiments, the elevated expression levels of the exosomal RNA described herein are combined to form a risk score. In embodiments, the elevated expression levels of the cell-free RNA described herein are combined to form a risk score. In embodiments, the elevated expression levels of the cell-free RNA and exosomal RNA described herein are combined to form a risk score.

[0127] In embodiments, the elevated expression levels of the RNA are weighted and combined to form a risk score. In embodiments, the elevated expression levels of the exosomal RNA described herein are weighted and combined to form a risk score. In embodiments, the elevated expression levels of the cell-free RNA described are weighted and combined to form a risk score. In embodiments, the elevated expression levels of the cell-free and exosomal RNA described herein are weighted and combined to form a risk score.

[0128] Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as the control (e.g., healthy patients). The 2v tmethod is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The Ct (threshold cycle) method quantification was used for the evaluation of the expression level of each miRNA. The threshold cycle (Ct) is defined as the PCR cycle at which the fluorescent signal of the reporter dye crosses an arbitrarily placed threshold. This method allows to quantify the absolute expression of each miRNAs in each sample analyzed and then to calculate the different expression of each miRNA in sample versus the controls. These expression values of the RNA can be used individually to produce a risk score, can be added together to produce a risk score, or logistic regression analysis can be applied to produce a risk score based on weighted values of the expression levels of the RNA.

[0129] In embodiments, the disclosure provides methods of processing RNA expression data generated from the expression levels of the RNA in the biological sample obtained from a patient as described herein, for establishing the presence of a signature indicative of gastric cancer, comprising the steps of (i) normalizing and / or scaling numeric values of the RNA expression data (e.g., the exosomal RNA expression data and / or cell-free RNA expression data),(ii) refining the discriminatory power of individual RNA by statistically weighting some of the numeric values associated therewith, and (iii) summating the numeric values obtained from step (ii) to provide a composite expression score. In embodiments, the composite expression score obtained from step (iii) is compared to a control and the comparison allows the sample to be designated as positive or negative for gastric cancer. In embodiments, the composite expression score is normalized. In embodiments, the composite expression score is scaled. In embodiments, the composite expression score is weighted. Weighted refers to the relevant value being adjusted to more appropriately reflect its contribution to the profile. Normalizing and / or scaling numeric values of the RNA expression data can be done using expression data of controls, such as RUN6B, miR-16-5p. or a combination thereof.

[0130] In embodiments, risk score is a combination of the risk score for the cell-free RNA is added to the risk score for the exosomal RNA. In embodiments, the expression of level of each miRNA is calculated using 2'ACtmethod, the normalized expression values are log10transformed, and then used in the equations herein.

[0131] Kits

[0132] Provided here are kits comprising components, such as reagents and reaction mixtures, to conduct the assays to detect the miRNA and mRNA as described herein. As part of the kit, materials and instruction are provided, e.g., for storage and use of kit components. In embodiments, the kits comprise one or more of the following: a RNA probe that can hybridize to a RNA biomarker, pairs of primers that under appropriate reaction conditions can prime amplification of at least a portion of a RNA marker or a RNA encoding a polypeptide marker (e.g., by PCR), instructions on how to use the kit, and a label or insert indicating regulatory' approval for diagnostic or therapeutic use. In embodiments, the kit further includes RNA microarrays comprising RNA of the disclosure or molecules which specifically bind to the RNA described herein. In embodiments, standard techniques of microarray technology are utilized to assess expression of the RNA. Polynucleotide arrays, particularly arrays that bind RNA described herein, also can be used for diagnostic applications, such as for identifying subjects that have a condition characterized by expression of polypeptide biomarkers.

[0133] “Assaying” or “detecting” means using an analytic procedure to qualitatively assess or quantitatively measure the presence or amount or the functional activity of a target entity (e.g., miRNA, mRNA). For example, detecting the level of RNA (such as miRNA or mRNA) means using an analytic procedure (such as an in vitro procedure) to qualitatively assess or quantitatively measure the presence or amount of the RNA. In embodiments, raw expressionvalues are normalized by performing quantile normalization relative to the reference distribution and subsequent log 10-transformation. In embodiments, when RNA expression is detected using the nCounter® Analysis System marketed by Nanostring Technologies, the reference distribution is generated by pooling reported (i.e., raw) counts for the test sample and one or more control samples (preferably at least 2 samples, more preferably at least any of 4, 8 or 16 samples) after excluding values for technical (both positive and negative control) probes and without performing intermediate normalization relying on negative (background-adjusted) or positive (synthetic sequences spiked with known titrations).

[0134] The terms “probe” or “primer” refer to one or more nucleic acid fragments whose specific hybridization to a sample can be detected. A probe or primer can be of any length depending on the particular technique it will be used for. For example, PCR primers are generally between 10 and 40 nucleotides in length, while nucleic acid probes for, e.g., a Southern blot, can be more than a hundred nucleotides in length. The probe or primers can be unlabeled or labeled as described below so that its binding to a target sequence can be detected (e.g., with a FRET donor or acceptor label). The probe or primer can be designed based on one or more particular (preselected) portions of a chromosome, e.g., one or more clones, an isolated whole chromosome or chromosome fragment, or a collection of polymerase chain reaction (PCR) amplification products. One of skill can adjust these factors to provide optimum hybridization and signal production for a given hybridization and detection procedures, and to provide the required resolution among different genes or genomic locations.

[0135] Probes and primers can also be immobilized on a solid surface (e.g., nitrocellulose, glass, quartz, fused silica slides), as in an array. Techniques for producing high density arrays can also be used for this purpose. One of skill will recognize that the precise sequence of particular probes and primers can be modified from the target sequence to a certain degree to produce probes that are “substantially identical” or “substantially complementary to” a target sequence, but retain the ability' to specifically bind to (i.e., hybridize specifically to) the same targets from which they were derived.

[0136] The term “capable of hybridizing to” refers to a polynucleotide sequence that forms Watson-Crick bonds with a complementary sequence. One of skill will understand that the percent complementarity' need not be 100% for hybridization to occur, depending on the length of the polynucleotides, length of the complementary' region(e.g. 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, or more bases in length), and stringency of the conditions. For example, a polynucleotide (e.g., primer or probe) can be capable of binding to a polynucleotide having 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or100% complementarity over the stretch of the complementary' region.

[0137] In embodiments, methods include detecting a level of a biomarker with a specific binding agent (e.g., an agent that binds to a protein or nucleic acid molecule). Exemplary’ binding agents include an antibody or a fragment thereof, a detectable protein or a fragment thereof, a nucleic acid molecule such as an oligonucleotide / polynucleotide comprising a sequence that is complementary to patient genomic DNA, miRNA, mRNA or a cDNA produced from patient mRNA. or any combination thereof. In embodiments, an antibody is labeled with detectable moiety, e.g., a fluorescent compound, an enzyme or functional fragment thereof, or a radioactive agent. In embodiments, an antibody is detectably labeled by coupling it to a chemiluminescent compound. In embodiments, the presence of the chemiluminescent-tagged antibody is then determined by detecting the presence of luminescence that arises during the course of chemical reaction. Non-limiting examples of useful chemiluminescent labeling compounds are luminol, isoluminol, theromatic acridinium ester, imidazole, acridinium salt and oxalate ester.

[0138] In embodiments, a specific binding agent is an agent that has greater than 10-fold, preferably greater than 100-fold, and most preferably, greater than 1000-fold affinity for the target molecule as compared to another molecule. As the skilled artisan will appreciate the term specific is used to indicate that other biomarkers present in the sample do not significantly bind to the binding agent specific for the target molecule. In embodiments, the level of binding to a biomolecule other than the target biomarker results in a binding affinity which is at most only 10% or less, only 5% or less only 2% or less or only 1% or less of the affinity to the target molecule, respectively. A preferred specific binding agent will fulfill both the above minimum criteria for affinity as well as for specificity. For example, in embodiments an antibody has a binding affinity’ (e.g., Kd) in the low micromolar (IO-6). nanomolar (10’7-l O’9), with high affinity antibodies in the low nanomolar (10‘9) or picomolar (10‘12) range for its specific target biomarker.

[0139] In embodiments, the subject matter provides a composition comprising a binding agent, wherein the binding agent is attached to a solid support, (e.g., a strip, a polymer, a bead, a nanoparticle, a plate such as a multiwell plate, or an array such as a microarray). In embodiments relating to the use of a nucleic acid probe attached to a solid support (such as a microarray), a nucleic acid in a test sample may be amplified (e.g., using PCR) before or after the nucleic acid to be measured is hybridized with the probe. In embodiments, reverse transcription polymerase chain reaction (RT-PCR) is used to detect mRNA levels or miRNA levels. In embodiments, a probe on a solid support is used, and mRNA (or a portion thereof) in abiological sample is converted to cDNA or partial cDNA and then the cDNA or partial cDNA is hybridized to a probe (e.g., on a microarray), hybridized to a probe and then amplified, or amplified and then hybridized to a probe. In embodiments, a strip may be a nucleic acid-probe coated porous or non-porous solid support strip comprising linking a nucleic acid probe to a carrier to prepare a conjugate and immobilizing the conjugate on a porous solid support. In embodiments, the support or carrier comprises glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite. In embodiments, the nature of the carrier can be either soluble to some extent or insoluble for the purposes of the present subject matter. In embodiments, the support material may have any structural configuration so long as the coupled molecule is capable of binding to a binding agent (e.g., an antibody). In embodiments, the support configuration may be spherical, as in a bead, or cylindrical, as in the inside surface of a test tube, or the external surface of a rod. In embodiments, the surface may be flat such as a plate (or a well within a multiwell plate), sheet, test strip, polysty rene beads. Those skilled in the art will know many other suitable carriers for binding antibody or antigen, or will be able to ascertain the same by use of routine experimentation.

[0140] In embodiments, a solid support comprises a polymer, to which an agent is chemically bound, immobilized, dispersed, or associated. In embodiments, a polymer support may be, e g., a network of polymers, and may be prepared in bead form (e.g., by suspension polymerization). In embodiments, the location of active sites introduced into a polymer support depends on the type of polymer support. In embodiments, in a swollen-gel-bead polymer support the active sites are distributed uniformly throughout the beads, whereas in a macroporous-bead polymer support they are predominantly on the internal surfaces of the macropores. In embodiments, the solid support, e.g.. a device, may contain a biomarker binding agent alone or together with a binding agent for at least one, two, three or more other biomarkers.

[0141] In embodiments, the cells in a biological sample are lysed to release a protein or nucleic acid. Numerous methods for lysing cells and assessing protein and nucleic acid levels are known in the art. In embodiments, cells are physically lysed, such as by mechanical disruption, liquid homogenization, high frequency sound waves, freeze / thaw cycles, with a detergent, or manual grinding. Non-limiting examples of detergents include Tween 20, Triton X- 100, and sodium dodecyl sulfate (SDS). Non-limiting examples of assays for determining the level of a protein include HPLC. LC / MS, ELISA, immunoelectrophoresis, Western blot, immunohistochemistry, and radioimmunoassays. Non-limiting examples of assays for determining the level of an mRNA include Northern blotting, RT-PCR, RNA sequencing, andqRT-PCR.

[0142] In embodiments, once a suitable biological sample has been obtained, it is analyzed to quantitate the expression level of each of the biomarker genes. In embodiments, determining the expression level of a gene comprises detecting and quantifying RNA transcribed from that gene or a protein translated from such RNA. In embodiments, the RNA is miRNA.

[0143] In embodiments, raw expression values are normalized by performing quantile normalization relative to the reference distribution and subsequent log 10-transformation. In embodiments, when the gene expression is detected using the nCounter® Analysis System marketed by NanoString® Technologies, the reference distribution is generated by pooling reported (i.e., raw) counts for the test sample and one or more control samples (preferably at least 2 samples, more preferably at least any of 4, 8 or 16 samples) after excluding values for technical (both positive and negative control) probes and without performing intermediate normalization relying on negative (background-adjusted) or positive (synthetic sequences spiked with known titrations). In embodiments, the T-effector signature score is then calculated as the arithmetic mean of normalized values for each of the genes in the gene signature.

[0144] A “detectable agent” or “detectable moiety” is a compound or composition detectable by appropriate means such as spectroscopic, photochemical, biochemical, immunochemical, chemical, magnetic resonance imaging, or other physical means. The RNA described herein and the expression level of the RNA described herein may be accomplished through the use of a detectable moiety in an assay or kit. A detectable moiety is a monovalent detectable agent or a detectable agent bound (e g. covalently and directly or via a linking group) with another compound, e.g., a nucleic acid. Exemplary' detectable agents / moieties for use in the present disclosure include an antibody ligand, a peptide, a nucleic acid, radioisotopes, paramagnetic metal ions, fluorophore (e.g. fluorescent dyes), electron-dense reagents, enzymes (e.g.. as commonly used in an ELISA), biotin, a biotin-avidin complex, a biotin-streptavidin complex, digoxigenin, magnetic beads (e.g., DYNABEADS® by ThermoFisher, encompassing functionalized magnetic beads such as DYNABEADS® M-270 amine by ThermoFisher), paramagnetic molecules, paramagnetic nanoparticles, ultrasmall superparamagnetic iron oxide nanoparticles, ultrasmall superparamagnetic iron oxide nanoparticle aggregates, superparamagnetic iron oxide nanoparticles, superparamagnetic iron oxide nanoparticle aggregates, monocrystalline iron oxide nanoparticles, monocrystalline iron oxide, nanoparticle contrast agents, liposomes or other delivery vehicles containing Gadolinium chelate molecules, gadolinium, radionuclides (e.g., carbon-11, nitrogen-13, oxygen-15, fluorine- 18. rubidium-82), fluorodeoxy glucose (e.g., fluorine-18 labeled), any gamma ray emitting radionuclides, positron-emitting radionuclide, radiolabeled glucose, radiolabeled water, radiolabeled ammonia, biocolloids, microbubbles (e.g., including microbubble shells including albumin, galactose, lipid, and / or polymers; microbubble gas core including air, heavy gas(es), perfluorcarbon, nitrogen, octafluoropropane, perflexane lipid microsphere, perflutren, etc.), iodinated contrast agents (e g., iohexol, iodixanol, ioversol, iopamidol, ioxilan, iopromide, diatrizoate, metrizoate, ioxaglate), barium sulfate, thorium dioxide, gold, gold nanoparticles, gold nanoparticle aggregates, fluorophores, two-photon fluorophores. or haptens and proteins or other entities which can be made detectable, e g., by incorporating a radiolabel into a peptide or antibody specifically reactive with a target peptide.

[0145] In embodiments, oligonucleotides in kits are capable of specifically hybridizing to a target region of a polynucleotide, such as for example, an RNA transcript or cDNA generated therefrom. As used herein, specific hybridization means the oligonucleotide forms an antiparallel double-stranded structure with the target region under certain hybridizing conditions, while failing to form such a structure with non-target regions when incubated with the polynucleotide under the same hybridizing conditions. The composition and length of each oligonucleotide in the kit will depend on the nature of the transcript containing the target region as well as the type of assay to be performed with the oligonucleotide and is readily determined by the skilled artisan.

[0146] In embodiments, the kit comprises reagents capable of detecting an expression level of RNA from a blood sample; wherein the RNA is an miRNA described herein.

[0147] In embodiments, the disclosure provides a kit for diagnosing gastric cancer in a patent, including reagents for detecting RNA markers in a biological (e.g., blood) sample from a patient; wherein the RNA is an miRNA described herein.

[0148] In embodiments, the kits comprise one or more of the following: a RNA probe that can hybridize to a RNA biomarker, pairs of primers that under appropriate reaction conditions can prime amplification of at least a portion of a RNA marker or a RNA encoding a polypeptide marker (e.g., by PCR), instructions on how to use the kit, and a label or insert indicating regulatory approval for diagnostic or therapeutic use.

[0149] In embodiments, the kit further includes RNA microarrays comprising RNA of the disclosure or molecules which specifically bind to the RNA described herein. In embodiments, standard techniques of microarray technology' are utilized to assess expression of the RNA. Polynucleotide arrays, particularly arrays that bind RNA described herein, also can be used for diagnostic applications, such as for identifying subjects that have a condition characterized by expression of polypeptide biomarkers, e g., interstitial lung disease.

[0150] In addition, the means for detecting of the assay system of the present disclosure can be immobilized on a substrate. Such a substrate can include any suitable substrate for immobilization of a detection reagent such as would be used in any of the previously described methods of detection. Briefly, a substrate suitable for immobilization of a means for detecting includes any solid support, such as any solid organic, biopolymer or inorganic support that can form a bond with the means for detecting without significantly affecting the activity and / or ability of the detection means to detect the desired target molecule. Exemplary organic solid supports include polymers such as polystyrene, nylon, phenol-formal dehy de resins, and acrylic copolymers (e.g., polyacry lamide). The kit can also include suitable reagents for the detection of the reagent and / or for the labeling of positive or negative controls, wash solutions, dilution buffers and the like. The assay system can also include a set of written instructions for using the system and interpreting the results.

[0151] Embodiments 1-48.

[0152] Embodiment 1. A method of detecting RNA biomarker in a patient having, or suspected of having, gastric cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

[0153] Embodiment 2. A method of treating gastric cancer in a patient comprising administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof; wherein a biological sample obtained from the patient comprises an elevated expression level, relative to a control, of RNA; wherein the RNA comprises miR-21- 3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

[0154] Embodiment 3. A method of treating gastric cancer in a patient in need thereof, the method comprising: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from a patient, wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p. miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p. miR-196a-5p, miR-183-5p, miR-135b-5p. or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent,administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof.

[0155] Embodiment 4. A method of diagnosing a patient with gastric cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, thereby diagnosing the patient with gastric cancer; wherein the RNA comprises miR-21-3p. miR-21-5p, miR-215-5p. miR-335-3p. miR-27a-3p. miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

[0156] Embodiment 5. A method of monitoring a patient at risk for developing gastric cancer, the method comprising (i) detecting an expression level of RNA in a biological sample obtained from the patient at a first point in time; (ii) detecting an expression level of RNA in a biological sample obtained from the patient at a second point in time, wherein the second point in time is later than the first point in time. Wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215- 5p, miR-335-3p, miR-27a-3p, miR-95-3p. miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

[0157] Embodiment 6. The method of embodiment 5, wherein an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has an increased risk of developing gastric cancer.

[0158] Embodiment 7. The method of any one of embodiments 1 to 6, wherein the RNA is cell-free RNA, exosomal RNA, or a combination thereof.

[0159] Embodiment 8. The method of embodiment 7, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, cell-free miR-181b-5p. and cell-free miR-431-5p, or a combination of two or more thereof.

[0160] Embodiment 9. The method of embodiment 7, wh erein the RNA comprises exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR- 215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR- 183-5p, exosomal miR-135b-5p, or a combination of tw o or more thereof.

[0161] Embodiment 10. The method of embodiment 7, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p. exosomal miR-215-5p. exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p,exosomal miR-135b-5p, or a combination of two or more thereof.

[0162] Embodiment 11. The method of any one of embodiments 1 to 6, wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR- 1246. miR-192-3p, or a combination of two or more thereof.

[0163] Embodiment 12. The method of embodiment 11, wherein the RNA is cell-free RNA, exosomal RNA, or a combination thereof.

[0164] Embodiment 13. The method of embodiment 12, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, or a combination of two or more thereof.

[0165] Embodiment 14. The method of embodiment 12, wherein the RNA comprises exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, or a combination of two or more thereof.

[0166] Embodiment 15. The method of embodiment 12, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, or a combination of two or more thereof.

[0167] Embodiment 16. The method of embodiment 12, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0168] Embodiment 17. The method of embodiment 12, wherein the RNA consists of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0169] Embodiment 18. The method of any one of embodiments 1 to 6, wherein the RNA comprises miR-21-3p, miR-21-5p. miR-215-5p. miR-27a-3p, miR-95-3p, or a combination of two or more thereof.

[0170] Embodiment 19. The method of any one of embodiments 16 to 18, wherein the RNA is cell-free RNA, exosomal RNA, or a combination thereof.

[0171] Embodiment 20. The method of embodiment 16, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p. cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95- 3p, or a combination of two or more thereof.

[0172] Embodiment 21. The method of embodiment 16, wherein the RNA comprises exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p, or a combination of two or more thereof.

[0173] Embodiment 22. The method of embodiment 16, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95- 3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, or a combination of two or more thereof.

[0174] Embodiment 23. The method of embodiment 16, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95- 3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0175] Embodiment 24. The method of embodiment 16, wherein the RNA consists of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95- 3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

[0176] Embodiment 25. The method of embodiment 22 or 23, wherein the RNA further comprises cell-free miR-335-3p.

[0177] Embodiment 26. The method of embodiment 22, 23, or 25, wherein the RNA further comprises cell-free miR-181b-5p.

[0178] Embodiment 27. The method of embodiment 22, 23, 25, or 26, wherein the RNA further comprises cell-free miR-431-5p.

[0179] Embodiment 28. The method of embodiment 22, 23, 25, 26, or 27, wherein the RNA further comprises exosomal miR-1246.

[0180] Embodiment 29. The method of embodiment 22, 23, 25, 26, 27. or 28, wherein the RNA further comprises exosomal miR-192-3p.

[0181] Embodiment 30. The method of embodiment 22, 23, 25, 26, 27, 28, or 29, wherein the RNA further comprises exosomal miR-196a-5p.

[0182] Embodiment 31. The method of embodiment 22, 23, 25, 26, 27. 28. 29. or 30, wherein the RNA further comprises exosomal miR-183-5p.

[0183] Embodiment 32. The method of embodiment 22, 23, 25, 26, 27, 28, 29, 30, or 31, wherein the RNA further comprises exosomal miR-135b-5p.

[0184] Embodiment 33. The method of embodiment 7, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p. cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p,exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p.

[0185] Embodiment 34. The method of embodiment 7, wherein the RNA consists of cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p. exosomal miR-27a-3p. exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p.

[0186] Embodiment 35. The method of any one of embodiments 1 to 34, wherein the biological sample is a blood sample.

[0187] Embodiment 36. The method of any one of embodiments 1 to 35, wherein the gastric cancer is early gastric cancer.

[0188] Embodiment 37. The method of any one of embodiments 1 to 35, wherein the gastric cancer is T2 gastric cancer.

[0189] Embodiment 38. The method of any one of embodiments 1 to 35, wherein the gastric cancer is T3 gastric cancer.

[0190] Embodiment 39. The method of any one of embodiments 1 to 35, wherein the gastric cancer is T4 gastric cancer.

[0191] Embodiment 40. The method of any one of embodiments 2. 3, and 6-38, wherein step (ii) comprises administering to the patient the effective amount of the anti-cancer agent.

[0192] Embodiment 41. The method of any one of embodiments 1, 4, and 5-38, further comprising administering to the patient an effective amount of an anti-cancer agent.

[0193] Embodiment 42. The method of any one of embodiments 2. 3, and 6-41, wherein the anti-cancer agent comprises a chemotherapeutic agent, a checkpoint inhibitor, or a combination thereof.

[0194] Embodiment 43. The method of any one of embodiments 2, 3, and 6-41, wherein the anti-cancer agent comprises a PD-1 inhibitor, a PD-L1 inhibitor, a CTLA-4 inhibitor, or a combination thereof.

[0195] Embodiment 44. The method of any one of embodiments 2, 3, and 6-41, wherein the anti-cancer agent comprises avelumab, nivolumab, ipilimumab, tremelimumab, trastuzumab, pembrolizumab, mitomycin, ramucirumab, everolimus, erlotinib, olaparib, mitomycin, sunitinib, gemcitabine. 5-fluorouracil, irinotecan, oxaliplatin, paclitaxel, capecitabine, cisplatin, irinotecan, docetaxel, doxorubicin, trastuzumab-deruxtecan, leucovorin, epirubicin, zolbetuximab, or acombination of two or more thereof.

[0196] Embodiment 45. The method of any one of embodiments 2. 3, and 6-41, wherein the anti-cancer agent comprises an alkylating agent, an antimetabolite compound, an anthracycline compound, an antitumor antibiotic, a platinum compound, a topoisomerase inhibitor, a vinca alkaloid, a taxane compound, an epothilone compound, or a combination of two or more thereof.

[0197] Embodiment 46. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises, cell-free miR-21-3p. cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95- 3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b-5p, or a combination of two or more thereof.

[0198] Embodiment 47. The kit of embodiment 46, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p. cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-1246. exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, or a combination of two or more thereof.

[0199] Embodiment 48. The kit of embodiment 46, wherein the RNA comprisescell-free miR- 21-3p, cell-free miR-21-5p, cell-free miR-215-5p. cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p. exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, or a combination of two or more thereof.EXAMPLES

[0200] Although the incidence of gastric cancer (GC) worldwide has declined over the past decades, it remains the third leading cause of cancer-related death. The poor prognosis in GC is mainly due to its discover}’ in advanced stages and inevitably depends upon patients’ symptoms, especially in Western countries where a screening program is not feasible. In countries with high rates of GC, routine mass screening currently relies on endoscopy. However, this endoscopy examination strategy has significant disadvantages such as discomfort to patients, poor compliance, and high cost, which greatly emphasizes the importance of developing effective strategies for the early detection of this malignancy. Accordingly, in this study, we undertook a systematic and comprehensive biomarker discovery' followed by a validation approach for the non-invasive detection of patients with GC.

[0201] Currently, the primary analytes for liquid biopsy include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating cell-free RNA, and extracellular vesicles(EVs). CTCs are difficult to use as diagnostic biomarkers due to their rarity and heterogeneity. Cf-RNA. on the other hand, has been debated to be released from various cellular sources, including the tumor cells, potentially lacking tissue specificity. In recent years, EVs have been highly stable and typically released in abundant quantities. The exosomal miRNAs (exo- miRNAs) can provide the identity of the cell ty pe, which makes them promising non-invasive biomarkers in tumor diagnosis. In this study, our results indicated that the cf-miRNA panel has a higher sensitivity but lower specificity than the exo-miRNA panel to detect patients with GC. The 13-miRNA combination signature has the superior ability for accurate identification of GC (AUC = 0.95) in the validation cohort than cf-miRNA or exo-miRNA panel individually. Interestingly, we observed that the 5 miRNA biomarkers overlapped between the cf-miRNA and exo-miRNA panels. The 5 cf-miRNA panel and exo-miRNA panels exhibited an AUC of 0.81 and 0.79 for distinguishing GC and NDCs in the validation cohort, respectively. Subsequently, we developed a final signature, DESTINEX, comprising these 5 overlapping cf- and exo- miRNAs, and evaluated its diagnostic performance with an AUC of 0.94. These results highlight that cf-panel and exo-panel complements and improves diagnostic accuracy. More importantly, DESTINEX had a higher sensitivity in distinguishing GC from non-disease controls (NDCs) in patients with EGC (pTl vs. pT2-4: 87% (95%CI: 79%-95%) vs. 81% (95%CI: 73%-89%); stage I II vs. stage III-IV: 91% (95%CI: 83%-98%) vs. 86% (95%CI: 77%-93%)), which highlighted that DESTINEX is equally effective, more feasible and economical for detecting patients with early stage GC compared to the 13-miRNA signature. Furthermore, the origin of these circulating biomarkers was confirmed as tumor-specific, as the expression level of each biomarker was significantly decreased in postsurgical serum specimens.

[0202] Summary

[0203] Our study comprised 4 phases: a miRNA-based biomarker discovery phase, a serumbased training phase, a serum-based validation phase, and the final diagnostic performance evaluation phase in an independent cohort. For the biomarker discovery' phase, we implemented a systematic discovery in tissue samples, serum specimens from GC patients, and NDC by small RNA sequencing. Subsequently, a combination signature of cf- and exo-miRNA biomarkers was identified using 161 GC patients and 102 NDC in the serum-based training phase. Next, we examined these cf- and exo-miRNAs in another independent cohort of 131 GC and 86 NDC. Finally, to evaluate the final diagnostic performance of the miRNA panel, 20 matched pairs of pre- and post-operative serum specimens from patients with GC were analyzed.

[0204] We developed 8 cf-miRNA and 10 exo-miRNA panels in the discovery phase. In the training phase, the cf- and exo-miRNA panel was reduced to 6 and 7 miRNAs using the LASSO(Least Absolute Shrinkage and Selection Operator) analysis. Subsequently, a combination signature of 8 miRNA biomarkers (5 overlapped cf- and exo-miRNA, 1 individual cf-miRNA. and 2 individual exo-miRNA) can robustly identify patients with GC with an Area under the Curve (AUC) of 0.96 and 0.95 in the training and validation cohorts, respectively. We observed that a signature of 5 miRNAs overlapping in both cell-free and exosomal samples remains similarly effective in identifying GC with an AUC value of 0.94 (sensitivity (SEN) = 88%. specificity (SPE) = 88%)) in the validation cohort. We demonstrated that the 5-miRNA signature successfully identified patients with early -stage (pTl) GC, with an AUC value of 0.96 (SEN = 87%, SPE = 96%). The significantly decreased expression levels of miRNAs in postsurgery serum specimens confirmed the robust specificity.

[0205] We identified and established DESTINEX assay for detecting GC using a comprehensive discovery approach, which was trained and validated in two independent cohorts of GC patients and NDCs. Our findings highlight the clinical significance of DESTINEX as a robust, non-invasive biomarker for the early identification of patients with GC.

[0206] Results

[0207] The primary objective of our research was to identify cf- and exo-miRNA biomarkers for the early detection of patients with GC. Toward this aim, we performed a comprehensive and systematic sequencing profiling of a variety of clinical biospecimens, followed by rigorous bioinformatics and statistical data analysis approaches to identify panels of candidate miRNAs. Firstly, we analyzed the DEMs between 47 primary GC and matched AN tissues, which led us to identify a panel of 44 upregulated and 64 downregulated miRNAs (logzFC > 0.5 and p < 0.01, FIG. 1A). We performed a similar analysis in the cell-free and exosomal fractions by analyzing 43 cf-RNA and 32 exo-RNA sequencing data from patients with GC and 20 NDC subjects. These analyses yielded a panel of 112 upregulated and 52 downregulated cf-miRNAs (log2FC > 1 and p < 0.01, FIG. IB) and a panel of 54 upregulated and 72 downregulated exo-miRNAs (log2FC > 1 and p < 0.01, FIG. 1C) that differentiated patients with GC from NDC subjects.

[0208] To prioritize GC-specific biomarker candidates, we narrowed a panel of 8 miRNAs (miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p. miR-181b-5p, and miR-431 -5p, FIGS. 1D-1E) whose expression overlapped between tissue and cell-free fractions, and another panel of 10 miRNAs (miR-21-3p, miR-21-5p, miR-1246, miR-192-3p, miR-215-5p, miR-27a-3p, miR-95-3p, miR-196a-5p, miR-183-5p, and miR-135b-5p, FIGS. 1D-1F) where the expression overlapped between tissue and exosomes. A panel of 5 miRNAs (miR-21-3p, miR-21-5p, miR-215-5p, miR-27a-3p. and miR-95-3p, FIG. ID) emerged as common candidates between the cell-free and exosomal fractions.

[0209] We performed logistic regression and calculated the risk scores using the coefficients derived from this model for each individual 8 cf-miRNA and 10 exo-miRNA candidates. ROC analysis was performed to evaluate the diagnostic performance of the combined panel of 8 cf- miRNAs and 10 exo-miRNAs for their ability to discriminate patients with GC from the NDC subjects. These analyses revealed that both panels were robust in their diagnostic ability to identity’ patients with GC, as evidenced by remarkable area under the curve (AUC) values of 0.96 for the cf-miRNA panel (95% confidence interval (Cl): 0.91-1.00 and 0.99 for the exo- miRNA panel (95%CI: 0.96-1.00) in the discovery cohort (FIG. 1G). These data demonstrated that the miRNA-based biomarker discovery’ allowed us to successfully identity’ and develop unique cf-miRNA and exo-miRNA panels for the early detection of patients GC utilizing rigorous and thorough bioinformatic analyses.

[0210] The training and validation of a non-invasive miRNA-based panel for the detection of patients with GC. To establish a miRNA biomarker panel for the non-invasive diagnosis of patients with GC, we quantitated the expression levels of discovered miRNA candidates in serum samples from patients in the training cohort (GC: 161 cases, NDC: 102 subjects) using RT-qPCR assays. In accordance with the sequence-profiling results, all 8 cf-miRNAs (miR-21- 3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-431-5p, miR-181b-5p, miR-27a-3p, and miR- 95-3p) and 9 of the 10 exo-miRNAs (miR-21-3p, miR-21-5p, miR-1246, miR-196a-5p, miR- 183-5p, miR-192-3p, miR-215-5p, miR-27a-3p, and miR-95-3p) exhibited significantly elevated expression levels in the GC vs. control group.

[0211] Analysis A

[0212] To accurately distinguish patients with GC from NDC subjects, LASSO based machine learning algorithm regression analysis was applied in the training cohort. Training of the candidates in this cohort yielded 6 of 8 cf-miRNA candidates (miR-21-3p, miR-21-5p. miR-215- 5p, miR-335-3p, miR-27a-3p, and miR-95-3p) and 7 of 10 exo-miRNA candidates (miR-21-3p, miR-21-5p, miR-1246, miR-192-3p, miR-215-5p, miR-27a-3p, and miR-95-3p). The diagnostic performance of each cf- and exo- miRNA biomarker is summarized in FIGS. 15A-15B. The coefficients and constants of each cf- and exo-miRNA candidate were subsequently determined in a multivariate logistic regression analysis. The formula of cf- and exo-miRNA panels were as follows: cf-miRNA panel: 0.470 x miR-21-3p + 2.461 x miR-21-5p - 3.518 x miR-215-5p + 4.308 x miR-335-3p - 0.313 x miR-27a-3p - 0.294 x miR-95-3p - 0.744 (FIG. 15A); exo- miRNA panel: 3.944 x miR-21-3p + 1.126 x miR-21-5p - 1.581 x miR-1246 - 1.282 x miR- 192-3p + 4.372 x miR-215-5p - 1.152 x miR-27a-3p - 1. 177 x miR-95-3p + 10.581 (FIG.15B). The risk scores of cf- and exo-miRNA panels were calculated according to theseequations, and significantly higher scores were observed in patients with GC compared to NDCs in both panels (p < 0.001, FIG. 6A). The ROC analysis demonstrated a remarkable and comparable performance of both the 6 cf-miRNA panel (AUC = 0.90, 95%CI = 0.86-0.93, Sens. = 83%, Spec. = 80%, FIG. 2A) and the 7 exo-miRNA panel (AUC = 0.88, 95%CI = 0.93-0.92, Sens. = 78%, Spec. = 86%, FIG. 2A) in the training cohort patients, highlighting their ability to accurately discriminate patients with GC from NDC subjects. In support of our original hypothesis, the cf-miRNA panel exhibited a higher sensitivity. and the exo-miRNA panel revealed a higher specificity for the detection of patients with GC.

[0213] We validated the diagnostic performance of the 6 cf-miRNAs and 7 exo-miRNAs in another independent validation cohort (GC: 131 cases, NDC: 86 cases). In this serum-based validation phase, we applied the same analytical model and coefficients derived from the logistic regression equation from the training cohort to calculate the risk scores. It was notable to observe that the performance of these markers was consistent even in this cohort, and the risk scores were significantly higher in patients with GC compared to NDCs (p < 0.001, FIG. 2B). The ROC curve analysis in this independent validation cohort revealed an impressive performance for the 6 cf-miRNA panel (AUC = 0.87, 95%CI = 0.82-0.92, Sens. = 85%, Spec. = 78%, FIG. 2C) as well as the 7 exo-miRNA panel (AUC = 0.83, 95%CI = 0.77-0.88, Sens. = 77%, Spec. = 83%, FIG. 2C). Taken together, these results demonstrate that we successfully trained and validated panels of cf- and exo-miRNAs for the robust, non-invasive identification of patients with GC.

[0214] Establishment of a combined cell-free and exosomal miRNA signature for the non- invasive diagnosis of patients with GC. Our original hypothesis was that cf-miRNAs may be more sensitive while exo-miRNAs might offer higher specificity, and their combination could offer optimal diagnostic accuracy. To test our hypothesis, we combined the markers within the 6 cf- and the 7 exo-panels in the training cohort. In support of our original hypothesis, we observed that a combined 13-miRNA signature exhibited a superior diagnostic performance in the training cohort (AUC = 0.96, 95%CI = 0.94-0.98, FIG. 2D) vis-a-vis individual cf- and exo- miRNA biomarker panels (AUC = 0.90 and 0.88, respectively). In addition to the AUC values, the combined miRNA signature also displayed markedly improved sensitivity (combination vs. cf- vs. exo-miRNAs: 91% vs. 83% vs. 78%) and specificity (combination vs. cf- vs. exo- miRNAs: 88% vs. 80% vs. 86%) over cf-miRNA and exo-miRNA panels individually in the training cohort (FIG. 6B). The waterfall plot illustrates the robust ability of the combination signature (with 146 / 161 cases. 90.7% categorized as true positives; 91 / 103 cases. 88.3% categorized as true negatives) to identity’ patients with GC in the training cohort (FIG. 6C, upperpanel).

[0215] We undertook a similar analysis in the independent validation cohort, which revealed a similarly superior diagnostic performance of this combination signature (AUC = 0.95, 95%CI = 0.92-0.98, FIG. 2D) vs. individual cf- and exo-miRNA panels (AUC = 0.88 and 0.83, respectively). Significantly, the integrated miRNA signature demonstrated substantial enhancements in both sensitivity (combination vs. cf- vs. exo-miRNAs: 85% vs. 77% vs. 89%) and specificity (combination vs. cf- vs. exo-miRNAs: 78% vs. 83% vs. 88%) compared to the individual cf-miRNA and exo-miRNA panels in the validation cohort (FIG. 2E). The performance of the combination signature for identification of patients with GC was confirmed by waterfall analysis, with 116 out of 131 cases (88.5%) correctly identified as true positive and 77 out of 87 cases (88.5%) correctly identified as true negative in the validation cohort (FIG. 6C, lower panel). We performed decision curve analysis (DCA) to determine the clinical usefulness for the risk-stratification of each biomarker panel in the validation cohort. As shown in the DCA curves, the patient risk stratification using the combination signature offered a significantly superior net benefit in comparison to individual cf- and exo-miRNA panels, highlighting that the diagnostic implementation of this signature could mitigate harm and reduce misdiagnosis in the clinical practice (FIG. 2F). Collectively, these findings highlight that the 13- miRNA combination signature offers a remarkable diagnostic ability for accurate identification of GC.

[0216] Establishment of a facile, inexpensive, and clinically feasible miRNA signature for the non-invasive detection of patients with GC. We observed that a smaller subset of 5 miRNAs (miR-21-3p, miR-21-5p, miR-215-5p, miR-27a-3p, and miR-95-3p) were common between the cf- and exo-miRNA panels. To establish a feasible and economical signature that includes the minimum number of biomarkers with the optimal diagnostic performance for clinical application, we compared the performance of a 10-miRNA signature (combination of 5 cf- miRNA and 5 exo-miRNA) with the 13-miRNA signature to identify patients with GC. Firstly, we performed multivariate logistic regression analysis and established formula for cf- and exo- miRNA panels (5 cf-miRNA panel: 0.922 x cf-miR-21-3p +3.834 x cf-miR-21-5p - 3.470 x cf- miR-215-5p + 1.360 x cf-miR-27a-3p - 0.427 x cf-miR-95-3p; 5 exo-miRNA panel: 3.163 x exo-miR-21-3p + 0.659 x exo-miR-21-5p + 5.462 x exo-miR-215-5p - 2.122 x exo-miR-27a-3p - 1.996 x exo-miR-95-3p + 3.615). The risk scores of both cf- and exo-miRNA panels were significantly higher in patients ith GC compared to NDCs in the training and validation cohort (p < 0.001. FIGS. 7A-7B). It was observed that the diagnostic ability of the 5-miRNA panels is relatively low in cell-free or exosomes individually in the training (cell-free: AUC = 0.86,sensitivity = 0.80; specificity = 0.72; exosomes: AUC = 0.85, sensitivity = 0.76; specificity = 0.82, FIG. 7C) compared to the 6 cf-miRNA and 7 exo-miRNA panels. We again demonstrate that cf-miRNA panel provides higher sensitivity, while exo-miRNA panel offers higher specificity using the same 5 miRNAs. Similarly, the panels of 5 cf-miRNAs and 5 exo-miRNAs exhibit AUC values of 0.81 and 0.79 in the validation cohort, respectively. These values are relatively low compared to those of the 6 cf-miRNA and 7 exo-miRNA panels (FIG. 7D). However, the combination of 5 cf- and exo-miRNAs manifests a synergistic effect, leading to a robust diagnostic assay with significantly improved sensitivity and specificity.

[0217] To test this aim, we developed a final signature for the detection of gastric cancer by combining 5 cf- and exo-miRNAs (DESTINEX) and calculated risk probability for each patient in the training cohort. The results show that the overall performance of DESTINEX achieved an AUC value of 0.95 in the training cohort and 0.94 in the validation cohort (FIG. 3A). The waterfall plots confirmed the efficacy of DESTINEX in distinguishing patients with GC, with 90.2% true positives (145 / 161 cases) and 87.4% true negatives (90 / 103 cases) in the training cohort (FIG. 8A. upper panel). In the validation cohort, it achieved 87.8% true positives (115 / 131 cases) and 88.0% true negatives (73 / 83 cases) (FIGS. 3B and 8A). DESTINEX exhibited significantly enhanced sensitivity and specificity when compared to the individual cf- miRNA and exo-miRNA panels in both the training (FIG. 8B) and validation cohort (FIG. 3C).

[0218] We then conducted a systematically comparative analysis of the diagnostic performance between DESTINEX and the 13-miRNA signature; the definite AUC, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) were summarized in Table 1. By evaluating AUC and accuracy in the training (FIG. 8C, Table 1) and validation cohort (FIG. 3D, Table 1), the results indicated that the performance of DESTINEX remained consistently comparable to that of the 13-miRNA signature. Moreover, the DCA analysis for DESTINEX exhibited equally similar net benefit compared with the 13-miRNA signature (FIG. 3E). Similarly, the calibration curve analysis reassured that the predicted probability of DESTINEX matched the actual probability of patients with GC (FIG. 3F, DESTINEX: Chi-square = 5.5, df = 10, / ? = 0.86; 13-miRNA signature: Chi-square = 5.9, df = 10, / ? = 0.83). Taken together, DESTINEX distinguished patients with GC from NDCs, offering a powerful and cost-effective tool for effectively screening patients with GC.

[0219] Table 1: The performance of DESTINEX and 13-miRNA signature for detection of GC in training and validation cohort

[0220] The non-invasive miRNA signature efficiently identifies patients with early gastric cancer. Early diagnosis is a necessary healthcare strategy in all settings, as it improves survival and clinical outcomes. Early GC is defined as cancer that only invades the mucosal or submucosal layer (stage Tl), regardless of whether lymph node metastasis is present. Our results revealed that the risk score of DESTINEX to detect GC was consistently high, even in patients with pTl stage (Mean risk score in NDCs: -2.042; pTl : 3.638; pT2-4: 3.182; NDCs vs. pTl or pT2-4: p < 0.001, FIG. 4A). We observed that DESTINEX had a superior ability to detect patients with EGC in the validation cohort (AUC of pTl vs. pT2-4: 0.96 vs. 0.93, FIG. 4B). We also evaluated the performance of GC in early TNM stages (I&II vs. NDCs) and it was observed that the risk score of DESTINEX was also high in the GC patients with stage I-II (mean risk score in NDCs: -2.042; I&II: 3.579; NDCs vs. I&II.p < 0.001, FIG. 4C). Our non-invasive DESTINEX efficiently identified patients even with the earliest stages of GC, as proven by ROC analysis (stage I&II: AUC = 0.95, 95%CI = 0.92-0.98, Sens. = 91%, Spec. = 88%; stage III&IV: AUC = 0.93, 95%CI = 0.90-0.97, Sens. = 86%, Spec. = 87%, FIG. 4D).

[0221] We compared the sensitivity of DESTINEX in each subgroup from several clinicopathological features (pT stage. TNM stage, pathology, CEA, CAI 9-9, lymphatic invasion, FIG. 4E). The results highlighted that DESTINEX exhibited a higher sensitivity in clinical variables, including pT stage (pTl vs. pT2-4: 87% (95%CI: 79%-95%) vs. 81% (95%CI: 73%-89%)), TNM stage (I&II vs. III&IV: 91% (95%CI: 83%-98%) vs. 86% (95%CI: 77%-93%)), and lymph node invasion (no invasion vs. invasion: 93% (95%CI: 85%-100%) vs.86% (95%CI: 79%-94%)). Collectively, these data highlight that DESTINEX retains its advantage in sensitivity when identifying patients with the earliest stages of GC. We investigated the diagnostic performance of DESTINEX regarding their sensitivity at a fixed specificity of 90%, 92.5%, 95%, and 97.5% for early detection of GC in the validation cohort, summarized in Table 2. The results showed that DESTINEX has higher sensitivity at a fixed specificity of 90% (pTl vs. pT2-4: 90% (95%CI: 81% - 98%) vs. 86% (95%CI: 76% - 93%)), a fixed specificity of 92.5% (pTl vs. pT2-4: 89% (95%CI: 73% - 97%) vs. 82% (95%CI: 62% - 92%)), a fixed specificity of 95% (pTl vs. pT2-4: 87% (95%CI: 60% - 95%) vs. 80% (95%CI: 52% - 90%)) and a fixed specificity of 97.5% (pTl vs. pT2-4: 70% (95%CI: 40% - 94%) vs. 58% (95%CI: 30% - 87%)) for detection of patients with early-stage (pTl) GC. Taken together, DESTINEX robustly distinguished patients with early-stage GC from NDCs.

[0222] Table 2: The diagnostic performance of DESTINEX at varying specificity thresholds for detection of early stage (pTl) GC

[0223] DESTINEX exhibited robust specificity for detecting patients with GC. To further investigate the specificity of DESTINEX for GC detection, we analyzed the expression levels of circulating cf- and exo-miRNAs (miR-21-3p, miR-21-5p, miR-215-5p, miR-27a-3p, miR-95-3p) in serum samples from 20 GC patients where follow-up samples had been collected before surgery (n = 20) and 3 months after surgery (n = 20). We hypothesized that the expression of high-specificity biomarkers would be reduced after treatment such as curative surgery eliminates their source. As expected, excluding exosomal miR-95-3p, all other cf- and exo-miRNA expression levels significantly decreased in post-surgery samples (p < 0.05, FIG. 9). Risk probability calculated based on DESTINEX for this subset of patients was significantly reduced in postoperative serum specimens (p < 0.001, FIG. 4F).

[0224] To ensure the high specificity of the non-invasive DESTINEX, we compared the diagnostic capabilities in patients with several other gastrointestinal cancers, including colorectal cancer (CRC), pancreactic ductal adenocarcinoma (PDAC). esophageal squamous cell carcinoma (ESCC), intrahepatic cholangiocarcinoma (ICC), and hepatocellular carcinoma(HCC). DESTINEX exhibited the highest diagnostic value in GC vs. all other types of gastrointestinal cancers (FIG. 4G. GC: AUC = 0.94, 95%CI = 0.91-0.97. Sens. = 88%, Spec. = 88%; CRC: AUC = 0.73, 95%CI = 0.57-0.90, Sens. = 65%, Spec. = 80%; PDAC: AUC = 0.67, 95%CI = 0.48-0.85, Sens. = 75%, Spec. = 65%; ESCC: AUC = 0.63, 95%CI = 0.44-0.82, Sens. = 60%, Spec. = 85%; ICC: AUC = 0.59, 95%CI = 0.41-0.77, Sens. = 45%, Spec. = 85%; HCC: AUC = 0.51, 95%CI = 0.31-0.70, Sens. = 25%, Spec. = 100%). The results demonstrated that the DESTINEX was highly specific in detecting GC, which will have tremendous impact for routine clinical use.

[0225] Analysis B

[0226] To establish a miRNA biomarker panel for non-invasive GC diagnosis, we quantitated the expression levels of discovered miRNA candidates in serum samples from the training cohort (GC: 161 cases, NDC: 102 subjects) using RT-qPCR assays. One GC case was excluded due to undetectable expression levels. Consistent with sequence-profiling results, all 8 cf- miRNAs and 9 of the 10 exo-miRNAs (excluding miR-135-5p) exhibited significantly elevated expression in GC vs. controls and were included in further analysis. The diagnostic performance of each cf- and exo-miRNA biomarker is summarized in FIGS. 16A-16B.

[0227] We applied an extreme Gradient Boosting (XGBoost)-based machine-learning algorithm to the training cohort. Separate panels of 8 cf-miRNAs and 9 exo-miRNAs were established, and risk scores were calculated using the XGBoost classifier. Density plots of cf- miRNA and exo-miRNA panels risk score revealed distinct clustering patterns, with NDCs predominantly in the bottom left and GC patients in the top right (FIG. 10A). ROC analysis demonstrated remarkable performance for the 8 cf-miRNA panel (AUC: 90.9%, Sens. = 85.0%, Spec. = 81.4%. FIG. 10B) and the 9 exo-miRNA panel (AUC: 87.8%, Sens. = 60.0%, Spec. = 97. 1%, FIG. 10B). highlighting their ability to accurately discriminate GC patients from NDCs. The cf-miRNA panel exhibited higher sensitivity, and the exo-miRNA panel revealed greater specificity for GC detection.

[0228] Our hypothesis proposed that cf-miRNAs provide higher sensitivity while exo- miRNAs greater specificity, with their combination offering optimal diagnostic accuracy. To test this, we combined markers from the 8 cf- and 9 exo-miRNA panels in the training cohort. Supporting our hypothesis, the combined 17-miRNA signature achieved a significantly higher diagnostic score than the individual panels (p < 0.001, FIG. 10C). The 17-miRNA signature exhibited superior performance (AUC: 96.3% [Cl95%: 94.3 - 98.4%], FIG. 10C) vis-a-vis individual cf- and exo-miRNA panels (AUC = 90.9% and 87.8%. respectively). Waterfall plots further confirmed its effectiveness, identifying 89.4% true positives (143 / 160 cases) and 95.1%true negatives (97 / 102 subjects) (FIGS. 13A-13B). The 17-miRNA signature included cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a- 3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b- 5p.

[0229] We validated the diagnostic performance of the 17-miRNA signature in another independent validation cohort (GC: 131 cases, NDC: 86 subjects). Four individuals were excluded due to undetermined miRNA levels. In this serum-based validation phase, we applied the XGBoost classifier model parameters from our locked-down assay in the training cohort to calculate risk scores. These markers performed consistently even in this cohort, with significantly higher diagnostic risk scores in GC patients than in NDCs (p < 0.001, FIG. 10D). ROC analysis revealed impressive performance for the 17-miRNA signature (AUC: 95.3% [Cl95°<>: 92.8 - 97.9%], FIG. 10D), achieving 87.0% true positives (114 / 131 cases) and 89.0% true negatives (73 / 82 subjects) (FIG. 10B). This signature exhibited a highly significant association with GC odd ratios (p < 0.001), indicating a proportional increase in the risk of GC (FIG. 10E)

[0230] We performed decision curve analysis (DCA) to determine the clinical utility of risk stratification for each biomarker panel in the validation cohort. The DCA curve demonstrated that the combined signature offered a significantly superior net benefit than the individual cf- and exo-miRNA panels, underscoring its ability to minimize harm and reduce misdiagnosis in clinical practice (FIG. 10F). Collectively, these findings highlight that the 17-miRNA combination signature offered remarkable diagnostic ability for accurate identification of GC.

[0231] We observed a smaller subset of 5 miRNAs (miR-21-3p, miR-21-5p, miR-215-5p, miR-27a-3p, and miR-95-3p) that were shared between the cf- and exo-miRNA panels. To establish a clinically feasible and effective diagnostic assay with minimum number of biomarkers and optimal diagnostic performance, we compared the 10-miRNA signature (combination of 5 cf-miRNA and 5 exo-miRNA) with the 17-miRNA signature for GC identification. Firstly, using an XGBoost algorithm, we established the 5 cf-miRNAs and 5 exo- miRNAs panels. Shapley Additive Explanations (SHAP) values highlighted the contributions of each miRNA within these panels (FIG. 11A). The top three contributors were consistent across both panels (miR-21-3p, miR-21-5p, and miR-215-5p). The diagnostic ability of the 5-miRNA panel was slightly lower for the cell-free component (AUC: [5 cf-miRNAs vs. 8 cf-miRNA panel: 87.0% vs. 90.9%]), but showed similar performance for exosomal markers (AUC: [5 cf-miRNA vs. 8 cf-miRNA panel: 89. 1% vs 87.7%]). It was not apparent whether this reduced combination of 5 cf- and exo-miRNAs was synergistic and would still result in a robust diagnostic assay with comparable or improved sensitivity and specificity vs. the larger panels of markers mentioned earlier.

[0232] To test this hypothesis, we developed a final signature for the detection of gastric cancer by combining 5 cf- and exo-miRNAs (DESTINEX) and calculated risk probability for each patient in the training cohort. DESTINEX scores were significantly higher in GC patients than NDCs (p < 0.001, FIG. 11B), with an AUC value of 95.8% (CI95%: 93.7 - 97.9%) (FIG. 11B). Given these results, the final DESTINEX assay was developed and fully locked, including all model parameters and expression threshold cutoffs. When applied to the validation cohort, the DESTINEX score remained significantly higher in patients with GC vs. NDCs (p < 0.001), achieving an AUC of 94.8% (CI95%: 92. 1 - 97.5%) (FIG. 11C). DESTINEX assay demonstrated higher sensitivity for cf-miRNAs ([5 cf- vs. 5 exo-miRNAs: 87.8% vs. 64.9%]), and superior specificity7for exo-miRNAs ([5 cf- vs. 5 exo-miRNAs: 74.4% vs. 90.2%]). The DESTINEX assay, which harnesses the sensitivity and specificity of both cf- and exo-miRNAs. yielded an overall sensitivity of 88.5% and a specificity' of 89.0% by employing the same XGBoost classifier and cutoff (1.065) as the training cohort (FIG. 11D).

[0233] We conducted a sy stematic comparative analy sis of the diagnostic performance between DESTINEX and the original 17-miRNA signature: the AUC values, sensitivity, specificity, accuracy, positive predictive value (PPV). and negative predictive value (NPV) are summarized in Table 3. An evaluation of accuracy, precision, recall, and Fl score in the validation cohorts indicated that DESTINEX remained consistently comparable to the 17- miRNA signature (FIG. HE). DCA analysis for DESTINEX exhibited equally similar net benefit (FIG. HF). Calibration curve analysis reassured that the predicted probability of DESTINEX matched the actual probability of GC (Fig. 11G, DESTINEX: Chi-square = 7.95, p = 0.63; 17-miRNA signature: Chi-square = 5.9, p = 0.83). DESTINEX robustly distinguished GC patients from NDCs, offering a powerful tool for effectively screening patients with GC.

[0234] Table 3

[0235] In the Training Cohort in Table 3, the DESTINEX number is at cutoff 1.065 based on the Youden Cutoff value, and the 17-miRNA signature is at cutoff 0.349 based on the Youden Cutoff value. In the Validation Cohort in Table 3, the DESTINEX number is at cutoff 1.065 as determined from the training cohort, and the 17-miRNA signature is at cutoff 0.349 as determined from the training cohort. AUC: Area Under the Curve; PPV : Positive Predictive Value; NPV: Negative Predictive Value

[0236] Early diagnosis is a necessary healthcare strategy in all settings, as it significantly improves clinical outcomes. Early GC (EGC) is defined as cancer confined to the mucosal or submucosal layer (stage Tl), regardless of lymph node metastasis. Our results revealed DESTINEX risk scores were consistently higher even in pTl stage patients (p < 0.001, FIG. 12A). The DESTINEX assay exhibited superior ability in detecting EGC in the validation cohort (AUC of pTl vs. pT2-4: 96.8% vs. 93.9%, FIG. 12B). The DESTINEX scores were significantly higher in GC patients, across all stages from I to IV (p < 0.001, FIG. 12C). ROC analysis highlighted DESTINEX's robust identification of EGC (stage I&II: AUC = 95.4%; stage III&IV: AUC = 94.0%, FIG. 12D).

[0237] We evaluated the sensitivity of DESTINEX in various subsets of GC patients based on clinicopathological features (pT stage, TNM stage, pathology, lymphatic invasion, FIG. 14). Using the locked cutoff identified from the training cohort, DESTINEX exhibited significantly higher sensitivity for detecting pTl stage cancers (pTl vs. pT2-4: 95% vs. 86%) and for patients with positive lymph node invasion (lymph node metastasis) (yes vs. no: 89% vs. 85%).

[0238] We further investigated the diagnostic performance of DESTINEX regarding theirspecificity at various sensitivity thresholds of 90%, 92.5%, and 95% for EGC detection in the validation cohort, summarized in Table 4. The results revealed that DESTINEX had a remarkable sensitivity even at a specificity of 90% (pTl vs. pT2-4: 93.9% vs. 82.9%) for the detection of patients with EGC. DESTINEX could robustly distinguish patients with early -stage GC fromNDCs.

[0239] Table 4: The diagnostic performance of DESTINEX at varying sensitivity thresholds for detection of early-stage (pTl) GC

[0240] In the tables: Sens is Sensitivity; Spec is Specificity. For Sensitivity The cutoff value was determined from the training cohort.

[0241] To further investigate DESTINEX specificity for GC detection, we analyzed circulating cf- and exo-miRNAs levels (miR-21-3p. miR-21-5p, miR-215-5p, miR-27a-3p, miR- 95-3p) in serum samples from a subset of 20 GC patients, comparing pre-s urgery blood samples with matched specimens collected 3 months post-surgery. We hypothesized that if the elevated cf- and exo-miRNAs levels in DESTINEX were tumor-derived, their expression would drop following tumor removal. As expected, other than exosomal miR-95-3p, the expression of all cf- and exo-miRNAs was significantly decreased in post-surgery blood serum specimens (p < 0.05, FIG. 9). Additionally, DESTINEX risk probabilities were significantly reduced in postoperative serum specimens (p < 0.001, FIG. 12E), highlighting that these markers are highly correlated with the presence of gastric neoplasia.

[0242] To ensure the specificity of DESTINEX for GC, we compared its diagnostic performance in patients with several other gastrointestinal cancers, including colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), esophageal squamous cell carcinoma (ESCC), intrahepatic cholangiocarcinoma (ICC), and hepatocellular carcinoma (HCC). DESTINEX exhibited the highest diagnostic value for GC vs. all other gastrointestinal cancers (FIG. 12F, GC: AUC = 95.7%; CRC: AUC = 59.5%; PDAC: AUC = 76.0%; ESCC: AUC = 70.3%; ICC: AUC = 58.0%; HCC: AUC = 69.8%). These results demonstrated that our non-invasive diagnostic DESTINEX signature is highly specific in detecting GC, which will enable it to have future routine clinical use.

[0243] This study indicates that cf-miRNAs offer higher sensitivity, while exo-miRNAs offer higher specificity to identify GC. The 17-miRNA signature exhibited a superior ability’ for accurate GC identification vs. the cf-miRNA or exo-miRNA panels individually. Finally, we developed a signature, DESTINEX, comprising 5 overlapping cf- and exo-miRNAs, which yielded an impressive diagnostic performance with an AUC of 94.8% in the validation cohort. These results highlight that cf-panel and exo-panel could complement each other in enhancing sensitivity and specificity, resulting in improved diagnostic accuracy. DESTINEX showed higher sensitivity in distinguishing EGC fromNDCs (pTl vs. pT2-4: 94.7% vs. 86.0%), highlighting that DESTINEX is equally effective, more feasible and economical for detecting patients with EGC compared to the 17-miRNA signature.

[0244] Methods for Example 1 and Example 2

[0245] Study design and patient cohorts. All participants gave informed written consent, and the study protocol received approval from the Institutional Review Boards of the University of Asan College of Medicine, Ajou College of Medicine, Samsung Medical Center, Nagoya Graduate School of Medicine, Hokkaido Hospital, Mie Graduate School of Medicine.

[0246] The detailed design and workflow for this study, involving a total of 809 samples, are presented in FIG. 5. Our study comprised 4 phases: an initial comprehensive and genome-wide transcriptomic sequencing-based biomarker discovery phase, which included small RNA sequencing data from a total of 189 samples; a serum-based biomarker training phase with 263 samples, subsequent validation phases with 217 samples; and finally, a diagnostic performance evaluation phase in an independent clinical cohort of 100 patients with other gastrointestinal cancers, along with 20 paired pre- and post-operative serum samples from patients diagnosed with GC.

[0247] For the biomarker discovery phase, we performed small-RNA sequencing (small- RNA-Seq) using RNA samples from 47 GC tissues and matched adjacent normal (AN) tissues, total cell-free RNA (cf-RNA), and exosomal RNA (exo-RNA) from 20 non-disease controls (NDCs, Asan, Korea), and cf- and exo-RNA from 43 and 32 GC patients, respectively (Ajou, Korea). We analyzed the sequencing data for biomarker discovery using elaborate bioinformatics to identify and prioritize biomarkers for the subsequent training phase. For the serum-based training phase, we performed real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) assays to evaluate the expression levels of cf- and exo- miRNAs in 263 serum specimens collected from 161 GC patients and 102 NDCs who wereenrolled at the Nagoya University Hospital (Nagoya, Japan) between 2016 and 2020. For the serum-based validation phase, we investigated the performance of the trained cf- and exo- miRNAs panel in an additional independent clinical cohort consisting of 131 GC patients enrolled at Ajou University (Suwon, Korea) between 2017 and 2020 and 86 NDC subjects enrolled at Asan Medical Center (Seoul, Korea) and Samsung Medical Center (Seoul, Korea) between 2009 and 2017. The clinicopathological characteristics of each clinical cohort are described in FIG. 17. To evaluate the diagnostic performance of the final validated miRNA signature, 20 matched pairs of pre- and post-operative serum specimens from patients with GC enrolled at Ajou University' (Suwon, Korea) were analyzed. Finally, to confirm the specificity of our combination signature for GC, we compared its performance with other gastrointestinal cancers by analyzing serum specimens from 20 cases each of esophageal squamous cell carcinoma (ESCC) and pancreatic ductal adenocarcinoma (PDAC) from patients enrolled at Nagoya University (Nagoya, Japan), colorectal cancer (CRC) cases from the Mie University (Mie, Japan), and hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) cases from Hokkaido University (Hokkaido, Japan).

[0248] Tissue, cell-free, and exosomal RNA extraction. To prepare small RNA sequencing libraries from the tissue specimens, total RNA was isolated from fresh-frozen GC tissues and matched AN tissues using AllPrep DNA / RNA / miRNA Universal Kit (Qiagen, Valencia, CA, USA). Similarly, to generate the small RNA sequencing libraries for blood specimens, the total cf- and exo-RNA were isolated from 400 pl serum using the miRNeasy kit and exoRNeasy Midi Kit (Qiagen), respectively. For the qRT-PCR assays, exosomes were initially isolated from 200 pl serum using a Total Exosome Isolation Kit (Invitrogen, Waltham, MA, USA), followed by RNA extraction using the miRNeasy Kit (Qiagen). Cell-free total RNA was isolated from 200 pl serum using miRNeasy Kit directly (Qiagen).

[0249] Small RNA sequencing. Total tissue RNA, cf-RNA, and exo-RNA were used for generating small-RNA-Seq libraries using NEXTflex Small RNA-Seq Kit v3 (PerkinElmer, Waltham, MA). After size exclusion and quality assurance, the sequencing libraries were pooled and paired-end sequencing was performed on an Illumina NovaSeq platform. For the analysis of raw sequencing data, following quality control by FASTQC (v0.12) and adaptor trimming by Cutadapt (v3.4), the miRDeep2 tool was used for miRNA alignment (against miRbase release 22) and quantification of miRNA expression.

[0250] Biomarker discovery analysis for identification of cf- and exo-miRNAs. We analyzed genome-wide small-RNA-Seq expression profiling data during the biomarker discovery phase to identity' differentially expressed miRNAs (DEMs) in patients with GC. The differentiallyexpressed gene analysis was performed using the Timma' package26. The tissue DEMs were filtered at a |log2foldchange (log2FC) | > 0.5 and a p-value < 0.01. Cf- and exo-miRNAs were filtered at a log2FC > 1 and a -value < 0.01. Finally, the miRNA candidates that overlapped between tissue, cell-free, and exosomal fractions were selected for further biomarker training and validation phases.

[0251] RT-qPCR assays. The complementary DNA (cDNA) was synthesized using the miRCURY LNA RT Kit (Qiagen). The expression level of each miRNA candidate was quantified using a SensiFASTIMSYBR® LO-ROX Kit (Bioline, London, UK) on a QuantStudio 7 flex real-time quantitative PCR system (Applied Biosystems, Foster City, CA). After evaluation of endogenous control miRNAs reported in the literature, including miR-16-5p. RNU6B, miR-39-3p. and miR-30a-5p, RNU6B and miR-16-5p were determined to be the optimal controls in cf- and exo-RNA respectively, based on their stable expression across all samples (NGC and GC). The expression values of miRNAs were calculated by the 2'ACTmethod.

[0252] Statistical analysis. All statistical analyses were performed within the R-software suite (version 4.3.2) and GraphPad Prism (Version 9). The differential expression analysis between GC patients and NDC subjects was performed using the “limma” package in R. The volcano and heatmap plots were generated by the “ggplot2” and “pheatmap” packages, respectively. The least absolute shrinkage and selection operator (LASSO) based machine learning algorithm was performed using the “glmnet” and the binary logistic regression “glm” functions. To evaluate the performance of diagnostic biomarkers, the receiver operating characteristic curve (ROC) analysis was conducted using the “pROC” package. The decision curve analysis (DC A) was developed to delineate the net benefit value of the miRNA signature by using the “rmda” function. Calibration curve analysis was applied to assess the calibration of the miRNA signature using the “CalibrationCurves” function in R. The confusion matrix analysis was conducted to evaluate the diagnostic performance of the miRNA signature via the “cvms” package in R. The Mann-Whitney U and t-test were applied to compare two independent groups with continuous variables. A / i- value of < 0.05 was considered a statistically significant change.

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Claims

CLAIMSWhat is claimed is:

1. A method of detecting an RNA biomarker in a patient having, or suspected of having, gastric cancer, the method comprising detecting the RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p. or a combination of two or more thereof.

2. The method of claim 1, comprising detecting an elevated expression level, relative to a control, of the RNA in the biological sample obtained from the patient.

3. A method of treating gastric cancer in a patient in need thereof, the method comprising:(i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from a patient, wherein the RNA comprises miR-21- 3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b- 5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b- 5p, or a combination of two or more thereof; and(ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof.

4. A method of diagnosing a patient with gastric cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, thereby diagnosing the patient with gastric cancer; wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-18 lb-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

5. A method of monitoring a patient at risk for developing gastric cancer, the method comprising:(i) detecting an expression level of RNA in a biological sample obtained from the patient at a first point in time;(ii) detecting an expression level of RNA in a biological sample obtained from the patient at a second point in time, wherein the second point in time is later than thefirst point in time; wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

6. The method of claim 5. wherein an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has an increased risk of developing gastric cancer.

7. A method of treating gastric cancer in a patient in need thereof, the method comprising:(i) selecting a patient having a diagnosis of gastric cancer based on an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-21-3p. miR-21-5p, miR-215- 5p, miR-335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR-196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof; and(ii) treating the patient from step (i) by administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof.

8. The method of claim 1, wherein the RNA is cell-free RNA, exosomal RNA, or a combination thereof.

9. The method of claim 8, wherein the RNA comprises:(i) cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, and cell-free miR-95-3p; or(ii) exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

10. The method of claim 8. wherein the RNA comprises cell-free miR-21-3p. cell- free miR-21-5p, cell-free miR-215-5p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

11. The method of claim 8, wherein the RNA comprises cell-free miR-21-3p, cell-free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, exosomal miR-21-3p, exosomal miR-21-5p, exosomal miR-1246, exosomal miR- 192-3p, exosomal miR-215-5p, exosomal miR-27a-3p, and exosomal miR-95-3p.

12. The method of claim 8. wherein the RNA comprises cell-free miR-21-3p, cell- free miR-21-5p, cell-free miR-215-5p. cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR- 21-5p, exosomal miR-1246, exosomal miR-215-5p, exosomal miR-27a-3p, exosomal miR-95- 3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR-135b-5p.

13. The method of claim 8, wherein the RNA comprises cell-free miR-21-3p, cell- free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-18 lb-5p, cell-free miR-431-5p. exosomal miR-21-3p, exosomal miR- 21-5p, exosomal miR-1246, exosomal miR-192-3p. exosomal miR-215-5p, exosomal miR-27a- 3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, and exosomal miR- 135b-5p.

14. The method of claim 8, wherein the RNA comprises cell-free miR-21-3p, cell- free miR-21-5p, cell-free miR-215-5p, cell-free miR-335-3p, cell-free miR-27a-3p, cell-free miR-95-3p, cell-free miR-181b-5p, cell-free miR-431-5p, exosomal miR-21-3p, exosomal miR- 21-5p, exosomal miR-1246, exosomal miR-192-3p, exosomal miR-215-5p, exosomal miR-27a- 3p, exosomal miR-95-3p, exosomal miR-196a-5p, exosomal miR-183-5p, exosomal miR-135b- 5p, or a combination of two or more thereof.

15. The method of claim 1. wherein the biological sample is a blood sample.

16. The method of claim 1 , wherein the gastric cancer is early gastric cancer.

17. The method of claim 1, wherein the gastric cancer is T2 gastric cancer, T3 gastric cancer, or T4 gastric cancer.

18. The method of any one of claims 1, 2, 4-6, and 8-17, further comprising administering to the patient an effective amount of an anti-cancer agent.

19. The method of any one of claims 3, 7, and 18. wherein the anti-cancer agent comprises a chemotherapeutic agent, a checkpoint inhibitor, or a combination thereof.

20. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises miR-21-3p, miR-21-5p, miR-215-5p, miR- 335-3p, miR-27a-3p, miR-95-3p, miR-181b-5p, miR-431-5p, miR-1246, miR-192-3p, miR- 196a-5p, miR-183-5p, miR-135b-5p, or a combination of two or more thereof.

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