How to classify disease outcomes

Abstract

Disclosed herein are methods for predicting clinical or disease outcome in a subject with a disease. Methods for generating indices predictive of disease outcomes are also provided. The method includes a procedure for measuring miRNA levels in a cell-free sample from a subject. Further provided are compositions for doing so.

Description

[Technical field]

[0001] This application claims priority to U.S. Patent Application No. 63 / 318,105, filed March 9, 2022, and U.S. Patent Application No. 63 / 357,964, filed July 1, 2022, each of which is incorporated by reference in its entirety. [Background technology]

[0002] Cancer is one of the leading causes of death, and its incidence is rising dramatically. Accurate cancer outcome prediction and / or cancer classification is a beneficial aspect of cancer treatment, and various diagnostics, such as imaging, biopsy, and various laboratory-based tests, are used to achieve that goal. However, current methods lack sufficient sensitivity and / or accuracy, at least in part due to the complex interactions between various cancer types / subtypes and various external factors (e.g., environmental factors) and / or subject's intrinsic factors (e.g., genotype). Current methods are insufficient and ineffective in predicting disease outcomes, hindering the introduction of effective treatments. Patients with more severe disease outcomes may require treatments with additional resources (e.g., dosage or frequency of treatment), and vice versa. Furthermore, current methods for cancer prediction may require extensive invasive procedures, preventing the application of the methods and cancer characterization to the entire population, which may contribute to ineffective prediction of cancer outcome prediction and / or cancer classification. In some cases, current methods may not prevent early detection of disease. In other cases, patients may first be diagnosed at an advanced stage with widespread metastases, which contribute to the cancer's high fatality rate. Summary of the Invention [Means for solving the problem]

[0003] Appropriate cancer outcome prediction and / or cancer classification may lead to effective cancer treatment. Provided herein are methods, compositions, or kits for generating an index for predicting disease outcome in a subject. The disease may include cancer. The index may help accurately, effectively, and / or efficiently predict disease outcome in a subject so that appropriate treatment options can be presented to the subject. In some cases, the method may include stratifying a population of subjects having similar types or subtypes of cancer, thereby minimizing differences in cancer type / subtype that may confound disease outcome prediction. In some cases, accurate prediction of disease outcome may facilitate the presentation of effective treatment to a subject according to disease state. In some cases, accurate prediction of disease outcome may reduce disease morality and improve the quality of life expectancy of a subject. In some embodiments, miRNAs in a cell-free sample may be used to accurately, effectively, and / or efficiently predict disease outcome. When miRNAs are encapsulated by extracellular vesicles, they may be kept stable in the cell-free sample. The methods and / or compositions can enable accurate prediction of disease outcomes using acellular samples or samples obtained by non-invasive collection. Such methods can facilitate cancer outcome prediction and / or cancer classification across a population, which can lead to increasingly accurate cancer outcome prediction and / or cancer classification. In some cases, the methods provided herein can enable disease outcome prediction using two or more biomarkers, which can improve accuracy compared to methods using only one biomarker. The methods can also enable the identification of biomarkers specific for disease outcome prediction, disease classification, or a combination thereof. In some cases, the methods can also enable early detection of disease.

[0004] Provided herein is a method.In one embodiment, the method can include: (a) obtaining the index derived from at least two micro ribonucleic acid (miRNA) of the subject with ovarian cancer; and (b) determining the outcome of ovarian cancer of the subject, wherein at least two miRNA are obtained from the cell-free sample of the subject.

[0005] In some embodiments, the ovarian cancer comprises type I ovarian cancer, type II ovarian cancer, or a combination thereof. In some embodiments, the ovarian cancer comprises type I ovarian cancer. In some embodiments, the type I ovarian cancer comprises endometrioid carcinoma, ovarian clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, or a combination thereof. In some embodiments, the type I ovarian cancer comprises endometrioid carcinoma. In some embodiments, the type I ovarian cancer comprises ovarian clear cell carcinoma. In some embodiments, the type I ovarian cancer comprises mucinous carcinoma. In some embodiments, the type I ovarian cancer comprises low-grade serous carcinoma. In some embodiments, the ovarian cancer comprises type II ovarian cancer. In some embodiments, the type II ovarian cancer comprises high-grade serous ovarian cancer. In some embodiments, the ovarian cancer comprises epithelial ovarian cancer, germ cell tumor, stromal cell tumor, or a combination thereof. In some embodiments, the ovarian cancer comprises epithelial ovarian cancer. In some embodiments, the ovarian cancer comprises germ cell tumor. In some embodiments, the ovarian cancer comprises stromal cell tumor.

[0006] In some embodiments, the at least two miRNAs comprise miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, miR-6805-5p, or a combination thereof. In some embodiments, the at least two miRNAs comprise miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, or a combination thereof. In some embodiments, the at least two miRNAs comprise miR-187-5p. In some embodiments, the at least two miRNAs comprise miR-6870-5p. In some embodiments, the at least two miRNAs comprise miR-1908-5p. In some embodiments, the at least two miRNAs comprise miR-6727-5p. In some embodiments, the at least two miRNAs comprise miR-187-5p and miR-6870-5p. In some embodiments, the at least two miRNAs comprise miR-187-5p, miR-6870-5p, and miR-1908-5p. In some embodiments, the at least two miRNAs comprise miR-187-5p, miR-6870-5p, and miR-6727-5p. In some embodiments, the at least two miRNAs comprise miR-187-5p, miR-6870-5p, miR-1908-5p, and miR-6727-5p. In some embodiments, the at least two miRNAs comprise miR-150-3p, miR-3195, miR-7704, or a combination thereof. In some embodiments, the at least two miRNAs comprise miR-150-3p. In some embodiments, the at least two miRNAs comprise miR-3195. In some embodiments, the at least two miRNAs comprise miR-7704.In some embodiments, the at least two miRNAs include miR-150-3p, miR-3195, and miR-7704.

[0007] In some embodiments, the index is derived from the levels of at least two miRNAs. In some embodiments, the levels of at least two miRNAs include expression levels of at least two miRNAs or derivatives of expression levels of at least two miRNAs. In some embodiments, the index is obtained by processing the levels of at least two miRNAs in at least two subjects with ovarian cancer. In some embodiments, the index is obtained by processing the levels of at least two miRNAs in at least 10 subjects with ovarian cancer. In some embodiments, the index is obtained by processing the levels of at least the at least two miRNAs in at least 50 subjects with ovarian cancer. In some embodiments, the index is obtained by processing the levels of at least the at least two miRNAs in at least 100 subjects with ovarian cancer. In some embodiments, the index comprises a formula comprising the levels of at least two miRNAs. In some embodiments, the formula is (a) 0.218×(the level of miR-187-5p)+0.280×(the level of miR-6870-5p), or (b) 0.148×(said level of miR-187-5p)+0.273×(said level of miR-6870-5p)+0.186×(the level of miR-1908-5p), or (c) 0.034×(said level of miR-187-5p)+0.025×(said level of miR-6870-5p)+0.186×(the level of miR-1908-5p). (said level of miR-187-5p) + 0.236 × (said level of miR-6870-5p) + 0.504 × (said level of miR-6727-5p) + 0.048 × (said level of miR-1908-5p), or (d) 0.031 × (said level of miR-187-5p) + 0.231 (said level of miR-6870-5p) + 0.351 × (said level of miR-6727-5p).

[0008] In some embodiments, the formula comprises (a) 0.463 × (level of miR-150-3p) + 1.323 × (level of miR-3195) + 0.636 × (level of miR-7704), or (b) 0.399 × (said level of miR-150-3p) + 1.426 × (said level of miR-3195) + 0.480 × (said level of miR-7704).

[0009] In some embodiments, the index is obtained by processing the levels of at least two miRNAs with an algorithm. In some embodiments, the algorithm comprises a statistical model. In some embodiments, the statistical model comprises a linear regression model. In some embodiments, the linear regression model comprises a Cox model. In some embodiments, the Cox model comprises a univariate Cox model or a multivariate Cox model. In some embodiments, the Cox model comprises a univariate Cox model. In some embodiments, the Cox model comprises a multivariate Cox model. In some embodiments, the levels of at least two miRNAs are determined by microarray, sequencing reaction, probe hybridization, polymerase chain reaction (PCR), or a combination thereof. In some embodiments, the levels of at least two miRNAs are determined by microarray.

[0010] In some embodiments, the ovarian cancer is stage I ovarian cancer, stage II ovarian cancer, stage III ovarian cancer, or stage IV ovarian cancer. In some embodiments, the stage I ovarian cancer comprises stage IA ovarian cancer or stage IB ovarian cancer. In some embodiments, the stage II ovarian cancer comprises stage IIA ovarian cancer or stage IIB ovarian cancer. In some embodiments, the stage III ovarian cancer comprises stage IIIA ovarian cancer, stage IIIB ovarian cancer, or stage IIIC ovarian cancer. In some embodiments, the outcome comprises the amount of time that the ovarian cancer does not progress to the next stage while the subject is undergoing treatment for ovarian cancer or after the subject is treated for ovarian cancer. In some embodiments, the outcome comprises the amount of time that the subject survives after the subject is determined to have ovarian cancer or after the subject is treated for ovarian cancer. In some embodiments, the outcome comprises progression-free survival (PFS) or overall survival (OS). In some embodiments, the outcome comprises PFS. In some embodiments, it comprises OS.

[0011] In some embodiments, the subject's acellular sample comprises a subject's bodily fluid. In some embodiments, the bodily fluid comprises serum, urine, sweat, plasma, tears, semen, vaginal fluid, amniotic fluid, milk, or a combination thereof. In some embodiments, the bodily fluid sample comprises serum. In some embodiments, at least two miRNAs are derived from the subject's extracellular vesicles.

[0012] Another aspect of the disclosure provides a non-transitory computer-readable medium containing machine-executable code that, when executed by one or more computer processors, implements any of the methods described above or elsewhere herein.

[0013] Another aspect of the disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto, the computer memory including machine executable code that, when executed by the one or more computer processors, implements any of the methods described above or elsewhere herein.

[0014] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, in which merely exemplary embodiments of the present disclosure are shown and described. As will be understood, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description should be regarded as illustrative in nature, and not as restrictive.

[0015] Incorporation by Reference All publications, patents, and patent applications mentioned herein are incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent that the publications and patents or patent applications incorporated by reference conflict with the present disclosure contained herein, the present specification is intended to supersede and / or take precedence over any such conflicting material.

[0016] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as "figures"), in which: [Brief description of the drawings]

[0017] [Figure 1] An overview of patient and microRNA (miRNA) selection is shown. [Diagram 2]1 shows Kaplan-Meier curves showing overall survival (OS) of high-grade serous ovarian cancer patients stratified according to serum miRNA levels. Patients were stratified according to the median level of each miRNA. [Diagram 3] 1 shows the expression levels of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, and miR-6850-5p in the serum of subjects with high-grade serous ovarian cancer (HGSOC) versus healthy subjects. [Figure 4A] Figure 1 shows Kaplan-Meier curves showing overall survival (OS) stratified by the median value of exemplary endpoints. n indicates the number of subjects. [Figure 4B] 4 shows Kaplan-Meier curves showing OS stratified by the median of another exemplary endpoint, where n indicates the number of subjects. [Figure 5A] 1 shows Kaplan-Meier curves showing progression-free survival (PFS) stratified by the median value of an exemplary endpoint. n indicates the number of subjects. [Figure 5B] 1 shows Kaplan-Meier curves showing PFS stratified by the median of another exemplary endpoint, where n indicates the number of subjects. [Figure 6A] Kaplan-Meier curves showing OS stratified by high, intermediate, and low exemplary endpoints are shown. n indicates the number of subjects. P values ​​(p) for various comparisons between endpoint groups are shown. [Figure 6B] Kaplan-Meier curves showing high, intermediate, and low PFS stratified by exemplary endpoints. n indicates the number of subjects. P values ​​(p) for various comparisons between endpoint groups are shown. [Figure 6C] 6A shows a correlation plot between exemplary indices for OS in FIG. 6A and exemplary indices for PFS in FIG. 6B. The squared correlation coefficient (R2) and P-value (p) are shown. [Figure 7A]The relative expression levels of miR-187-5p, miR-1908-5p, and miR-6870-5p (mimic) transfected into A2780 and SK-OV-3 cell lines are shown. NC indicates the negative control. [Figure 7B] Proliferation assay of cells transfected with the miRNAs of FIG. 7A is shown. [Figure 7C] 7 shows proliferation assays of cells transfected with the miRNAs of FIG. 7A in the presence of various drugs. [Figure 8] An overview of patient selection is provided. [Figure 9A] Hazard ratios (HR) and 95% confidence intervals (CI) for overall survival (OS) calculated with miRNA levels as continuous variables are shown. Heatmap shows miRNA expression from ovarian clear cell carcinoma tissues. [Figure 9B] Hazard ratios (HR) and 95% confidence intervals (CI) for progression-free survival (PFS) calculated with miRNA levels as continuous variables are shown. Heatmap shows expression of miRNAs from ovarian clear cell carcinoma tissues. [Figure 10-1] 1 shows Kaplan-Meier curves showing OS and PFS of patients with ovarian clear cell carcinoma stratified according to serum microRNA levels. Patients were stratified according to the median level of each microRNA. [Figure 10-2] A continuation of Figure 10-1 is shown. [Figure 11] We present an overview of candidate miRNA selection, selected by expression of miRNAs from ovarian clear cell carcinoma (OCCC) tissues according to serum microRNA levels using univariate and multivariate Cox regression analysis in patients with clear cell carcinoma. [Figure 12A] Kaplan-Meier curves showing OS stratified with the index-median OS are shown. Patients were stratified according to the median levels of each microRNA. [Figure 12B]Kaplan-Meier curves showing PFS stratified with median OS. Patients were stratified according to the median levels of each microRNA. [Figure 13A] Serum levels of miRNAs stratified with median values ​​of exemplary endpoints for OS and exemplary endpoints for PFS are shown. [Figure 13B] 13B shows a correlation plot between the exemplary endpoints for OS and PFS from FIG. 13A. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] definition The term "cancer," as used herein, refers to or describes a physiological condition in a mammal involving unregulated cell growth. Cancer may include cells or tissues involving unregulated cell growth.

[0019] The terms "determining," "measuring," "evaluating," "assessing," "assaying," and "analysing," as used herein, refer to various types of measurements. The terms include determining whether an element is present or not (e.g., detecting). The terms can include quantitative, qualitative, or both quantitative and qualitative determinations. The terms can include relative or absolute determinations.

[0020] The term "subject," as used herein, refers to a biological entity. The subject can be a mammal. The mammal can be a human.

[0021] Whenever the terms "at least," "greater than," or "greater than or equal to" precede the first number in a series of two or more numbers, the term "at least" or "greater than" applies to every number in the series.

[0022] Whenever the terms "at most," "no more than," "less than," or "less than or equal to" appear before the first number in a series of two or more numbers, the term "less than" or "less than" applies to each and every number in the series.

[0023] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0024] As used herein, the term "and / or" in a phrase such as "A and / or B" is intended to include both A and B, A or B, A (single), and B (single). Similarly, the term "and / or" in a phrase such as "A, B, and / or C" is intended to include each of the following specific examples: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (single); B (single); C (single).

[0025] The term "about" or "approximately" as used herein when referring to a measurable value, such as an amount or concentration, includes a variation of 20%, 10%, 5%, 1%, 0.5%, or even 0.1% of the stated amount. For example, "about" can mean plus or minus 10%, as per convention in the art. Alternatively, "about" can mean a range of plus or minus 20%, plus or minus 10%, plus or minus 5%, or plus or minus 1% of a given value. Alternatively, particularly for biological systems or processes, the term can mean within an order of magnitude, up to 5-fold, or up to 2-fold of the value. Where specific values ​​may be recited in the present application and claims, unless otherwise specified, the term "about" should be construed to mean up to an acceptable margin of error for the particular value. Also, where ranges, subranges, or both of values ​​may be presented, the ranges or subranges can include the endpoints of the ranges or subranges. The terms "substantially," "substantially no," "substantially free," and "approximately" can be used when describing a size, a location, or both to indicate that the stated value can be up to a range of values ​​that are reasonably expected. For example, a numerical value can have a value that can be + / - 0.1% of the specified value (or range of values), + / - 1% of the specified value (or range of values), + / - 2% of the specified value (or range of values), + / - 5% of the specified value (or range of values), + / - 10% of the specified value (or range of values), etc. Any numerical ranges described herein can be intended to include all subranges subsumed therein.

[0026] method In some examples, the methods described herein can include generating an index that can be used to predict a clinical or disease outcome in a subject having a disease of interest. Provided herein are compositions or kits for carrying out the methods described herein.

[0027] The method can include obtaining an index derived from micro ribonucleic acid (miRNA). The method can include obtaining an index derived from micro ribonucleic acid (miRNA) of a subject. The subject can have cancer. The method can include determining an outcome of a subject having cancer. The method can include obtaining an index derived from at least two micro ribonucleic acid (miRNA) of a subject. The method can include determining an outcome of an ovarian cancer of a subject, where the at least two miRNAs are obtained from an acellular sample of the subject. The method can include (a) obtaining an index derived from at least two micro ribonucleic acid (miRNA) of a subject having ovarian cancer, and (b) determining an outcome of an ovarian cancer of the ...

[0028] cancer In some examples, the methods described herein can predict a disease outcome for a subject with cancer. The methods can predict a disease outcome for a subject with ovarian cancer. In some examples, the methods can predict a clinical outcome or disease outcome for a cancer type in a subject. In some examples, the methods can predict a clinical outcome or disease outcome for a cancer subtype in a subject. In some examples, the methods can predict a clinical outcome or disease outcome for an ovarian cancer type in a subject. In some examples, the methods can predict a clinical outcome or disease outcome for an ovarian cancer subtype in a subject.

[0029] In some instances, there may be two types of ovarian cancer. In some instances, the two types of ovarian cancer are type I ovarian cancer and type II ovarian cancer. In some instances, type I ovarian cancer may be a slow-growing, indolent neoplasm arising from a precursor lesion in the ovary. In some instances, type I ovarian cancer may include endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, or low-grade serous carcinoma. In some instances, type II cancer may be a serous tubal intraepithelial carcinoma (STIC) and / or a clinically invasive neoplasm that may develop de novo from the ovarian surface epithelium. In some instances, type II cancer may include high-grade serous carcinoma (HGSOC).

[0030] In some examples, the type of ovarian cancer may include epithelial ovarian cancer, germ cell tumors, stromal cell tumors, or a combination thereof. In some examples, epithelial ovarian cancer may include cancer cells that cover the outer surface of the ovaries. The cancer cells may spread to the pelvic and abdominal linings and organs, and then to other parts of the body. In some examples, germ cell tumors may begin in the germ cells (e.g., eggs) of the ovaries. In some examples, stromal cell tumors may form in tissues that support the ovaries. In some examples, the method can predict disease outcomes for subjects with two of epithelial ovarian cancer, germ cell tumors, and stromal cell tumors. In some examples, the method can predict disease outcomes for subjects with epithelial ovarian cancer, germ cell tumors, and stromal cell tumors. In some examples, the method can predict disease outcomes for subjects with epithelial ovarian cancer. In some examples, the method can predict disease outcomes for subjects with germ cell tumors. In some examples, the method can predict disease outcomes for subjects with stromal cell tumors.

[0031] The subject may have endometrioid ovarian cancer, mucinous ovarian cancer, serous ovarian cancer, clear cell ovarian cancer, or a combination thereof. The subject may have endometrioid ovarian cancer or mucinous ovarian cancer. The subject may have endometrioid ovarian cancer or serous ovarian cancer. The subject may have mucinous ovarian cancer or serous ovarian cancer. In some examples, the method can predict disease outcomes of subjects with at least two of endometrioid ovarian cancer, mucinous ovarian cancer, serous ovarian cancer, and clear cell ovarian cancer. In some examples, the method can predict disease outcomes of subjects with at least three of endometrioid ovarian cancer, mucinous ovarian cancer, serous ovarian cancer, and clear cell ovarian cancer. In some examples, the method can predict disease outcomes of subjects with endometrioid ovarian cancer, mucinous ovarian cancer, serous ovarian cancer, and clear cell ovarian cancer.

[0032] In some examples, the method can predict disease outcomes for subjects with type I or type II ovarian cancer. In some examples, the method can predict disease outcomes for subjects with type I and type II ovarian cancer. In some examples, the method can predict disease outcomes for subjects with type I ovarian cancer. In some examples, the method can predict disease outcomes for subjects with type II ovarian cancer. In some cases, the method can predict disease outcomes for subjects with endometrioid carcinoma. In some cases, the method can predict disease outcomes for subjects with clear cell carcinoma. In some cases, the method can predict disease outcomes for subjects with mucinous carcinoma. In some cases, the method can predict disease outcomes for subjects with low-grade serous carcinoma. In some cases, the method can predict disease outcomes for subjects with HGSOC. In some cases, the method can predict disease outcomes for subjects with endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, HGSOC, or a combination thereof. In some cases, the method can predict disease outcomes of subjects with at least two of endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, and HGSOC. In some cases, the method can predict disease outcomes of subjects with at least three of endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, and HGSOC. In some cases, the method can predict disease outcomes of subjects with at least four of endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, and HGSOC. In some cases, the method can predict disease outcomes of subjects with endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, and HGSOC.

[0033] In some cases, the cancer may have stages of cancer. The cancer may include stage I cancer, stage II cancer, stage III cancer, stage IV cancer, or a combination thereof. The cancer may also include stage 0 cancer, stage I cancer, stage II cancer, stage III cancer, or stage IV cancer. The cancer may include stage 0 cancer. The cancer may include stage I cancer. The cancer may include stage II cancer. The cancer may include stage III cancer. The cancer may include stage IV cancer.

[0034] A subject with stage 0 cancer may not have cancer, but is at risk of developing cancer. For example, a subject may have neoplastic cells that may develop into cancer. Stage I cancer may include small cancers. Stage I cancer may be localized to one area, tissue, or organ. Stage I cancer may be an early stage cancer. Stage I cancer may not have grown deep into tissue adjacent to its primary site. Stage I cancer may not have grown to lymph nodes. In some examples, stage I cancer may include stage IA cancer or stage IB cancer. In some examples, stage I cancer may include stage IA cancer. In some examples, stage I cancer may include stage IB cancer. Stage IA cancer may include stage I cancer with a tumor that is at most about 2 centimeters (cm) in cross section. Stage IB cancer may include stage I cancer with a tumor that is at least about 2 cm in cross section. Stage IB cancer may include stage I cancer with a tumor that is at most about 4 cm in cross section. Stage IB cancer can include stage I cancer having a tumor measuring about 2 to 4 cm in cross section.

[0035] In some instances, stage II or III cancer may include cancer that has spread to tissues or lymph nodes adjacent to its primary site. In some instances, stage II cancer may include cancer that has not spread to lymph nodes. Stage II cancer is larger in size, volume, or weight than stage I cancer. In some instances, stage II cancer may include stage IIA cancer or stage IIB cancer. In some instances, stage II cancer may include stage IIA cancer. In some instances, stage II cancer may include stage IIB cancer. Stage IIA cancer may include stage II cancer having a tumor that is at least about 4 cm in cross section and has not spread to lymph nodes. Stage IIB cancer may include stage II cancer that has spread to up to about 3 lymph nodes. Stage IIB cancer may include stage II cancer that is at most about 2 cm in cross section and has spread to up to about 3 lymph nodes. Stage IIB cancer can include stage II cancer that has spread to about 2 to 4 lymph nodes at a cross section and up to about 3 lymph nodes. Stage IIB cancer can include stage II cancer that has spread to at least about 4 lymph nodes at a cross section and up to about 3 lymph nodes.

[0036] Stage III cancers are larger in size, volume, or weight than stage II cancers. Stage III cancers may have a greater penetration into tissue than stage II cancers. In some instances, stage III cancers may have spread to at least four lymph nodes. In some instances, stage III cancers may include stage IIIA cancer, stage IIIB cancer, or stage IIIC cancer. In some instances, stage III cancers may include stage IIIA cancer. In some instances, stage III cancers may include stage IIIB cancer. In some instances, stage III cancers may include stage IIIC cancer. Stage IIIA cancers may include stage III cancers that are up to about 2 cm in cross section and have spread to at least about four lymph nodes. Stage IIIB cancers may include stage III cancers that are about 2 to 4 cm in cross section and have spread to at least about four lymph nodes. Stage IIIB cancers may include stage III cancers that are at least about 4 cm in cross section and have spread to at least about four lymph nodes.

[0037] In some instances, stage IV cancer may include cancer that has spread to other organs or sites in the subject relative to the site / tissue in which the cancer originated. In some instances, stage IV cancer may include advanced or metastatic cancer.

[0038] In some cases, the method can predict a disease outcome for a subject having cancer at a cancer stage. In some cases, the method can predict a disease outcome for a subject having stage 0 cancer, stage I cancer, stage II cancer, stage III cancer, or stage IV cancer. In some cases, the method can predict a disease outcome for a subject having stage 0 cancer. In some cases, the method can predict a disease outcome for a subject having stage I cancer. In some cases, the method can predict a disease outcome for a subject having stage IA cancer. In some cases, the method can predict a disease outcome for a subject having stage IB cancer. In some cases, the method can predict a disease outcome for a subject having stage IIA cancer. In some cases, the method can predict a disease outcome for a subject having stage IIB cancer. In some cases, the method can predict a disease outcome for a subject having stage IIIA cancer. In some cases, the method can predict a disease outcome for a subject having stage IIIB cancer. In some cases, the method can predict disease outcome for a subject with stage IIIC cancer. In some cases, the method can predict disease outcome for a subject with stage IV cancer.

[0039] In some cases, the method can predict disease outcomes for subjects with stage 0 ovarian cancer, stage I ovarian cancer, stage II ovarian cancer, stage III ovarian cancer, stage IV ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage 0 ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage I ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IA ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IB ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IIA ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IIB ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IIIA ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IIIB ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IIIC ovarian cancer. In some cases, the method can predict disease outcomes for subjects with stage IV ovarian cancer.

[0040] In some cases, the cancer may also include breast cancer, colorectal cancer, lung cancer, ovarian cancer, pancreatic cancer, or a combination thereof. In some cases, the cancer may include at least two of breast cancer, colorectal cancer, lung cancer, ovarian cancer, and pancreatic cancer. In some cases, the cancer may include at least two of liver cancer, bladder cancer, pancreatic cancer, lung cancer, or prostate cancer. In some cases, the cancer may include at least three of breast cancer, colorectal cancer, lung cancer, ovarian cancer, and pancreatic cancer. In some cases, the cancer may include at least four of breast cancer, colorectal cancer, lung cancer, ovarian cancer, and pancreatic cancer. In some cases, the cancer may include breast cancer. In some cases, the cancer may include colorectal cancer. In some cases, the cancer may include lung cancer. In some cases, the cancer may include ovarian cancer. In some cases, the cancer may include pancreatic cancer. The cancer may also include lymphoma, blastoma, sarcoma, leukemia, squamous cell carcinoma, peritoneal cancer, hepatocellular carcinoma, gastric cancer, glioblastoma, cervical cancer, liver cancer, bladder cancer, gallbladder cancer, colon cancer, endometrial or uterine cancer, salivary gland cancer, kidney or renal cancer, renal cell carcinoma, prostate cancer, vulvar cancer, thyroid cancer, and head and neck cancer.

[0041] In some cases, the type of lung cancer may include small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), lung carcinoid tumor, adenoid cystic carcinoma, lymphoma, or sarcoma. In some examples, the method can predict disease outcomes for subjects with two of SCLC, NSCLC, lung carcinoid tumor, adenoid cystic carcinoma, lymphoma, and sarcoma. In some examples, the method can predict disease outcomes for subjects with three of SCLC, NSCLC, lung carcinoid tumor, adenoid cystic carcinoma, lymphoma, and sarcoma. In some examples, the method can predict disease outcomes for subjects with four of SCLC, NSCLC, lung carcinoid tumor, adenoid cystic carcinoma, lymphoma, and sarcoma. In some examples, the method can predict disease outcomes for subjects with five of SCLC, NSCLC, lung carcinoid tumor, adenoid cystic carcinoma, lymphoma, and sarcoma. In some examples, the method can predict disease outcomes for subjects with SCLC, NSCLC, lung carcinoid tumors, adenoid cystic carcinoma, lymphoma, and sarcoma. In some examples, the method can predict disease outcomes for subjects with SCLC. In some examples, the method can predict disease outcomes for subjects with NSCLC. In some examples, the method can predict disease outcomes for subjects with lung carcinoid tumors. In some examples, the method can predict disease outcomes for subjects with sarcoma. In some examples, the method can predict disease outcomes for subjects with adenoid cystic carcinoma. In some examples, the method can predict disease outcomes for subjects with lymphoma.

[0042] In some examples, the NSCLC subtypes may include lung adenocarcinoma, lung squamous cell carcinoma, large cell (undifferentiated) carcinoma, adenosquamous cell carcinoma, sarcomatoid carcinoma, or any combination thereof. In some examples, the method can predict disease outcomes of subjects with two of lung adenocarcinoma, lung squamous cell carcinoma, large cell (undifferentiated) carcinoma, adenosquamous cell carcinoma, and sarcomatoid carcinoma. In some examples, the method can predict disease outcomes of subjects with three of lung adenocarcinoma, lung squamous cell carcinoma, large cell (undifferentiated) carcinoma, adenosquamous cell carcinoma, and sarcomatoid carcinoma. In some examples, the method can predict disease outcomes of subjects with four of lung adenocarcinoma, lung squamous cell carcinoma, large cell (undifferentiated) carcinoma, adenosquamous cell carcinoma, and sarcomatoid carcinoma. In some examples, the method can predict disease outcomes of subjects with lung adenocarcinoma, lung squamous cell carcinoma, large cell (undifferentiated) carcinoma, adenosquamous cell carcinoma, and sarcomatoid carcinoma.

[0043] In some examples, the method can predict disease outcomes for subjects with lung adenocarcinoma. In some examples, the method can predict disease outcomes for subjects with lung squamous cell carcinoma. In some examples, the method can predict disease outcomes for subjects with large cell (anaplastic) carcinoma. In some examples, the method can predict disease outcomes for subjects with adenosquamous carcinoma. In some examples, the method can predict disease outcomes for subjects with sarcomatoid carcinoma.

[0044] Colorectal cancer may include colorectal adenocarcinoma, gastrointestinal carcinoid tumor, primary colorectal lymphoma, gastrointestinal stromal tumor, leiomyosarcoma, squamous cell carcinoma, familial adenomatous polyposis, or a combination thereof. Colorectal adenocarcinoma may include mucinous adenocarcinoma or signet ring cell adenocarcinoma. Pancreatic cancer may include exocrine (non-endocrine) pancreatic cancer, pancreatic neuroendocrine carcinoma, or benign precancerous lesion. Exocrine (non-endocrine) pancreatic cancer may include pancreatic adenocarcinoma, pancreatic squamous cell carcinoma, pancreatic adenosquamous carcinoma, pancreatic gelatinoid carcinoma.

[0045] In other cases, gastric cancer may include gastrointestinal cancer or gastrointestinal stromal cancer. Melanoma may include superficial spreading melanoma, lentigo maligna melanoma, acral lentigo melanoma, nodular melanoma. Lymphoma may include B-cell lymphoma (including low-grade / follicular non-Hodgkin's lymphoma (NHL), small lymphocytic (SL) NHL, intermediate-grade / follicular NHL, intermediate-grade diffuse NHL, high-grade immunoblastic NHL, high-grade lymphoblastic NHL, high-grade small noncleaved cell NHL, bulky mass disease NHL, mantle cell lymphoma, or AIDS-related lymphoma). The leukemia may include chronic lymphocytic leukemia (CLL), acute lymphoblastic leukemia (ALL), hairy cell leukemia, multiple myeloma, acute myeloid leukemia (AML), or chronic myeloblastic leukemia.

[0046] In some cases, the method can predict a disease outcome for a subject having stage 0 lung cancer, stage I lung cancer, stage II lung cancer, stage III lung cancer, stage IV lung cancer. In some cases, the method can predict a disease outcome for a subject having stage 0 lung cancer. In some cases, the method can predict a disease outcome for a subject having stage I lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IA lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IB lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IIA lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IIB lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IIIA lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IIIB lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IIIC lung cancer. In some cases, the method can predict a disease outcome for a subject having stage IV lung cancer.

[0047] In some cases, the method can predict a disease outcome for a subject with stage 0 breast cancer, stage I breast cancer, stage II breast cancer, stage III breast cancer, stage IV breast cancer. In some cases, the method can predict a disease outcome for a subject with stage 0 breast cancer. In some cases, the method can predict a disease outcome for a subject with stage I breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IA breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IB breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IIA breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IIB breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IIIA breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IIIB breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IIIC breast cancer. In some cases, the method can predict a disease outcome for a subject with stage IV breast cancer.

[0048] In some cases, the method can predict disease outcomes for subjects with stage 0 pancreatic cancer, stage I pancreatic cancer, stage II pancreatic cancer, stage III pancreatic cancer, stage IV pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage 0 pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage I pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IA pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IB pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IIA pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IIB pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IIIA pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IIIB pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IIIC pancreatic cancer. In some cases, the method can predict disease outcomes for subjects with stage IV pancreatic cancer.

[0049] In some cases, the method can predict a disease outcome for a subject having stage 0 colorectal cancer, stage I colorectal cancer, stage II colorectal cancer, stage III colorectal cancer, stage IV colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage 0 colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage I colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage IA colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage IB colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage IIA colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage IIB colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage IIIA colorectal cancer. In some cases, the method can predict a disease outcome for a subject having stage IIIB colorectal cancer. In some cases, the method can predict disease outcome for a subject with stage IIIC colorectal cancer. In some cases, the method can predict disease outcome for a subject with stage IV colorectal cancer.

[0050] MicroRNA In some examples, the methods described herein can predict a clinical or disease outcome of a subject with cancer using the indicator. The indicator can be derived from a nucleic acid of the subject. The nucleic acid can include deoxyribose nucleic acid (DNA) or ribonucleic acid (RNA). In some examples, the nucleic acid can be a nucleic acid molecule. In some cases, the nucleic acid can be a type / type of nucleic acid. The nucleic acid molecule can include one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. In some cases, the nucleic acid molecule can include a polymer of nucleotides. In some cases, the nucleic acid molecule can include a polynucleotide. In some cases, the nucleic acid molecule can include a modified polynucleotide. In some cases, the nucleic acid molecule can include standard or non-standard nucleotides. Standard nucleotides can include adenosine (A), cytosine (C), guanine (G), thymine (T), uracil (U), or variants thereof. In some cases, the nucleic acid can be single-stranded, double-stranded, or triple-stranded. In some cases, the beacon can be single stranded. In some cases, the nucleic acid can be double stranded. In some cases, the nucleic acid can be single stranded and double stranded. The beacon can be derived from RNA. The beacon can be derived from DNA.

[0051] The RNA for deriving the indicator may include extracellular RNA. The RNA for deriving the indicator may include RNA that is not contained in a cell just before the RNA is extracted. Such RNA may also be referred to as cell-free RNA. For example, cell-free RNA may not be contained by the plasma membrane of a cell. In other cases, cell-free RNA may not be contained or present in the cytoplasm of a cell. Cell-free RNA may be present outside a cell. The cell may include a living or viable cell. The cell may include an intact cell.

[0052] The cell-free or extracellular RNA may be secreted by a cell. In some cases, the RNA may be encapsulated by a lipid membrane. In some cases, the RNA may be encapsulated by a vesicle. In some cases, the RNA may be encapsulated by an extracellular vesicle or an extracellular membrane vesicle. In some cases, the RNA may be encapsulated by an exosome. The RNA may be circulating in the subject's body fluids. The RNA may be circulating in the subject's blood or serum. The RNA may be secreted by a viable or intact cell. The RNA may be released by a dying, non-viable, unhealthy, or non-intact cell.

[0053] The RNA may include non-coding RNA. In some cases, the RNA may include small non-coding RNA. In some cases, the length of the non-coding RNA may be up to about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or 100 nucleotides (nt) in length. The length of the non-coding RNA may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or 100 nt in length. The non-coding RNA for generating the indicator may include micro ribonucleic acid (miRNA or microRNA), transfer ribonucleic acid (tRNA), long non-coding RNA (lncRNA), ribosomal ribonucleic acid (rRNA), small nuclear RNA (snRNA), piwi-interacting RNA (piRNA), small nucleolar RNA (snoRNA), extracellular RNA (exRNA), small Cajal body-specific RNA (scaRNA), silencing ribonucleic acid (siRNA), YRNA (small non-coding RNA), heterogeneous nuclear RNA (HnRNA), or endless / circular RNA (eRNA).

[0054] In some examples, the RNA for generating the indicator may include miRNA. In some examples, the miRNA may be endogenous. In some examples, the miRNA may be single-stranded. In some examples, the miRNA may be double-stranded. In some cases, the miRNA may include both single-stranded and double-stranded regions. In some cases, the miRNA may include a step-loop region. In some examples, the miRNA may be a non-coding small RNA. In some examples, the miRNA may regulate target gene expression. In some cases, the miRNA may include a sequence complementary to a sequence of a messenger RNA (mRNA). When the miRNA base pairs with the mRNA, the miRNA may suppress, reduce, or inhibit the translation of the mRNA. In other cases, the miRNA may include a sequence complementary to a sequence of a promoter or enhancer region of a gene. When the miRNA base pairs with the promoter or enhancer region of a gene (e.g., the miRNA base pairs with the DNA of the gene promoter or enhancer), the miRNA may activate the expression or transcription of the gene.

[0055] In some cases, the miRNA may be up to about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or 100 nucleotides (nt) in length. The miRNA may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or 100 nt in length. In some cases, the miRNA may have a length of about 22 nt.

[0056] In some instances, the miRNA may be cell-free miRNA. In some instances, the cell-free miRNA may not be encapsulated by the plasma membrane of the cell. In other instances, the cell-free miRNA may not be encapsulated or present in the cytoplasm of the cell. The cell-free miRNA may be present outside the cell. The cell-free miRNA or the extracellular miRNA may be secreted by the cell. In some instances, the miRNA may be encapsulated by a lipid membrane. In some instances, the miRNA may be encapsulated by a vesicle. In some instances, the miRNA may be encapsulated by an extracellular vesicle or an extracellular membrane vesicle. In some instances, the miRNA may be encapsulated by an exosome. The miRNA may circulate in the subject's body fluids. The miRNA may circulate in the subject's blood or serum. In some instances, the miRNA may be secreted from the cell in an extracellular vesicle and may mediate intercellular communication in local and remote microenvironments.

[0057] In some cases, at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 150, 200, 500, 1000, 2000, 5000, 10000, or more miRNAs can be used to generate an index. In some cases, at least about 1 miRNA can be used to generate an index. In some cases, at least about 2 miRNAs can be used to generate an index. In some cases, at least about 3 miRNAs can be used to generate an index. In some cases, at least about four miRNAs can be used to generate the indicator. In some cases, at least about five miRNAs can be used to generate the indicator. In some cases, at least about six miRNAs can be used to generate the indicator. In some cases, at least about seven miRNAs can be used to generate the indicator. In some cases, at least about eight miRNAs can be used to generate the indicator. In some cases, at least about nine miRNAs can be used to generate the indicator. In some cases, at least about ten miRNAs can be used to generate the indicator. In some cases, at least about eleven miRNAs can be used to generate the indicator. In some cases, at least about twelve miRNAs can be used to generate the indicator. In some cases, at least about thirteen miRNAs can be used to generate the indicator. In some cases, at least about fourteen miRNAs can be used to generate the indicator. In some cases, at least about fifteen miRNAs can be used to generate the indicator. In some cases, at least about sixteen miRNAs can be used to generate the indicator. In some cases, at least about seventeen miRNAs can be used to generate the indicator. In some cases, at least about 18 miRNAs can be used to generate the index. In some cases, at least about 19 miRNAs can be used to generate the index. In some cases, at least about 20 miRNAs can be used to generate the index.In some cases, an indicator can be generated using at least about 21 miRNAs. In some cases, an indicator can be generated using at least about 22 miRNAs. In some cases, an indicator can be generated using at least about 23 miRNAs. In some cases, an indicator can be generated using at least about 24 miRNAs. In some cases, an indicator can be generated using at least about 25 miRNAs. In some cases, an indicator can be generated using at least about 26 miRNAs. In some cases, an indicator can be generated using at least about 27 miRNAs. In some cases, an indicator can be generated using at least about 28 miRNAs. In some cases, an indicator can be generated using at least about 29 miRNAs. In some cases, an indicator can be generated using at least about 30 miRNAs. In some cases, an indicator can be generated using at least about 31 miRNAs. In some cases, an indicator can be generated using at least about 32 miRNAs. In some cases, an indicator can be generated using at least about 33 miRNAs. In some cases, an indicator can be generated using at least about 34 miRNAs. In some cases, an indicator can be generated using at least about 35 miRNAs. In some cases, an indicator can be generated using at least about 36 miRNAs. In some cases, an indicator can be generated using at least about 37 miRNAs. In some cases, an indicator can be generated using at least about 38 miRNAs. In some cases, an indicator can be generated using at least about 39 miRNAs. In some cases, an indicator can be generated using at least about 40 miRNAs. In some cases, an indicator can be generated using at least about 41 miRNAs. In some cases, an indicator can be generated using at least about 42 miRNAs. In some cases, an indicator can be generated using at least about 43 miRNAs. In some cases, an indicator can be generated using at least about 44 miRNAs. In some cases, an indicator can be generated using at least about 45 miRNAs. In some cases, an indicator can be generated using at least about 46 miRNAs.In some cases, an index can be generated using at least about 47 miRNAs. In some cases, an index can be generated using at least about 48 miRNAs. In some cases, an index can be generated using at least about 49 miRNAs. In some cases, an index can be generated using at least about 50 miRNAs. In some cases, an index can be generated using at least about 100 miRNAs. In some cases, an index can be generated using at least about 150 miRNAs. In some cases, an index can be generated using at least about 200 miRNAs. In some cases, an index can be generated using at least about 500 miRNAs. In some cases, an index can be generated using at least about 1000 miRNAs. In some cases, an index can be generated using at least about 2000 miRNAs. In some cases, an index can be generated using at least about 5000 miRNAs. In some cases, an index can be generated using at least about 10,000 miRNAs. In some cases, an index can be generated using more than at least about 10,000 miRNAs.

[0058] In some cases, an index can be generated using up to about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 150, 200, 500, 1000, 2000, 5000, or 10000 miRNAs. In some cases, an index can be generated using up to about 1 miRNA. In some cases, an index can be generated using up to about 2 miRNAs. In some cases, an index can be generated using up to about 3 miRNAs. In some cases, an indicator can be generated using up to about four miRNAs. In some cases, an indicator can be generated using up to about five miRNAs. In some cases, an indicator can be generated using up to about six miRNAs. In some cases, an indicator can be generated using up to about seven miRNAs. In some cases, an indicator can be generated using up to about eight miRNAs. In some cases, an indicator can be generated using up to about nine miRNAs. In some cases, an indicator can be generated using up to about ten miRNAs. In some cases, an indicator can be generated using up to about eleven miRNAs. In some cases, an indicator can be generated using up to about twelve miRNAs. In some cases, an indicator can be generated using up to about thirteen miRNAs. In some cases, an indicator can be generated using up to about fourteen miRNAs. In some cases, an indicator can be generated using up to about fifteen miRNAs. In some cases, an indicator can be generated using up to about sixteen miRNAs. In some cases, an indicator can be generated using up to about seventeen miRNAs. In some cases, up to about 18 miRNAs can be used to generate the index. In some cases, up to about 19 miRNAs can be used to generate the index. In some cases, up to about 20 miRNAs can be used to generate the index. In some cases, up to about 21 miRNAs can be used to generate the index.In some cases, an indicator can be generated using up to about 22 miRNAs. In some cases, an indicator can be generated using up to about 23 miRNAs. In some cases, an indicator can be generated using up to about 24 miRNAs. In some cases, an indicator can be generated using up to about 25 miRNAs. In some cases, an indicator can be generated using up to about 26 miRNAs. In some cases, an indicator can be generated using up to about 27 miRNAs. In some cases, an indicator can be generated using up to about 28 miRNAs. In some cases, an indicator can be generated using up to about 29 miRNAs. In some cases, an indicator can be generated using up to about 30 miRNAs. In some cases, an indicator can be generated using up to about 31 miRNAs. In some cases, an indicator can be generated using up to about 32 miRNAs. In some cases, an indicator can be generated using up to about 33 miRNAs. In some cases, an indicator can be generated using up to about 34 miRNAs. In some cases, an indicator can be generated using up to about 35 miRNAs. In some cases, an indicator can be generated using up to about 36 miRNAs. In some cases, an indicator can be generated using up to about 37 miRNAs. In some cases, an indicator can be generated using up to about 38 miRNAs. In some cases, an indicator can be generated using up to about 39 miRNAs. In some cases, an indicator can be generated using up to about 40 miRNAs. In some cases, an indicator can be generated using up to about 41 miRNAs. In some cases, an indicator can be generated using up to about 42 miRNAs. In some cases, an indicator can be generated using up to about 43 miRNAs. In some cases, an indicator can be generated using up to about 44 miRNAs. In some cases, an indicator can be generated using up to about 45 miRNAs. In some cases, an indicator can be generated using up to about 46 miRNAs. In some cases, an indicator can be generated using up to about 47 miRNAs. In some cases, up to about 48 miRNAs can be used to generate the index.In some cases, an index can be generated using up to about 49 miRNAs. In some cases, an index can be generated using up to about 50 miRNAs. In some cases, an index can be generated using up to about 100 miRNAs. In some cases, an index can be generated using up to about 150 miRNAs. In some cases, an index can be generated using up to about 200 miRNAs. In some cases, an index can be generated using up to about 500 miRNAs. In some cases, an index can be generated using up to about 1000 miRNAs. In some cases, an index can be generated using up to about 2000 miRNAs. In some cases, an index can be generated using up to about 5000 miRNAs. In some cases, an index can be generated using up to about 10000 miRNAs.

[0059] In some cases, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 150, 200, 500, 1000, 2000, 5000, or 10000 miRNAs may be used to generate the index. In some cases, one miRNA may be used to generate the index. In some cases, two miRNAs may be used to generate the index. In some cases, three miRNAs may be used to generate the index. In some cases, an indicator can be generated using 4 miRNAs. In some cases, an indicator can be generated using 5 miRNAs. In some cases, an indicator can be generated using 6 miRNAs. In some cases, an indicator can be generated using 7 miRNAs. In some cases, an indicator can be generated using 8 miRNAs. In some cases, an indicator can be generated using 9 miRNAs. In some cases, an indicator can be generated using 10 miRNAs. In some cases, an indicator can be generated using 11 miRNAs. In some cases, an indicator can be generated using 12 miRNAs. In some cases, an indicator can be generated using 13 miRNAs. In some cases, an indicator can be generated using 14 miRNAs. In some cases, an indicator can be generated using 15 miRNAs. In some cases, an indicator can be generated using 16 miRNAs. In some cases, an indicator can be generated using 17 miRNAs. In some cases, an indicator can be generated using 18 miRNAs. In some cases, an indicator can be generated using 19 miRNAs. In some cases, 20 miRNAs can be used to generate the index. In some cases, 21 miRNAs can be used to generate the index. In some cases, 22 miRNAs can be used to generate the index. In some cases, 23 miRNAs can be used to generate the index. In some cases, 24 miRNAs can be used to generate the index.In some cases, 25 miRNAs can be used to generate the index. In some cases, 26 miRNAs can be used to generate the index. In some cases, 27 miRNAs can be used to generate the index. In some cases, an indicator can be generated using 28 miRNAs. In some cases, an indicator can be generated using 29 miRNAs. In some cases, an indicator can be generated using 30 miRNAs. In some cases, an indicator can be generated using 31 miRNAs. In some cases, an indicator can be generated using 32 miRNAs. In some cases, an indicator can be generated using 33 miRNAs. In some cases, an indicator can be generated using 34 miRNAs. In some cases, an indicator can be generated using 35 miRNAs. In some cases, an indicator can be generated using 36 miRNAs. In some cases, an indicator can be generated using 37 miRNAs. In some cases, an indicator can be generated using 38 miRNAs. In some cases, an indicator can be generated using 39 miRNAs. In some cases, an indicator can be generated using 40 miRNAs. In some cases, an indicator can be generated using 41 miRNAs. In some cases, an indicator can be generated using 42 miRNAs. In some cases, an indicator can be generated using 43 miRNAs. In some cases, an index can be generated using 44 miRNAs. In some cases, an index can be generated using 45 miRNAs. In some cases, an index can be generated using 46 miRNAs. In some cases, an index can be generated using 47 miRNAs. In some cases, an index can be generated using 48 miRNAs. In some cases, an index can be generated using 49 miRNAs. In some cases, an index can be generated using 50 miRNAs. In some cases, an index can be generated using 100 miRNAs. In some cases, an index can be generated using 150 miRNAs. In some cases, an index can be generated using 200 miRNAs. In some cases, an index can be generated using 500 miRNAs. In some cases, an index can be generated using 1000 miRNAs. In some cases, an index can be generated using 2000 miRNAs. In some cases, an index can be generated using 5000 miRNAs.In some cases, 10,000 miRNAs can be used to generate the index.

[0060] In some examples, the miRNAs used to generate the indicator may include miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, miR-6805-5p, or combinations thereof. In some examples, the miRNAs used to generate the indicator may include at least about one of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about two of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the indicator may include at least about three of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about four of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about five of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the indicator may include at least about six of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about seven of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about eight of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include at least about nine of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include at least about ten of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about eleven of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include at least about twelve of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about 13 of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include at least about 14 of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include at least about 15 of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include at least about 16 of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include at least about 17 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some instances, the miRNAs used to generate the indicators include miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, etc. It may include at least about 18 of miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include at least about 19 of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include at least about twenty of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.

[0061] In some examples, the miRNAs used to generate the indicator may include up to about one of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include up to about two of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include up to about three of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the indicator may include up to about four of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include up to about five of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include up to about six of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the indicator may include up to about seven of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include up to about eight of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include up to about nine of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include up to about ten of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include up to about eleven of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include up to about twelve of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the indicator may include up to about thirteen of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include up to about 14 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include up to about 15 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include up to about 16 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include up to about 17 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include up to about 18 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some instances, the miRNAs used to generate the indicators include miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p,. It may include up to about 19 of miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include up to about 20 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.

[0062] In some examples, the miRNAs used to generate the indicator may include one of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include two of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include three of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the indicator may include four of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include five of miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include six of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the indicator may include seven of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include eight of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include nine of the following: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include ten of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the indicator may include eleven of the following: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include twelve of the following: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include 13 of the following: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include 14 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include 15 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some examples, the miRNAs used to generate the index may include 16 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include 17 of the following: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include 18 of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.In some instances, the miRNAs used to generate the indicator are miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5. p, miR-4730, miR-671-5p, and miR-6805-5p. In some examples, the miRNAs used to generate the index may include twenty of: miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, and miR-6805-5p.

[0063] In some cases, at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 150, 200, or 210 of the miRNAs listed in Table 1 can be used to generate the index. In some cases, up to about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 150, 200, or 210 of the miRNAs listed in Table 1 can be used to generate the index. In some cases, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine ...

[0064] In some examples, the miRNA used to generate the indicator may include miR-187-5p. In some examples, the miRNA used to generate the indicator may include miR-6870-5p. In some examples, the miRNA used to generate the indicator may include miR-1908-5p. In some examples, the miRNA used to generate the indicator may include miR-6727-5p. In some examples, the miRNA used to generate the indicator may include miR-711. In some examples, the miRNA used to generate the indicator may include miR-1229-5p. In some examples, the miRNA used to generate the indicator may include miR-1914-3p. In some examples, the miRNA used to generate the indicator may include miR-4513. In some examples, the miRNA used to generate the indicator may include miR-4656. In some examples, the miRNA used to generate the indicator may include miR-4787-3p. In some examples, the miRNA used to generate the indicator may include miR-6787-5p. In some examples, the miRNA used to generate the indicator may include miR-6850-5p. In some examples, the miRNA used to generate the indicator may include miR-7107-5p. In some examples, the miRNA used to generate the indicator may include miR-7150. In some examples, the miRNA used to generate the indicator may include miR-150-3p. In some examples, the miRNA used to generate the indicator may include miR-3195. In some examples, the miRNA used to generate the indicator may include miR-7704. In some examples, the miRNA used to generate the indicator may include miR-365a-5p. In some examples, the miRNA used to generate the indicator may include miR-4730. In some examples, the miRNA used to generate the indicator may include miR-671-5p. In some examples, the miRNA used to generate the indicator may include miR-6805-5p.

[0065] In some examples, the miRNAs used to generate the index may include at least two miRNAs. In some examples, the at least two miRNAs may include miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, or any combination thereof. In some examples, the at least two miRNAs may include miR-187-5p. In some examples, the at least two miRNAs may include miR-6870-5p. In some examples, the at least two miRNAs may include miR-1908-5p. In some examples, the at least two miRNAs may include miR-6727-5p. In some examples, the at least two miRNAs may include 187-5p and miR-6870-5p. In some examples, the at least two miRNAs may include miR-187-5p, miR-6870-5p, and miR-1908-5p. In some examples, the at least two miRNAs may include miR-187-5p, miR-6870-5p, and miR-6727-5p. In some examples, the at least two miRNAs may include miR-187-5p, miR-6870-5p, miR-1908-5p, and miR-6727-5p. In some examples, the at least two miRNAs may include miR-150-3p, miR-3195, miR-7704, or any combination thereof. In some examples, the at least two miRNAs may include miR-150-3p. In some examples, the at least two miRNAs may include miR-3195. In some examples, the at least two miRNAs may include miR-7704. In some examples, the at least two miRNAs may include miR-150-3p, miR-3195, and miR-7704. In some examples, the miRNAs used to generate the index may include those miRNAs described in Example 1 or listed in Table 1. In some cases, a combination of miRNAs may be used to generate the index, which combination may be identified based on the methods described in Example 2 and / or Example 3.Other methods for identifying combinations of miRNAs that generate a signature are described elsewhere in this disclosure.

[0066] In some examples, the miRNAs for generating the index can be identified from a database dataset. In some cases, the database can include a National Center for Biotechnology Information (NCBI) database. In some cases, the database can include a Gene Expression Omnibus (GEO). In some cases, the database dataset can have an identification number. For example, the identification number for a GEO dataset can be an accession number. In some cases, the miRNA can be any one of the miRNAs listed in the dataset under NCBI GEO accession number GSE106817. The miRNAs listed in other datasets or databases can also be used to generate the index using the methods described herein.

[0067] In some cases, the composition can include a probe for any miRNA described herein. The probe can have a sequence of the miRNA. The probe can also have a sequence complementary to the miRNA. Such a probe can include a primer, a hybridization probe, a probe for microarrays, a probe for miRNA sequencing, or a combination thereof. Kits can include these compositions. Kits can also include compositions for isolating and identifying miRNAs described herein.

[0068] miRNA levels In some examples, the methods described herein can include generating an index. In some examples, the index can include a value predictive of a clinical or disease outcome for a subject with cancer. In some examples, the index can be generated using a level of miRNA. The level of miRNA can include an expression level of miRNA.

[0069] The expression level of the miRNA can include the amount of the miRNA measured in a sample of the subject. In some examples, the expression level of the miRNA can be used to generate an index. In some examples, the level of the miRNA can be measured as a concentration level. The concentration level can be expressed as weight / volume. In some examples, the level of the miRNA can be measured in milligrams / milliliter (mg / ml). In some examples, the level of the miRNA can be measured in micrograms / milliliter (μg / ml). In some examples, the level of the miRNA can be measured in nanograms / milliliter (ng / ml). In some examples, the level of the miRNA can be measured in picograms / milliliter (pg / ml). In some examples, the level of the miRNA can be measured in micrograms / microliter (μg / μl). In some examples, the level of the miRNA can be measured in nanograms / microliter (ng / μl). In some examples, the level of the miRNA can be measured in picograms / microliter (pg / μl). The concentration can be the level of the miRNA in the subject's cell-free sample. The concentration can be the level of miRNA in the subject's body fluid. The concentration can also be expressed in the form of moles. In some examples, the level of miRNA can also be measured as weight. For example, the weight level of miRNA can include mg, μg, ng, or pg. In some examples, the level of miRNA can also be measured as number of molecules. One example of the unit of number of molecules is moles.

[0070] In some examples, the indicator can be generated using miRNA levels, miRNA expression levels, normalized miRNA levels (as described herein), or a combination thereof. In some examples, the indicator can be generated using miRNA levels. In some examples, the indicator can be generated using miRNA expression levels. In some examples, the indicator can be generated using normalized miRNA levels.

[0071] In some examples, miRNA levels can be normalized by dividing the measured level of a particular miRNA by the level of a control (e.g., a housekeeping gene). For example, the control can be a gene that is expressed at an invariant level. In some examples, the control gene can be a gene that is expressed without variation in the cell. Invariant expression can include a level (e.g., expression level) of a gene or gene transcript that does not vary between cell types or situations (e.g., a disease state, a health state of a cell or subject, or a treatment situation). In some examples, the control gene can be a gene transcribed as a coding RNA (e.g., actin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), or ubiquitin). The mRNA level of the coding gene can be the level of the control. In some examples, the control can include a miRNA. The control miRNA can include a housekeeping miRNA. The housekeeping miRNA can be a miRNA that has an invariant expression level. The housekeeping miRNA may include miR-151a-5p, miR-27b-3p, or miR-103a-3p. The control level may include any level described herein. The normalized level may refer to a relative level.

[0072] In some examples, the level can include a normalized level. In some cases, the level of the miRNA can be normalized using a statistical normalization method. In some examples, the statistical normalization method can be a Z-score transformation. In some examples, the statistical normalization method can be a range transformation. In some examples, the statistical normalization method can be a ratio transformation. In some examples, the statistical normalization method can be an interquartile range. In some examples, the statistical normalization can be a linear scaling. In some examples, the statistical normalization method can be clipping. In some examples, the statistical normalization method can be log scaling. In some examples, the statistical normalization method can be linear normalization.

[0073] In some cases, the normalized level can include a parametric value of the miRNA level measured in multiple instances (e.g., multiple replicates). The replicates can include biological replicates. The replicates can include measurements in different biological samples. The normalized level or parametric value of the miRNA level can include a measured parameter of the miRNA level. The normalized miRNA level can include the mean, mode, median, maximum, minimum, range of values, first quartile, second quartile, third quartile, or fourth quartile of the miRNA level. In some cases, the normalized miRNA level can include the mean of the level of the miRNA. In some cases, the normalized miRNA level can include the mode of the level or expression level of the miRNA. In some cases, the normalized miRNA level can include the median of the level or expression level of the miRNA. In some cases, the normalized miRNA level can include the maximum of the level or expression level of the miRNA. In some cases, the normalized miRNA level can include the minimum of the level or expression level of the miRNA. In some cases, the normalized miRNA level can include a range of miRNA levels or expression levels. In some cases, the normalized miRNA level can include a first quartile of the miRNA level or expression level. In some cases, the normalized miRNA level can include a second quartile of the miRNA level or expression level. In some cases, the normalized miRNA level can include a third quartile of the miRNA level or expression level. In some cases, the normalized miRNA level can include a fourth quartile of the miRNA level or expression level. The normalized level can be a level derived from the addition, subtraction, multiplication, division, or combination thereof of two or more measurements or parameters.In other cases, the normalized level can include any parameter described herein of the expression distribution of an entity (e.g., a gene, a gene transcript, an RNA, or an miRNA). In some cases, the normalized miRNA level can be expressed as a rank among a list of miRNAs. The rank can be based on the level, expression level, or normalized of the miRNA.

[0074] In some cases, the level, expression level, or normalized level of miRNA can be converted to a threshold level of miRNA. In some cases, the threshold level of miRNA can also be used to predict disease outcome. In some examples, the threshold level of miRNA can include the level of miRNA in a cancer-positive subject. In some examples, the threshold level of miRNA can include the level of miRNA in a subject with cancer or a subject at risk of having or developing cancer. In some examples, the threshold level of miRNA can include the level of miRNA in a cancer-negative subject. In some examples, the threshold level of miRNA can include the level of miRNA in a subject without cancer or a subject at risk of having or developing cancer. The subject can be healthy. In some cases, the threshold level of miRNA can include the level of miRNA in a cell, tissue, or subject with cancer; or a sample derived from a cell, tissue, or subject with cancer. In some cases, the threshold level of miRNA can be the average level, mode level, median level, maximum level, minimum level, range of levels, first quartile level, second quartile level, third quartile level, or fourth quartile level of miRNA in a sample from a subject with cancer or a subject with a risk of having cancer or developing cancer.In some cases, the threshold level of miRNA can include the level of miRNA in a cell, tissue, or subject that does not have cancer; or in a sample from a cell, tissue, or subject that does not have cancer.In some cases, the threshold level of miRNA can be the average level, mode level, median level, maximum level, minimum level, range of levels, first quartile level, second quartile level, third quartile level, or fourth quartile level in a sample from a subject that does not have cancer or a subject with a risk of having cancer or developing cancer.

[0075] The threshold level of the miRNA may include the mean, mode, median, maximum, minimum, range of values, first quartile, second quartile, third quartile, or fourth quartile of the measured value. In some cases, the threshold level of the miRNA may include the mean of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA may include the mode of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA may include the median of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA may include the maximum of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA may include the minimum of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA may include the range of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA may include the first quartile of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA may include the second quartile of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA can include the third quartile of the level or normalized level of the miRNA. In some cases, the threshold level of the miRNA can include the fourth quartile of the level or normalized level of the miRNA. The threshold level can be a level derived from the addition, subtraction, multiplication, division, or combination thereof of two or more measurements or parameters. In other cases, the threshold level can include any parameter of the expression distribution of the RNA as described herein.

[0076] The nucleic acid for generating an indicator or predicting disease outcome can be a biomarker.In some cases, multiple threshold levels of miRNA can also be used to predict disease outcome.In some examples, multiple threshold levels can refer to a pattern of cancer or a pattern of biomarkers for cancer.Biomarkers can include measurements of miRNA levels.

[0077] Disease outcomes In some examples, the index can be used to predict disease outcomes. Disease outcomes can include the amount of time that a subject is alive or not dead after being determined to have a disease. Disease outcomes can include the amount of time that a subject is alive or not dead after being treated for a disease. Disease outcomes can include the amount of time that a subject is alive or not dead after being determined to have a risk of a disease. Disease outcomes can include the amount of time that a subject is alive or not dead after being determined to have cancer. Disease outcomes can include the amount of time that a subject is alive or not dead after being treated for cancer. Disease outcomes can include the amount of time that a subject is alive or not dead after being determined to have a risk of cancer. In some cases, the amount of time that a subject is alive or not dead after being determined to have a disease or being treated for a disease can include overall survival (OS). OS can include the amount of time that a subject is alive or not dead after being determined to have a disease. OS can also include the amount of time that a subject is alive or not dead after being treated for a disease. The disease may include any cancer described herein.

[0078] Disease outcomes can include the amount of time a subject lives without the disease worsening. In some cases, the amount of time during or after treatment of a disease that a subject lives without the disease worsening or does not die can include progression-free disease (PFS). When a subject experiences an increase in the amount of symptoms or more symptoms, the disease may be worsening. When a subject experiences an increase in the intensity of symptoms or disease, the disease may be worsening. The intensity of symptoms or disease can include measurements of biomarkers of the disease (e.g., levels of biomarkers), the amount of damage to the subject's cells or tissues caused by the disease, the subject's sense of the subject's health stage, or a combination thereof. When a subject receives an increase in the amount or dose of treatment to maintain the intensity of symptoms or disease, the disease may be worsening. When a subject experiences an increase in the amount of symptoms or more symptoms, the cancer may be worsening. When a subject experiences an increase in the intensity of symptoms or cancer, the cancer may be worsening. The intensity of symptoms or cancer can include measurements of biomarkers of the cancer (e.g., levels of biomarkers), the amount of damage to the subject's cells or tissues caused by the cancer, the subject's sense of the subject's health stage, or a combination thereof. A cancer may worsen when a subject receives an increased amount or dose of treatment to maintain symptoms or intensity of the cancer. In some cases, the worsening of a cancer may include a cancer at one stage progressing to a subsequent cancer stage. In some cases, a cancer worsens when a stage 0 cancer progresses to stage I, IA, IB, II, IIA, IIB, IIII, IIIA, IIIB, IIIC, or IV cancer. In some cases, a cancer worsens when a stage I cancer progresses to stage II, IIA, IIB, IIII, IIIA, IIIB, IIIC, or IV cancer. In some cases, a cancer worsens when a stage IA cancer progresses to stage IB, II, IIA, IIB, IIII, IIIA, IIIB, IIIC, or IV cancer. In some cases, a cancer worsens when a stage IB cancer progresses to stage II, IIA, IIB, IIII, IIIA, IIIB, IIIC, or IV cancer.In some cases, the cancer is worsening when a stage II cancer progresses to a stage IIII, IIIA, IIIB, IIIC, or IV cancer. In some cases, the cancer is worsening when a stage IIA cancer progresses to a stage IIB, IIII, IIIA, IIIB, IIIC, or IV cancer. In some cases, the cancer is worsening when a stage IIB cancer progresses to a stage IIII, IIIA, IIIB, IIIC, or IV cancer. In some cases, the cancer is worsening when a stage III cancer progresses to a stage IV cancer. In some cases, the cancer is worsening when a stage IIIA cancer progresses to a stage IIIB, IIIC, or IV cancer. In some cases, the cancer is worsening when a stage IIIB cancer progresses to a stage IIIC or IV cancer. In some cases, the cancer is worsening when a stage IIIC cancer progresses to a stage IV cancer.

[0079] In some cases, OS and PFS can be measured using a survival curve. For example, the survival curve can include a Kaplan-Meier curve.

[0080] Indicators and disease outcome prediction In some examples, the index can be generated by computing the miRNA levels of the subject. The computing can include applying the miRNA levels to a statistical model. The computing can include applying the multiple miRNA levels to the statistical model. The statistical model can include a mathematical model that implements a set of statistical assumptions regarding sample data generation. In some examples, the statistical model used to generate the index can be a log-rank test. In some examples, the statistical model used to generate the index can be a linear regression model. A linear regression model can include a model that assumes a linear relationship between input values ​​and a single output variable. In some examples, the statistical model used to generate the index can be a univariate Cox regression model or a univariate Cox model. A univariate Cox regression model can include a regression model that is used to determine the association between one input value and an outcome. In some examples, the statistical model used to generate the index can be a multivariate Cox regression model or a multivariate Cox model. A multivariate Cox regression model can include a regression model that is used to determine the association between one or more input values ​​and one or more outcomes. In some examples, the input value can be a miRNA level as described herein. In some examples, the outcome can be overall survival (OS). In some examples, the outcome can be progression free survival (PFS). In some examples, an index for overall survival (OS) can be generated. In some examples, an index for progression free survival (PFS) can be generated.

[0081] In some instances, the index is capable of predicting a disease outcome. In some instances, the index is capable of detecting the presence or absence of a disease. In other instances, the index is capable of predicting a disease outcome but is unable to detect the presence or absence of a disease.

[0082] In some examples, the index can predict a disease outcome of cancer. In some examples, the index can predict a disease outcome of ovarian cancer. In some examples, the index can predict a disease outcome of HGSOC. In some examples, the index can predict a disease outcome of clear cell ovarian cancer.

[0083] In some examples, the index may include a formula. In some instances, the formula is: Index=Σ[X n *(Y n )] where X represents a derivative value by which the level of the miRNA is multiplied, Y represents the level of the miRNA, and n represents one miRNA.

[0084] In some cases, Y n can include the level of any miRNA described herein. The level of a particular miRNA (i.e., Y n ), then X n is Y n In some cases, it may involve multiplying by X n may contain numeric values. X n, 0.0000001, 0.00001, 0.0001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0. 66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17 ,1.18,1.19,1.2,1.21,1.22,1.23,1.24,1.25,1.26,1.27,1.28,1.29,1.3,1.31,1.32,1.33,1.34,1.35,1.36,1.37,1.38,1.39,1.4,1.41,1.42,1. 43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68 ,1.69,1.7,1.71,1.72,1.73,1.74,1.75,1.76,1.77,1.78,1.79,1.8,1.81,1.82,1.83,1.84,1.85,1.86,1.87,1.88,1.89,1.9,1.91,1.92,1.93,1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 50, 100, 1000, or more. X. nThe values ​​for are up to about 0.0000001, 0.000001, 0.00001, 0.0001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0 .41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.6 6, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0 .92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17 ,1.18,1.19,1.2,1.21,1.22,1.23,1.24,1.25,1.26,1.27,1.28,1.29,1.3,1.31,1.32,1.33,1.34,1.35,1.36,1.37,1.38,1.39,1.4,1.41,1.42,1. 43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68 ,1.69,1.7,1.71,1.72,1.73,1.74,1.75,1.76,1.77,1.78,1.79,1.8,1.81,1.82,1.83,1.84,1.85,1.86,1.87,1.88,1.89,1.9,1.91,1.92,1.93,1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 50, 100, 1000. X. nThe values ​​for are approximately 0.0000001, 0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0. 16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0. 67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18 ,1.19,1.2,1.21,1.22,1.23,1.24,1.25,1.26,1.27,1.28,1.29,1.3,1.31,1.32,1.33,1.34,1.35,1.36,1.37,1.38,1.39,1.4,1.41,1.42,1.43,1. 44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 1.69 ,1.7, 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8, 1.81, 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.88, 1.89, 1.9, 1.91, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 50, 100, 1000. X. nThe values ​​for are 0.0000001, 0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0. 16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0. 67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18 ,1.19,1.2,1.21,1.22,1.23,1.24,1.25,1.26,1.27,1.28,1.29,1.3,1.31,1.32,1.33,1.34,1.35,1.36,1.37,1.38,1.39,1.4,1.41,1.42,1.43,1. 44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 1.69 ,1.7, 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8, 1.81, 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.88, 1.89, 1.9, 1.91, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 50, 100, 1000.

[0085] The formula for the index is: 1. Index = 0.218×(miR‐187‐5p)+0.280×(miR‐6870‐5p), 2. Index = 0.148×(miR‐187‐5p)+0.273×(miR‐6870‐5p)+0.186×(miR‐1908‐5p), 3. Index = 0.034×(miR-187-5p)+0.236×(miR-6870-5p)+0.504×(miR-6727-5p)+0.048×(miR-1908-5p)-0.251×(miR-6870-5p), 4. Index = 0.031×(miR‐187‐5p)+0.231×(miR‐6870‐5p)+0.351×(miR‐6727‐5p), 5. Index = 0.463 × (miR‐150‐3p) + 1.323 × (miR‐3195) + 0.636 × (miR‐7704), or 6. The index may include any one of the following: 0.399 x (miR-150-3p) + 1.426 x (miR-3195) + 0.480 x (miR-7704).

[0086] The formula of the index may include formula 1. The formula of the index may include formula 2. The formula of the index may include formula 3. The formula of the index may include formula 4. The formula of the index may include formula 5. The formula of the index may include formula 6. The formula of the index may include any two of formula 1, formula 2, formula 3, formula 4, formula 5, and formula 6. The formula of the index may include any three of formula 1, formula 2, formula 3, formula 4, formula 5, and formula 6. The formula of the index may include any three of formula 1, formula 2, formula 3, formula 4, formula 5, and formula 6. The formula of the index may include any four of formula 1, formula 2, formula 3, formula 4, formula 5, and formula 6. The formula of the index may include any five of formula 1, formula 2, formula 3, formula 4, formula 5, and formula 6.

[0087] In other cases, the exact makeup of the index formula may vary depending on the subject, the miRNA, and / or the statistical model used to generate the index.

[0088] Using the index to predict disease outcomes can generate at least two different subject populations, one with an index value higher than a threshold index value and another with an index value lower than a threshold index value. Using the index to predict disease outcomes can generate at least two different subject populations, one with an index value equal to or higher than a threshold index value and another with an index value lower than a threshold index value. In some cases, using the index to predict disease outcomes can generate at least two different subject populations, one with an index value higher than a threshold index value and another with an index value lower than a threshold index value. The two subject populations can have different disease outcomes. The different outcomes can include differences in OS or PFS. The different outcomes can include differences in outcomes described herein. Generating two different subject populations can be referred to as stratifying or stratifying.

[0089] The threshold index value may include the mean, mode, median, maximum, minimum, range, first quartile, second quartile, third quartile, or fourth quartile of the index value of the subject population. In some cases, the threshold index value may include the mean of the index value of the subject population. In some cases, the threshold index value may include the mode of the subject's level or the subject population. In some cases, the threshold index value may include the median of the subject's level or the subject population. In some cases, the threshold index value may include the maximum of the subject's level or the subject population. In some cases, the threshold index value may include the minimum of the subject's level or the subject population. In some cases, the threshold index value may include the range of the subject's level or the subject population. In some cases, the threshold index value may include the first quartile of the subject's level or the subject population. In some cases, the threshold index value may include the second quartile of the subject's level or the subject population. In some cases, the threshold index value may include the third quartile of the subject's level or the subject population. In some cases, the threshold index value may include the fourth quartile of the subject's level or the subject population.

[0090] In some cases, the miRNA level can also be used to predict disease outcomes without being used to generate an index. For example, a threshold level of miRNA can be used to predict disease outcomes. In some cases, using the level to predict disease outcomes can generate at least two different subject populations, one with miRNA levels above a threshold level and another with miRNA levels below the level. In some cases, using the threshold level of miRNA to predict disease outcomes can generate at least two different subject populations, one with miRNA levels above the level and another with miRNA levels below the threshold level. The two different subject populations can have different disease outcomes. In some cases, using the level to predict disease outcomes can generate at least two different subject populations, one with miRNA levels above a threshold level and another with miRNA levels below the level. The two subject populations can have different disease outcomes. The different outcomes can include differences in OS or PFS. The different outcomes can include differences in outcomes described herein. The level of miRNA for predicting disease outcome can include expression level or normalized level of miRNA. In some cases, the level of at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 150, 200, 210, 500, 1000, 2000, 5000, 10000, or more miRNAs can be used to predict disease outcome without being used to generate an index.In some cases, the level of at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 150, 200, 210, 500, 1000, 2000, 5000, or 10000 miRNAs can be used to predict disease outcome without being used to generate an index.

[0091] In some examples, the subject may be stratified based on the level of miRNA. In some examples, the subject may be stratified based on the serum level of miRNA. In some examples, the subject stratified based on miRNA may be analyzed using Kaplan-Meier curve. In some examples, the subject may be stratified based on the index value generated. In some examples, the subject stratified based on index may be analyzed using Kaplan-Meier curve. In some examples, the disease outcome analyzed using Kaplan-Meier curve may be overall survival (OS). In some examples, the disease outcome analyzed using Kaplan-Meier curve may be progression-free survival (PFS).

[0092] In some cases, a population with an index value higher than the index may have a higher OS or PFS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a lower OS or PFS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a higher OS or PFS value than a population with an index value lower than the index. In some cases, a population with an index value equal to or higher than the index may have a lower OS or PFS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a lower OS or PFS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a lower OS or PFS value than a population with an index value equal to or lower than the index. In some cases, a population with an index value higher than the index may have a higher OS or PFS value than a population with an index value equal to or lower than the index.

[0093] In some cases, a population having a miRNA level higher than a threshold level of miRNA may have a higher OS or PFS value than a population having a miRNA level lower than a threshold level of miRNA. In some cases, a population having a miRNA level higher than a threshold level of miRNA may have a lower OS or PFS value than a population having a miRNA level lower than a threshold level of miRNA. In some cases, a population having a miRNA level higher than a threshold level of miRNA may have a higher OS or PFS value than a population having a miRNA level lower than a threshold level of miRNA. In some cases, a population having a miRNA level equal to or higher than a threshold level of miRNA may have a lower OS or PFS value than a population having a miRNA level lower than a threshold level of miRNA. In some cases, a population having a miRNA level equal to or higher than a threshold level of miRNA may have a higher OS or PFS value than a population having a miRNA level lower than a threshold level of miRNA. In some cases, a population having a miRNA level higher than a threshold level of miRNA may have a lower OS or PFS value than a population having a miRNA level lower than a threshold level of miRNA. In some cases, a population having a miRNA level above a threshold level of the miRNA may have a higher OS or PFS value than a population having a miRNA level below the threshold level of the miRNA.

[0094] In some examples, good or non-poor disease outcomes may include higher OS or PFS values. In some examples, good or non-poor disease outcomes may include higher OS. In some examples, good or non-poor disease outcomes may include higher PFS. In some examples, good or non-poor disease outcomes may include higher OS and higher PFS.

[0095] In some examples, poor or bad disease outcomes may include lower OS or PFS values. In some examples, poor or bad disease outcomes may include lower OS. In some examples, poor or bad disease outcomes may include lower PFS. In some examples, poor or bad disease outcomes may include lower OS and lower PFS.

[0096] In some cases, a population with an index value higher than the index may have a lower OS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a higher OS value than a population with an index value lower than the index. In some cases, a population with an index value equal to or higher than the index may have a lower OS value than a population with an index value lower than the index. In some cases, a population with an index value equal to or higher than the index may have a higher OS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a lower OS value than a population with an index value equal to or lower than the index. In some cases, a population with an index value higher than the index may have a higher OS value than a population with an index value equal to or lower than the index.

[0097] In some cases, a population with an index value higher than the index may have a lower PFS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a higher PFS value than a population with an index value lower than the index. In some cases, a population with an index value equal to or higher than the index may have a lower PFS value than a population with an index value lower than the index. In some cases, a population with an index value equal to or higher than the index may have a higher PFS value than a population with an index value lower than the index. In some cases, a population with an index value higher than the index may have a lower PFS value than a population with an index value equal to or lower than the index. In some cases, a population with an index value higher than the index may have a higher PFS value than a population with an index value equal to or lower than the index.

[0098] In some examples, the indicators can also be generated using an algorithm. In some examples, the indicators can be generated by a computer algorithm. In some examples, the algorithms can include supervised learning algorithms, semi-supervised learning algorithms, or unsupervised learning algorithms. In some examples, the algorithms can include artificial neural networks, Bayesian classifiers, blind signal separation, decision trees, eigenmatrix, Gaussian radial basis functions, joint approximate diagonalization, kernel analysis and polynomial kernel analysis, linear and nonlinear independent component analysis (ICA), natural gradient maximum likelihood estimation, non-Gaussianity analysis, principal component analysis (PCA), sequential floating forward selection, support vector machines, or combinations thereof.

[0099] subject In some examples, the index can be calculated using miRNA expression levels of at least about 1 to at least about 10,000 subjects. In some examples, the number of subjects used to calculate the index is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 , 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, 520, 540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 80 0, 820, 840, 860, 880, 900, 920, 940, 960, 980, 1,000, 1,050, 1,100, 1,150, 1,200, 1,250, 1,300, 1,350, 1,400, 1,450, 1,500, 1,550, 1,600, 1,650, 1,700, 1,750, 1,800, 1,850, 1,900, 1,950, 2,000, 2,050, 2,100, 2,150, 2,200, 2,250, 2,300, 2,350, 2,400, 2,450, 2,500, 2,550, 2,6 00, 2,650, 2,700, 2,750, 2,800, 2,850, 2,900, 2,950, 3,000, 3,050, 3,100, 3,150, 3,200, 3,250, 3,300, 3,350, 3,400, 3,450, 3,500, 3,550, 3,600, 3,650, 3,700, 3,750, 3,800, 3,850, 3,900, 3,950, 4,000, 4,050, 4,100, 4,150, 4,200, 4,250, 4,300, 4,350, 4,400, 4,450, 4,500, 4,550, 4,600, 4,650, 4,700, 4,750, 4,800, 4,850, 4,900, 4,950, 5,000, 5,050, 5,100, 5,150, 5,200, 5,250, 5,300, 5,350, 5,400, 5,450, 5,500, 5,550, 5,600, 5,650, 5,700, 5,750, 5,800, 5,850, 5,900, 5,95 0, 6,000, 6,050, 6,100, 6,150, 6,200, 6,250, 6,300, 6,350, 6,400, 6,450, 6,500, 6,550, 6,600, 6,650, 6,700, 6,750, 6,800, 6,850, 6,900, 6,950, 7,000, 7,050, 7,100, 7,150, 7,200, 7,250, 7,300, 7,350, 7,400, 7,450, 7,500, 7,550, 7,600, 7,650, 7,700, 7,750, 7,800, 7,850, 7,900, 7,950, 8,000, 8,050, 8,100, 8,150, 8,200, 8,250, 8,300, 8,350, 8,400, 8,450, 8,500, 8,550, 8,600, 8,650, 8,700, 8,750, 8, It can be 800, 8,850, 8,900, 8,950, 9,000, 9,050, 9,100, 9,150, 9,200, 9,250, 9,300, 9,350, 9,400, 9,450, 9,500, 9,550, 9,600, 9,650, 9,700, 9,750, 9,800, 9,850, 9,900, 9,950, 10,000, or more.

[0100] In some examples, the index can be calculated using miRNA expression levels of up to about 1 to up to about 10,000 subjects. In some examples, the number of subjects used to calculate the index is up to about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114 , 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, 520, 540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 82 0, 840, 860, 880, 900, 920, 940, 960, 980, 1,000, 1,050, 1,100, 1,150, 1,200, 1,250, 1,300, 1,350, 1,400, 1,450, 1,500, 1,550, 1,600, 1,650, 1,700, 1,750, 1,800, 1,850, 1,900, 1,950, 2,000, 2,050, 2,100, 2,150, 2,200, 2,250, 2,300, 2,350, 2,400, 2,450, 2,500, 2,550, 2,600, 2, 650, 2,700, 2,750, 2,800, 2,850, 2,900, 2,950, 3,000, 3,050, 3,100, 3,150, 3,200, 3,250, 3,300, 3,350, 3,400, 3,450, 3,500, 3,550, 3,600, 3,650, 3,700, 3,750, 3,800, 3,850, 3,900, 3,950, 4,000, 4,050, 4,100, 4,150, 4,200, 4,250, 4,300, 4,350, 4,400, 4,450, 4,500, 4,550, 4,600, 4,650, 4,700, 4,750, 4,800, 4,850, 4,900, 4,950, 5,000, 5,050, 5,100, 5,150, 5,200, 5,250, 5,300, 5,350, 5,400, 5,450, 5,500, 5,550, 5,600, 5,650, 5,700, 5,750, 5,800, 5,850, 5,900, 5,950, 6,000, 6,050, 6,100, 6,150, 6,200, 6,250, 6,300, 6,350, 6,400, 6,450, 6,500, 6,550, 6,600, 6,650, 6,700, 6,750, 6,800, 6,850, 6,900, 6,950, 7,000, 7,050, 7,100, 7,150, 7,200, 7,250, 7,300, 7,35 0, 7,400, 7,450, 7,500, 7,550, 7,600, 7,650, 7,700, 7,750, 7,800, 7,850, 7,900, 7,950, 8,000, 8,050, 8,100, 8,150, 8,200, 8,250, 8,300, 8,350, 8,400, 8,450, 8,500, 8,550, 8,600, 8,650, 8,700, 8, 750, 8,800, 8,850, 8,900, 8,950, 9,000, 9,050, 9,100, 9,150, 9,200, 9,250, 9,300, 9,350, 9,400, 9,450, 9,500, 9,550, 9,600, 9,650, 9,700, 9,750, 9,800, 9,850, 9,900, 9,950, or 10,000.

[0101] In some examples, the index can be calculated using miRNA expression levels of about 1 to about 10,000 subjects. In some examples, the number of subjects used to calculate the index is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 109, 109, 102, 104, 105, 1 , 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, 520, 540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 820, 840, 86 0, 880, 900, 920, 940, 960, 980, 1,000, 1,050, 1,100, 1,150, 1,200, 1,250, 1,300, 1,350, 1,400, 1,450, 1,500, 1,550, 1,600, 1,650, 1,700, 1,750, 1,800, 1,850, 1,900, 1,950, 2,000, 2,050, 2,100, 2,150, 2,200, 2,250, 2,300, 2,350, 2,400, 2,450, 2,500, 2,550, 2,600, 2,650, 2,700, 2,750, 2,800, 2,850, 2,900, 2,950, 3,000, 3,050, 3,100, 3,150, 3,200, 3,250, 3,300, 3,350, 3,400, 3,450, 3,500, 3,550, 3,600, 3,650, 3,700, 3,750, 3,800, 3,850, 3,900, 3,950, 4,000, 4,050, 4,100, 4,150, 4,200, 4,250, 4,300, 4,350, 4,400, 4,450, 4,500, 4,550, 4,600, 4,650, 4,700, 4,750, 4,800, 4,850, 4,900, 4,950, 5,000, 5,050, 5,100, 5,150, 5,200, 5,250, 5,300, 5,350, 5,400, 5,450, 5,500, 5,550, 5,600, 5,650, 5,700, 5,750, 5,800, 5,850, 5,900, 5,950, 6,000, 6,0 50, 6,100, 6,150, 6,200, 6,250, 6,300, 6,350, 6,400, 6,450, 6,500, 6,550, 6,600, 6,650, 6,700, 6,750, 6,800, 6,850, 6,900, 6,950, 7,000, 7,050, 7,100, 7,150, 7,200, 7,250, 7,300, 7,350, 7,40 0, 7,450, 7,500, 7,550, 7,600, 7,650, 7,700, 7,750, 7,800, 7,850, 7,900, 7,950, 8,000, 8,050, 8,100, 8,150, 8,200, 8,250, 8,300, 8,350, 8,400, 8,450, 8,500, 8,550, 8,600, 8,650, 8,700, 8,750 , 8,800, 8,850, 8,900, 8,950, 9,000, 9,050, 9,100, 9,150, 9,200, 9,250, 9,300, 9,350, 9,400, 9,450, 9,500, 9,550, 9,600, 9,650, 9,700, 9,750, 9,800, 9,850, 9,900, 9,950, or 10,000.

[0102] In some examples, the number of subjects used to calculate the index can be 442. In some examples, the number of subjects used to calculate the index can be at least about 442. In some examples, the number of subjects used to calculate the index can be up to about 442. In some examples, the number of subjects used to calculate the index can be 969. In some examples, the number of subjects used to calculate the index can be at least about 969. In some examples, the number of subjects used to calculate the index can be up to about 969. In some examples, the number of subjects used to calculate the index can be 180. In some examples, the number of subjects used to calculate the index can be at least about 180. In some examples, the number of subjects used to calculate the index can be up to about 180. In some examples, the number of subjects used to calculate the index can be 175. In some examples, the number of subjects used to calculate the index can be at least about 175. In some examples, the number of subjects used to calculate the index can be up to about 175. In some examples, the number of subjects used to calculate the index can be 68. In some examples, the number of subjects used to calculate the index can be at least about 68. In some examples, the number of subjects used to calculate the index can be up to about 68. In some examples, the number of subjects used to calculate the index can be 66. In some examples, the number of subjects used to generate the index can be at least about 66. In some examples, the number of subjects used to generate the index can be up to about 66.

[0103] In some examples, the subject may have a particular cancer type or subtype. In some examples, the subject used to generate the index must not have received any treatment. In some examples, the subject may have a particular cancer type by selecting subjects with the same type of cancer. The subject may have a first type or subtype of cancer, but not a second type or subtype of cancer. In some examples, the first type or subtype of cancer or the second type or subtype of cancer may include any type or subtype disclosed in this disclosure. In some examples, the particular cancer type or subtype may be any of the cancer types or subtypes disclosed in this disclosure. The subject may have ovarian cancer. The subject may have endometrioid ovarian cancer or mucinous ovarian cancer. The subject may have endometrioid ovarian cancer or serous ovarian cancer. The subject may have mucinous ovarian cancer or serous ovarian cancer. The subject may have HGSOC. The subject may have clear cell carcinoma. The subject may have type I ovarian cancer. The subject may have type II ovarian cancer.

[0104] In some cases, the subject may have a stage of cancer. In some cases, the subject may have a stage of cancer. In some cases, the subject may have stage 0 cancer. In some cases, the subject may have stage I cancer. In some cases, the subject may have stage IA cancer. In some cases, the subject may have stage IB cancer. In some cases, the subject may have stage IIA cancer. In some cases, the subject may have stage IIB cancer. In some cases, the subject may have stage IIIA cancer. In some cases, the subject may have stage IIIB cancer. In some cases, the subject may have stage IIIC cancer. In some cases, the subject may have stage IV cancer. In some cases, the subject may have a stage of ovarian cancer. In some cases, the subject may have a stage of ovarian cancer. In some cases, the subject may have stage 0 ovarian cancer. In some cases, the subject may have stage I ovarian cancer. In some cases, the subject may have stage IA ovarian cancer. In some cases, the subject may have stage IB ovarian cancer. In some cases, the subject may have stage IIA ovarian cancer. In some cases, the subject may have stage IIB ovarian cancer. In some cases, the subject may have stage IIIA ovarian cancer. In some cases, the subject may have stage IIIB ovarian cancer. In some cases, the subject may have stage IIIC ovarian cancer. In some cases, the subject may have stage IV ovarian cancer.

[0105] In some examples, subjects may be excluded from index generation. In some examples, subjects with different types of subtypes of cancer may be excluded. In some examples, subjects with unclassifiable ovarian cancer samples may be excluded. In some examples, subjects with borderline malignant tumors may be excluded. In some examples, subjects with benign tumors may be excluded. In some examples, subjects may be excluded if they underwent treatment before sample collection. Non-limiting examples of treatments before sample collection that may exclude subjects include surgery, chemotherapy, or radiation therapy. In some examples, subjects may be excluded due to poor quality miRNA expression data from the sample. In some examples, subjects may be excluded due to poor quality microarray data. In some examples, certain miRNA molecules may be excluded from index generation. In some examples, certain miRNAs may be excluded due to low signal in miRNA expression data.

[0106] Isolation and identification of miRNAs In some examples, the method can include extracting a plurality of miRNAs from the sample. In some examples, the method can include extracting miRNAs from the sample. In some examples, extracting RNA from the sample can include lysing cells in which the RNA is located. In some examples, the RNA can be extracted from the sample using an organic extraction. In some examples, the organic extraction can use a phenol-guanidine isothiocyanate (GITC) based solution. In some examples, the RNA can be extracted from the sample using a silica membrane based spin column. In some examples, the RNA can be extracted from the sample using paramagnetic particles.

[0107] In some examples, miRNA can be extracted from a sample by contacting the sample with the nanowire of a device in which the nanowire is incorporated. In some examples, extracting miRNA can be performed under conditions in which the nanowire has a positive surface charge. For example, by contacting the sample with the nanowire under pH conditions in which the nanowire has a positive surface charge, free and EV-encapsulated microRNA can be captured on the nanowire. In some examples, the sample fluid can be pH adjusted such that the nanowire has a positive surface charge. Alternatively, in some examples, the nanowire can be made from a material that has a positive surface charge in bodily fluids to match the pH of the sample.

[0108] In some examples, after the RNA is extracted, the RNA can be identified. In some examples, the identified RNA can be miRNA. In some examples, the miRNA can be identified using a miRNA microarray. In some examples, the microarray can include probes that can bind to specific miRNAs. In some examples, the microarray can include about 100 to about 100,000 probes. In some examples, the microarray can include at least about 100, at least about 200, at least about 300, at least about 400, at least about 500, at least about 600, at least about 700, at least about 800, at least about 900, at least about 1,000, at least about 1,500, at least about 2,000, at least about 2,500, at least about 3,000, at least about 3,500, at least about 4,000, at least about 4,500, at least about 5,000, at least about 5,500, at least about The microarray may include 6,000, at least about 6,500, at least about 7,000, at least about 7,500, at least about 8,000, at least about 8,500, at least about 9,000, at least about 10,000, at least about 20,000, at least about 30,000, at least about 40,000, at least about 50,000, at least about 60,000, at least about 70,000, at least about 80,000, at least about 90,000, at least about 100,000, or more probes. In some examples, the microarray may include at least about 2,038 probes. In some examples, the microarray may include up to about 2,038. In some examples, the microarray may include 2,038. In some examples, the microarray may measure the expression level of a particular miRNA in a sample. In some examples, the microarray may measure the expression level of one or more particular RNAs simultaneously. In some examples, the microarray can simultaneously measure the expression levels of about 100 to about 100,000 specific miRNAs.In some examples, the microarray comprises at least about 100, at least about 200, at least about 300, at least about 400, at least about 500, at least about 600, at least about 700, at least about 800, at least about 900, at least about 1,000, at least about 1,500, at least about 2,000, at least about 2,500, at least about 3,000, at least about 3,500, at least about 4,000, at least about 4,500, at least about 5,000, at least about 5,500, at least about 6,000, The expression levels of at least about 6,500, at least about 7,000, at least about 7,500, at least about 8,000, at least about 8,500, at least about 9,000, at least about 10,000, at least about 20,000, at least about 30,000, at least about 40,000, at least about 50,000, at least about 60,000, at least about 70,000, at least about 80,000, at least about 90,000, at least about 100,000, or more miRNAs can be measured simultaneously. In some examples, the microarray can measure the expression levels of at least about 210 specific miRNAs simultaneously. In some examples, the microarray can measure the expression levels of up to about 210 specific miRNAs simultaneously. In some examples, the microarray can measure the expression levels of 210 specific miRNAs simultaneously.

[0109] In some examples, assaying the nucleic acid can include sequencing the nucleic acid or any derivative thereof. In other cases, assaying the nucleic acid can include identifying the nucleic acid or a derivative thereof. In some examples, assaying the nucleic acid can include identifying a sequence of the nucleic acid or a derivative thereof. In some examples, identifying a sequence of the nucleic acid or a derivative thereof can include identifying a mutant or variant of the nucleic acid or a derivative thereof relative to a wild type sequence of the nucleic acid. In some examples, assaying the nucleic acid can include identifying a modification (e.g., an epigenetic modification) of the nucleic acid sequence. In some examples, assaying the nucleic acid can include identifying a sequence of the nucleic acid or a derivative thereof. In some examples, assaying the nucleic acid can include identifying an expression level of the nucleic acid or a derivative thereof. In some examples, assaying or identifying the nucleic acid can include sequencing the nucleic acid or a derivative thereof.

[0110] In some examples, the sequencing can include whole genome sequencing. In some examples, the sequencing can include whole genome methylation sequencing. In some examples, the sequencing can include whole genome sequencing or whole genome methylation sequencing. In some examples, the sequencing can include whole genome sequencing and whole genome methylation sequencing. In some examples, the sequencing can include next generation sequencing. In some examples, the sequencing can include second generation sequencing. In some examples, the sequencing can include third generation sequencing. In some examples, the sequencing can include fourth generation sequencing.

[0111] In some examples, the sequencing can include chain termination sequencing, high throughput sequencing, mass spectrometry sequencing, massively parallel signature sequencing, Maxam Gilbert sequencing, nanopore sequencing, primer walking, pyrosequencing, Sanger sequencing, semiconductor sequencing, sequencing-by-hybridization, sequencing-by-ligation, sequencing-by-synthesis, single molecule sequencing, shotgun sequencing, bisulfite sequencing, or any combination thereof. In some examples, the sequencing can include chain termination sequencing. In some examples, the sequencing can include high throughput sequencing. In some examples, the sequencing can include mass spectrometry sequencing. In some examples, the sequencing can include massively parallel signature sequencing. In some examples, the sequencing can include Maxam Gilbert sequencing. In some examples, the sequencing can include nanopore sequencing. In some examples, the sequencing can include primer walking. In some examples, the sequencing can include pyrosequencing. In some examples, the sequencing can include Sanger sequencing. In some examples, the sequencing can include semiconductor sequencing. In some examples, the sequencing can include sequencing-by-hybridization. In some examples, the sequencing can include sequencing-by-ligation. In some examples, the sequencing can include sequencing-by-synthesis. In some examples, the sequencing can include single molecule sequencing. In some examples, the sequencing can include shotgun sequencing.

[0112] In some examples, the identification of the nucleic acid can include sequencing, PCR, microarray analysis, or fluorescent hybridization. In some cases, the identification of the nucleic acid can include PCR. In some cases, the identification of the nucleic acid can include microarray analysis. In some examples, the identification of the nucleic acid can include fluorescent hybridization. In some examples, the PCR can include allele-specific PCR, assembly PCR, asymmetric PCR, co-amplification with low denaturation temperature PCR, dial-out PCR, digital PCR, emulsion PCR, gene-specific PCR, helicase-dependent PCR, hot start PCR, inverse PCR, Klenow-based PCR, ligation-mediated PCR, methylation-specific PCR, mini-primer PCR, multiplex PCR, nested PCR, nested PCR, overlap extension PCR, quantitative PCR, real-time PCR, thermal asymmetric interlaced PCR and touchdown PCR, touchdown PCR, or two-sided PCR.

[0113] sample In some examples, the sample may include an animal sample. In some examples, the sample may include a mammalian sample. In some examples, the sample may include a primate sample. In some examples, the sample may include a human sample.

[0114] In some examples, the sample may be from a subject with cancer. In some examples, the sample may be from a subject with cancer or at risk of developing cancer. In some examples, the sample may be from a subject without cancer. In some examples, the sample may be from a healthy subject. In some examples, the sample may be from a subject without cancer or at risk of developing cancer. In some examples, the sample may be from a subject who is healthy or does not have any medical condition.

[0115] In some examples, the sample may include an acellular sample. An acellular sample may include a body sample from which cells have been removed. For example, the body sample may be centrifuged to remove cells. In some cases, cells may be filtered out of the body sample to form the acellular sample. In other cases, cells of the body sample may be digested or the plasma membrane of the cells may be removed or disrupted to expose cellular components to form the acellular sample. An acellular sample may not include cells. An acellular sample may be substantially free of cells. For example, an acellular sample may contain up to about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50% of cells relative to the bodily sample from which the acellular sample is derived. In some cases, the acellular sample may be obtained non-invasively. In other cases, the acellular sample must not be obtained invasively. In some cases, the non-invasive collection procedure must not damage the subject from which the sample is obtained. In other cases, the invasive collection procedure may damage the subject from which the sample is obtained. In some cases, the invasive collection may obtain a sample that includes at least cells.

[0116] The sample may include a bodily fluid. In some examples, the sample may include a blood sample. In some examples, the sample may include a serum sample. In some examples, the sample may include a plasma sample. In some examples, the bodily fluid may include an intracellular fluid or an extracellular fluid. Non-limiting examples of extracellular fluids may include intravascular fluid, interstitial fluid, lymphatic fluid, transcellular fluid, or a combination thereof. In some instances, bodily fluids may also include ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, earwax, breast milk, bronchoalveolar lavage fluid, semen (including prostatic fluid), Cowper's or bulbourethral fluid, female ejaculate, sweat, feces, hair, tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions, mucosal secretions, stool, pancreatic juice, sinus lavage, bronchopulmonary aspirate, or other lavage fluids. Bodily fluids may also include blastocyst cavity, umbilical cord blood, or maternal circulation, which may be of fetal or maternal origin. In some instances, the fluid sample is derived from a bodily fluid selected from whole blood, sputum, serum, plasma, urine, cerebrospinal fluid, nipple aspirate, saliva, and fine needle aspirate.

[0117] In some examples, the acellular sample may not include a biopsy sample. Non-limiting examples of biopsy samples may include bone biopsy, bone marrow biopsy, breast biopsy, gastrointestinal biopsy, lung biopsy, liver biopsy, prostate biopsy, nervous system biopsy, urogenital biopsy, lymph node biopsy, muscle biopsy, skin biopsy, blood biopsy, body fluid biopsy, cardiac biopsy, endometrial biopsy, open biopsy, sentinel lymph node biopsy, or any combination thereof. In some examples, the biopsy may include fine needle aspiration biopsy, needle biopsy, suction biopsy, excision biopsy, shave biopsy, punch biopsy, endoscopic biopsy, laparoscopic biopsy, bone marrow aspiration biopsy, liquid biopsy, and the present invention and any derivatives thereof, or the present invention and any combination thereof. In some examples, the biopsy may include an incisional biopsy or an excision biopsy.

[0118] Extracellular vesicles / exosomes In some examples, the miRNA can be encapsulated in an extracellular vesicle. In some examples, the miRNA encapsulated in the extracellular vesicle can be delivered from one cell to a second cell. In some examples, the extracellular vesicle can include a microvesicle. In some examples, the microvesicle can be formed by direct outward budding / shedding of the plasma membrane of the cell. In some examples, the extracellular vesicle can include an exosome. In some examples, the exosome can be encapsulated within a single outer membrane derived from an endosome. In some examples, the exosome can be secreted by any type of cell. In some examples, the extracellular vesicle can be an apoptotic body. In some examples, the apoptotic body can be released by a dying cell.

[0119] In some cases, the miRNAs used to predict disease outcomes or generate indicators may be encapsulated by exosomes derived from cancer cells. In other cases, the miRNAs used to predict disease outcomes or generate indicators may be encapsulated by exosomes derived from non-cancerous cells. EXAMPLES

[0120] The following examples are provided to illustrate various embodiments of the present invention and are not intended to limit the present invention in any way. The examples, as well as the methods described herein, represent currently preferred embodiments, are illustrative, and are not meant to be limitations on the scope of the present invention. Those skilled in the art will recognize modifications and other uses herein that are encompassed within the spirit of the present invention as defined by the claims. EXAMPLES

[0121] Preoperative serum microRNAs as potential predictive markers of disease outcome in ovarian clear cell carcinoma Herein, a method is provided for identifying miRNAs as markers predicting disease outcomes in a subject. An example of a disease can include any ovarian cancer.

[0122] We investigated 4,046 body fluid samples from healthy controls and subjects with ovarian tumors and generated their comprehensive microRNA profiles using the 3D-Gene® miRNA Labeling kit and 3D-Gene® Human miRNA Oligo Chip (Toray Industries, Inc.). This is described in Yokoi A et al., Integrated extracellular miRNA profiling for ovarian cancer screening. Nat Commun. 2018;9:4319, which is incorporated herein by reference in its entirety. The data is available through the NCBI database under the accession number GSE106817. Furthermore, 210 microRNAs were selected from the 2,038 microRNAs in the dataset according to the criteria used in the previous study. Briefly, the microRNAs were detected in extracellular vesicles derived from ovarian cancer cells. The 210 miRNAs identified are listed in Table 1 below.

[0123] [Table 1] JPEG2025506080000003.jpg255157

[0124] Similar methods can also be used to identify other miRNAs for predicting other cancer outcomes of subjects.Other cancers can include any cancers described herein.miRNA can include miRNAs in acellular samples.miRNA can include serum miRNA.miRNA can also include any miRNAs in body fluids described herein. EXAMPLES

[0125] Extracellular microRNA profiling for predicting disease outcome in subjects with high-grade serous ovarian cancer Provided herein are methods for identifying serum miRNAs as markers predictive of HGSOC outcome in a subject.

[0126] MicroRNA (miRNA) profiles for over 2000 microRNAs were generated from 4046 body fluid samples taken from control subjects and subjects with ovarian cancer using the 3D-Gene® miRNA Labeling kit and 3D-Gene® Human miRNA Oligo Chip (Toray Industries, Inc.) and deposited in the NCBI database under accession number GSE106817. This is described in the paper by Yokoi A et al. (described in Example 1). Pretreatment serum miRNA profiles were analyzed from an additional 442 subjects with ovarian tumors and 969 healthy controls. Clinical information was analyzed, including age, disease stage, histological subtype, treatment status, and disease outcome of the subjects. This study was approved by the National Cancer Center Hospital Institutional Review Board (2015-376, 2016-29), and each subject provided written informed consent. By excluding 262 subjects with other EOC, other malignant tumors, borderline malignant tumors, or benign tumors, 180 subjects with HGSOC were distinguished (see Figure 1). Then, after excluding 3 subjects with insufficient clinical information and 2 subjects with low quality of body fluid RNA samples, the association between the body fluid sample miRNA profile and disease outcome prediction of 175 subjects with HGSOC was evaluated. Among them, 2 subjects with missing recurrence details were excluded from PFS analysis. Furthermore, 210 miRNAs were selected from 2038 miRNAs in the dataset according to the criteria used in Example 1 above. These miRNAs were detected in extracellular vesicles derived from ovarian cancer cells.

[0127] Pretreatment serum miRNA levels of 210 subjects from 175 patients with HGSOC were used to predict disease outcomes in subjects. Characteristics of the 210 subjects with HGSOC (Table 2) included a median age of 60 years (range 28-82 years), with 92% of subjects diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage III or IV disease. Approximately half of the subjects received neoadjuvant chemotherapy, and 151 subjects (86.3%) underwent complete or optimal cytoreductive surgery. Additionally, 150 subjects (85.7%) received adjuvant chemotherapy. Median follow-up was 54.6 months (range 3.5-144.1 months).

[0128] [Table 2] Note: Data are presented as n (%) unless otherwise stated.

[0129] To investigate the association of potential miRNAs (Table 1) with OS, Kaplan-Meier curves were generated after stratifying subjects into high- and low-expression groups based on the median level of each miRNA. Thirteen miRNAs were associated with significantly worse OS (miR-187-5p, P = 0.040; miR-711, P = 0.033; miR-1229-5p, P = 0.024; miR-1908-5p, P = 0.011; miR-1914-3p, P = 0.041; miR-4513, P = 0.017). miR‐4656, P = 0.017; miR‐4787‐3p, P = 0.040; miR‐6727‐5p, P = 0.044; miR‐6850‐5p, P = 0.012; miR‐6870‐5p, P = 0.024; miR‐7107‐5p, P = 0.034; miR‐7150, P = 0.014; Figure 2). In contrast, miR‐6787‐5p was the miRNA associated with favorable OS (P = 0.045; Figure 2). Cox regression analysis was performed for OS and PFS using the 14 miRNAs as continuous variables. Univariate Cox regression analysis for OS yielded hazard ratios (HRs) of 1.383 (P = 0.011) and 1.394 (P = 0.046) for miR-6870-5p and miR-187-5p, respectively. Similarly, univariate Cox regression analysis for PFS yielded HRs of 1.280 (P = 0.027) and 1.375 (P = 0.029) for miR-6870-5p and miR-187-5p, respectively. Furthermore, HRs were calculated for three additional miRNAs (miR-6727-5p [HR 1.469; P = 0.009], miR-6850-5p [HR 1.641; P = 0.012], and miR-1908-5p [HR 1.489; P = 0.043]). Table 3 summarizes the hazard ratios (HRs) and 95% confidence intervals (CIs) for (A) overall survival (OS) and (B) progression-free survival (PFS), calculated using miRNA levels as continuous variables.

[0130] [Table 3]

[0131] Hazard ratios (HRs) and 95% confidence intervals (CIs) for (A) overall survival (OS) and (B) progression-free survival (PFS) were calculated using miRNA levels as continuous variables.

[0132] Ten circulating miRNAs (miR-320a, miR-665, miR-3184-5p, miR-6717-5p, miR-4459, miR-6076, miR-3195, miR-1275, miR-3185, and miR-4640-5p) were used in a predictive model for differentiation between ovarian cancer samples and healthy control samples (Yokoi A et al.). However, in the present study, none of them were significantly associated with the prognosis of HGSOC patients.

[0133] The levels of disease outcome predictive miRNAs in healthy controls were assessed as shown in Figure 3. miR-187-5p and miR-6870-5p were significantly higher in subjects with HGSOC than in healthy controls (P = 0.042 and P < 0.001, respectively). The levels of miR-1908-5p, miR-6727-5p, and miR-6850-5p were significantly lower in HGSOC (P < 0.001).

[0134] The indexes related to OS were calculated using miR-187-5p, miR-6870-5p, and miR-1908-5p. First, the expression values ​​of two miRNAs (miR-187-5p and miR-6870-5p) were used to calculate index-OS1, but the Kaplan-Meier curves for OS showed no significant difference between the high and low groups (P = 0.294; Figure 4A). Therefore, index-OS2 was calculated using miR-187-5p, miR-6870-5p, and miR-1908-5p. miR-1908-5p was the third OS-related miRNA with marginal significance (P = 0.082; Table 3). Patient characteristics stratified by index-OS2 are shown in Table 4.

[0135] [Table 4] Note: Data are presented as n (%) unless otherwise stated.

[0136] Kaplan-Meier curves for OS showed that patients with high index had a significantly shorter OS than those with low index ( P = 0.036; Fig. 4B ).

[0137] Similarly, five miRNAs (miR-187-5p, miR-6870-5p, miR-6727-5p, miR-1908-5p, and miR-6850-5p) were used to calculate the index for PFS. Kaplan-Meier curves for PFS showed that patients with high index-PFS1 had significantly shorter PFS than those with low index-PFS1 (P = 0.003; Figure 5A). Then, we simplified the index-PFS1 and created index-PFS2 using miR-187-5p, miR-6870-5p, and miR-6727-5p. Patient characteristics stratified by index-PFS2 are shown in Table 5.

[0138] [Table 5] Note: Data are presented as n (%) unless otherwise stated.

[0139] Kaplan-Meier curves for PFS showed that patients with high index had significantly shorter PFS than those with low index (P = 0.006; Fig. 5B). Therefore, index-PFS2 was sufficient to predict early recurrence. Furthermore, we stratified patients into three groups (low, intermediate, and high) based on index-OS2 and index-PFS2. Kaplan-Meier curves for OS showed that patients with high index had significantly worse OS than those with intermediate or low index (P = 0.003 and P = 0.033, respectively; Fig. 6A). Furthermore, Kaplan-Meier curves for PFS showed that patients with low index had significantly better PFS than those with high and intermediate index (P < 0.001 and P = 0.003, respectively; Fig. 6B). The correlation between index-OS2 and index-PFS2 was assessed using Pearson's correlation coefficient, which revealed a significant correlation (R2=0.859, P<0.001; Fig. 6C). Therefore, patients with intermediate indices were considered to have relatively poor PFS and good OS.

[0140] The indices related to OS (index-OS1 and index-OS2) and PFS (index-PFS1 and index-PFS2) are summarized as follows: 1. Indicator-OS1=0.218×(miR-187-5p)+0.280×(miR-6870-5p), 2. Indicator-OS2=0.148×(miR-187-5p)+0.273×(miR-6870-5p)+0.186×(miR-1908-5p), 3. Index‐PFS1 = 0.034 × (miR‐187‐5p) + 0.236 × (miR‐6870‐5p) + 0.504 × (miR‐6727‐5p) + 0.048 × (miR‐1908‐5p) − 0.251 × (miR‐6870‐5p), and 4. Indicator-PFS2=0.031×(miR-187-5p)+0.231×(miR-6870-5p)+0.351×(miR-6727-5p).

[0141] Multivariate Cox regression analysis for OS and PFS was performed to assess whether the indices were independent predictors of disease outcome. The association between FIGO stage and residual tumor volume at the time of debulking surgery (complete or optimal vs. suboptimal or no surgery) was calculated (P = 0.012 and P < 0.001, respectively; Table 6).

[0142] [Table 6] a 0 for complete / optimal surgery, 1 for suboptimal surgery / no surgery. b Index-OS2=0.148×(miR-187-5p)+0.273×(miR-6870-5p)+0.186×(miR-1908-5p). c Index-PFS2=0.031×(miR-187-5p)+0.231×(miR-6870-5p)+0.351×(miR-6727-5p).

[0143] By univariate analysis, FIGO stage and residual tumor volume at the time of debulking surgery (complete or optimal vs. suboptimal or no surgery) were also significantly associated with poor OS (P = 0.012 and P < 0.001, respectively; Table 6).

[0144] Multivariate analysis for OS showed that residual tumor volume and index-OS2 were independent predictors of poor disease outcome (HR 2.165 [95% CI, 1.277-3.669], P = 0.004; and HR 2.343 [95% CI, 1.182-4.641], P = 0.015, respectively). Univariate analysis for PFS showed that FIGO stage at the time of debulking surgery and residual tumor volume were also associated with worse disease outcome (P = 0.001 and P = 0.008, respectively; Table 6). Multivariate analysis for PFS showed an association between FIGO stage and residual tumor volume (HR 1.390 [95% CI, 1.069-1.809], P = 0.014; and HR 1.771 [95% CI, 1.077-2.914], P = 0.024, respectively), indicating that FIGO stage and residual tumor volume at the time of debulking surgery were also significantly associated with worse disease outcomes. Moreover, index-PFS2 was also a significant independent predictor of poor disease outcomes (HR 2.357 [95% CI, 1.289-4.311], P = 0.005; Table 6). Thus, both indices were significant independent predictors of poor disease outcomes.

[0145] The functions of miR-187-5p, miR-6870-5p, and miR-1908-5p in EOC cells were investigated. A2780 and SK-OV-3 cell lines were purchased from ATCC and maintained in RPMI-1640 (Nacalai Tesque, Inc.) containing 10% FBS and antibiotics. mirVana miRNA mimics for miR-187-5p (ID: MC12652), miR-1908-5p (ID: MC13846), miR-6870-5p (ID: MC27099), and negative control #1 were purchased from Thermo Fisher Scientific. Cells were seeded in 96-well plates and transfected with 20 nM of the mimics using Lipofectamine RNAi Max (Thermo Fisher Scientific). After 24, 48, and 72 h of culture, cell viability was assessed using the CellTiter-Glo 2.0 Cell Viability Assay (Promega Corp.) and a microplate reader (Gen5 Synergy H4; BioTek). For drug sensitivity analysis, after 24 h of transfection, the medium was replaced with cisplatin (Nichi-Iko Pharmaceutical Co., Ltd.) or docetaxel (Tokyo Chemical Industry Co., Ltd.) containing vehicle, and cells were cultured for 48 h. Cell viability was then assessed using the CellTiter-Glo 2.0 Cell Viability Assay. Quantitative RT-PCR was performed to assess transfection efficiency. Total RNA was extracted using the miRNeasy Mini Kit (Qiagen), and cDNA was synthesized using the TaqMan Advanced miRNA cDNA Synthesis Kit (Thermo Fisher Scientific). TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific) and TaqMan Advanced miRNA Assays (assay IDs are 479423_mir, 478735_mir, and 480864_mir; Thermo Fisher Scientific) were used for quantitative RT-PCR.

[0146] Pearson's correlation coefficient was used to assess the correlation between the two indices. Welch's t-test was used to compare miRNA expression and cell viability. Differences with P<0.05 were considered statistically significant.

[0147] miR-187-5p, miR-6870-5p, and miR-1908-5p were transfected into A2780 and SK-OV-3 cell lines, respectively (Figure 7A). Proliferation assays revealed that miR-1908-5p significantly reduced cell viability in both cell lines (A2780, P<0.05; and SK-OV-3, P<0.01; Figure 7B). The remaining two miRNAs slightly suppressed cell proliferation in SK-OV-3 cells. We then assessed the effects of miRNAs on therapeutic resistance (Figure 7C). EXAMPLES

[0148] Extracellular microRNA profiling for predicting disease outcome in subjects with ovarian clear cell carcinoma Provided herein are methods for identifying serum miRNAs as markers predictive of ovarian clear cell cancer outcome in a subject.

[0149] 210 microRNAs were selected according to Example 1. Twenty-five subjects with ovarian clear cell carcinoma and their formalin-fixed paraffin-embedded samples (cases 1-20) and fresh frozen surgical samples (cases 21-25) were used for analysis. The samples were described in Yoshida K, Yokoi A, Sugiyama M et al., Expression of the chrXq27.3 miRNA cluster in recurrent ovarian clear cell carcinoma and its impact on cisplatin resistance, Oncogene. 2021; 40: 1255-1268, which is incorporated herein by reference in its entirety.

[0150] Total RNA was extracted from formalin-fixed paraffin-embedded samples using the miRNeasy FFPE Kit (Qiagen, Hilden, Germany) and from fresh-frozen surgical samples using the miRNeasy Mini Kit (Qiagen). Comprehensive miRNA sequencing was performed according to the method described in Yoshida K, Yokoi A, Kagawa T et al. Unique miRNA profiling of squamous cell carcinoma arising from ovarian mature teratoma: comprehensive miRNA sequence analysis of its molecular background. Carcinogenesis. 2019;40:1435-1444), which is incorporated herein by reference in its entirety.

[0151] We selected 442 subjects with ovarian tumors whose preoperative serum miRNA profiles were described in Yokoi A, Matsuzaki J, Yamamoto Y et al. Integrated extracellular microRNA profiling for ovarian cancer screening. Nat Commun. 2018;9:4319, which is hereby incorporated by reference in its entirety. In addition, medical records, including age, stage, histological subtype, residual tumor volume, adjuvant therapy, and recurrence or death events, were retrospectively reviewed. This study was approved by the Institutional Review Board of the National Cancer Center Hospital (2015‐376, 2016‐29) and the Ethics Committee of Nagoya University (2015‐0237, 2017‐0053, and 2017‐0497), and each subject provided written informed consent.

[0152] Statistical analysis was performed using SPSS version 28 (IBM Corp., Armonk, NY). Overall survival (OS) was defined as the time from treatment initiation to death from any cause, and PFS was defined as the time from treatment initiation to tumor progression. Kaplan-Meier curves were used for analysis of OS and PFS, which were compared using the log-rank test. Univariate and multivariate Cox regression analyses were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Disease outcome prediction indices for OS and PFS were calculated separately based on the multivariate Cox regression model for miRNA candidates. Correlations between the two indices were assessed using Pearson's correlation coefficient.

[0153] By excluding 354 subjects with other epithelial ovarian cancers, other malignant tumors, borderline malignant tumors, or benign tumors, 68 subjects with ovarian clear cell carcinoma were distinguished (see Figure 8). After excluding 2 subjects with insufficient clinical information, the association between body fluid microRNA profiles and disease outcome prediction was evaluated for 66 subjects with ovarian clear cell carcinoma. The characteristics of these subjects are shown in Table 7.

[0154] [Table 7]

[0155] The median age of the subjects was 55.5 (range 27-76 years), and 36 subjects (54.5%) were diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage I disease. All but one subject underwent complete cytoreductive surgery. In addition, 60 subjects (90.9%) received adjuvant chemotherapy, typically a combination of carboplatin and paclitaxel. The median follow-up period was 64.3 months (range 8.0-153.3 months).

[0156] To investigate the impact of miRNAs on disease outcome prediction on OS and PFS, Cox regression analysis for OS and PFS was performed using 210 miRNAs as continuous variables. Univariate Cox regression analysis for OS found that only two miRNAs were significantly associated with shorter OS, and 22 miRNAs were significantly associated with longer OS (Figure 9A). Similarly, univariate Cox regression analysis for PFS found that only one miRNA was significantly associated with shorter PFS, and 16 miRNAs were significantly associated with longer PFS (Figure 9B). The expression of each miRNA from ovarian clear cell carcinoma tissues was evaluated (Figure 9A-Figure 9B). Twelve miRNAs were associated with OS and were detected in ovarian clear cell carcinoma tissues, while the other 12 miRNAs were not detected in the tissues (Figure 9A). Similarly, seven miRNAs were associated with PFS and were detected in ovarian clear cell carcinoma tissues, while the other 10 miRNAs were not detected in the tissues (Figure 9B).

[0157] To assess whether miRNAs were independent predictors of disease outcome, multivariate Cox regression analysis for OS and PFS was performed for each miRNA, age, stage, and residual tumor volume.By multivariate analysis, 6 of the 12 miRNAs were associated with significantly better OS (miR-150-3p, hazard ratio [HR] 0.682, p = 0.007; miR-3195, HR 0.365, p = 0.017; miR-7704, HR 0.246, p = 0.028; miR-365a-5p, HR 0.454, p = 0.023; miR-4730, HR 0.358, p = 0.015; miR-671-5p, HR 0.578, p = 0.005; Table 8). Similarly, four of the seven miRNAs were associated with significantly better PFS (miR‐150‐3p, HR 0.731, p = 0.018; miR‐3195, HR 0.283, p = 0.003; miR‐7704, HR 0.278, p = 0.020; miR‐6805‐5p, HR 0.167, p = 0.011), as listed in Table 8 below.

[0158] [Table 8] JPEG2025506080000011.jpg161170

[0159] Kaplan-Meier curves were generated after stratifying patients into high and low expression groups based on the median levels of seven miRNAs (miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, and miR-671-5p). As shown in Figure 10, only one miRNA among the six miRNAs (miR-150-3p, miR-3195, miR-7704, and miR-6805-5p) was significantly associated with better OS [miR-4730 (p = 0.011)]. Two of the four miRNAs (miR‐150‐3p, miR‐3195, miR‐7704, miR‐365a‐5p, miR‐4730, miR‐671‐5p, and miR‐6805‐5p) were associated with significantly better PFS [miR‐3195 (p = 0.033); miR‐7704 (p = 0.044).

[0160] To improve the accuracy of disease outcome prediction using these miRNAs, we calculated the index for OS. First, as shown in Figure 11, we calculated the index-OS using the body fluid expression values ​​of three miRNAs (miR-150-3p, miR-3195, and miR-7704), which were common candidates in the Cox regression analysis of both OS and PFS. The index for OS (index-OS) was as follows: (index-OS) = (0.463 × miR-150-3p) + (1.323 × miR-3195) + (0.636 × miR-7704). As shown in Figure 12A, the Kaplan-Meier curve for OS showed that patients with low index had significantly shorter OS than those with high index (p = 0.012). Similarly, we calculated the index for PFS using the same three miRNAs (miR-150-3p, miR-3195, and miR-7704). The index for PFS (index-PFS) was as follows: (index-PFS) = (0.399 x [miR-150-3p]) + (1.426 x [miR-3195]) + (0.480 x [miR-7704]). As shown in Figure 12B, the Kaplan-Meier curve for PFS showed that patients with low index-PFS had significantly shorter PFS than those with high index-PFS (p = 0.003). Therefore, index-OS and index-PFS were sufficient to predict disease outcome and early recurrence. Patient characteristics stratified by index-OS and index-PFS are shown in Table 9.

[0161] [Table 9]

[0162] The expression of the three miRNAs was divided into two groups according to index-OS and index-PFS, as shown in Figure 13A. Finally, the correlation between index-OS and index-PFS was assessed using Pearson's correlation coefficient, as shown in Figure 13B. The assessment revealed a significant correlation (R2=0.992, p<0.001). EXAMPLES

[0163] Predicting Cancer Outcomes Provided herein are methods for identifying miRNAs as markers predictive of cancer outcome in a subject.

[0164] A number of subjects are selected to predict cancer outcomes. The selected subjects may share a common cancer, cancer type, or cancer subtype. The selected subjects must not have other cancers, cancer types, or cancer subtypes. A sample is obtained from each of the subjects. The sample includes a cell-free sample or a bodily fluid sample. The sample includes a plurality of miRNAs. The level of at least one of the miRNAs is provided to an algorithm, such as a statistical model. The algorithm generates a formula that includes the levels of a subset of the provided miRNAs. The formula is then used to calculate a disease outcome index. The formula is then used to stratify the subjects into two populations based on high or low index values, the two populations having different disease outcomes or predicted disease outcomes.

[0165] Embodiment 1. (a) obtaining at least two microRNA (miRNA)-derived indices for a subject having ovarian cancer; (b) determining an outcome of the ovarian cancer in the subject; and Including, The at least two miRNAs are obtained from a cell-free sample of the subject. 2. The method of embodiment 1, wherein the ovarian cancer comprises type I ovarian cancer, type II ovarian cancer, or a combination thereof. 3. The method of embodiment 2, wherein said ovarian cancer comprises said type I ovarian cancer. 4. The method of embodiment 3, wherein said type I ovarian cancer comprises endometrioid carcinoma, ovarian clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, or a combination thereof. 5. The method of embodiment 4, wherein said type I ovarian cancer comprises said endometrioid carcinoma. 6. The method of embodiment 4, wherein said type I ovarian cancer comprises said ovarian clear cell carcinoma. 7. The method of embodiment 4, wherein said type I ovarian cancer comprises said mucinous carcinoma. 8. The method of embodiment 4, wherein said type I ovarian cancer comprises said low-grade serous carcinoma. 9. The method of any one of embodiments 2-8, wherein the ovarian cancer comprises type II ovarian cancer. 10. The method of embodiment 9, wherein said type II ovarian cancer comprises high-grade serous ovarian cancer. 11. The method of any one of claims 1 to 10, wherein the ovarian cancer comprises epithelial ovarian cancer, germ cell tumors, stromal cell tumors, or a combination thereof. 12. The method of embodiment 11, wherein the ovarian cancer comprises epithelial ovarian cancer. 13. The method of embodiment 11, wherein the ovarian cancer comprises a germ cell tumor. 14. The method of embodiment 11, wherein the ovarian cancer comprises a stromal cell tumor. 15. The method of any one of embodiments 1-14, wherein the at least two miRNAs include miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, miR-711, miR-1229-5p, miR-1914-3p, miR-4513, miR-4656, miR-4787-3p, miR-6787-5p, miR-6850-5p, miR-7107-5p, miR-7150, miR-150-3p, miR-3195, miR-7704, miR-365a-5p, miR-4730, miR-671-5p, miR-6805-5p, or a combination thereof. 16. The method of any one of embodiments 1 to 15, wherein the at least two miRNAs include miR-187-5p, miR-6870-5p, miR-1908-5p, miR-6727-5p, or a combination thereof. 17. The method of any one of embodiments 1 to 16, wherein the at least two miRNAs include miR-187-5p. 18. The method of any one of embodiments 1 to 17, wherein the at least two miRNAs include miR-6870-5p. 19. The method of any one of embodiments 1 to 18, wherein the at least two miRNAs include miR-1908-5p. 20. The method of any one of embodiments 1 to 19, wherein the at least two miRNAs include miR-6727-5p. 21. The method of any one of embodiments 1 to 20, wherein the at least two miRNAs include miR-187-5p and miR-6870-5p. 22. The method of any one of embodiments 1 to 21, wherein the at least two miRNAs include miR-187-5p, miR-6870-5p, and miR-1908-5p. 23. The method of any one of embodiments 1 to 22, wherein the at least two miRNAs include miR-187-5p, miR-6870-5p, and miR-6727-5p. 24. The method of any one of embodiments 1 to 23, wherein the at least two miRNAs include miR-187-5p, miR-6870-5p, miR-1908-5p, and miR-6727-5p. 25. The method of any one of embodiments 1 to 24, wherein the at least two miRNAs include miR-150-3p, miR-3195, miR-7704, or a combination thereof. 26. The method of any one of embodiments 1 to 25, wherein the at least two miRNAs include miR-150-3p. 27. The method of any one of embodiments 1 to 26, wherein the at least two miRNAs include miR-3195. 28. The method of any one of embodiments 1 to 27, wherein the at least two miRNAs include miR-7704. 29. The method of any one of embodiments 1 to 28, wherein the at least two miRNAs include miR-150-3p, miR-3195, and miR-7704. 30. The method of any one of the preceding embodiments, wherein the index is derived from the levels of the at least two miRNAs. 31. The method of embodiment 30, wherein the levels of the at least two miRNAs comprise the expression levels of the at least two miRNAs or a derivative of the expression levels of the at least two miRNAs. 32. The method according to embodiment 30 or 31, wherein the index is obtained by processing the levels of the at least two miRNAs of at least two subjects with ovarian cancer. 33. The method according to any one of embodiments 30 to 32, wherein the indicator is obtained by processing the levels of the at least two miRNAs in at least 10 subjects with ovarian cancer. 34. The method according to any one of embodiments 30 to 33, wherein the indicator is obtained by processing the levels of the at least two miRNAs in at least 50 subjects with ovarian cancer. 35. The method according to any one of embodiments 30 to 34, wherein the indicator is obtained by processing the levels of the at least two miRNAs in at least 100 subjects with ovarian cancer. 36. The method of any one of embodiments 30 to 35, wherein the index comprises a formula comprising the levels of the at least two miRNAs. 37. The above formula is (a) 0.218 × (level of miR‐187‐5p) + 0.280 × (level of miR‐6870‐5p), or (b) 0.148 × (the level of miR-187-5p) + 0.273 × (the level of miR-6870-5p) + 0.186 × (the level of miR-1908-5p), or (c) 0.034 × (the level of miR-187-5p) + 0.236 × (the level of miR-6870-5p) + 0.504 × (the level of miR-6727-5p) + 0.048 × (the level of miR-1908-5p), or (d) 0.031 × (the level of miR-187-5p) + 0.231 (the level of miR-6870-5p) + 0.351 × (the level of miR-6727-5p) 37. The method of embodiment 36, comprising: 38. The above formula is (a) 0.463 × (level of miR‐150‐3p) + 1.323 × (level of miR‐3195) + 0.636 × (level of miR‐7704), or (b) 0.399 × (the level of miR-150-3p) + 1.426 × (the level of miR-3195) + 0.480 × (the level of miR-7704) 37. The method of embodiment 36, comprising: 39. A method according to any one of embodiments 30 to 38, wherein the index is obtained by processing the levels of the at least two miRNAs using an algorithm. 40. The method of embodiment 39, wherein the algorithm comprises a statistical model. 41. The method of embodiment 40, wherein the statistical model comprises a linear regression model. 42. The method of embodiment 41, wherein the linear regression model comprises a Cox model. 43. The method of embodiment 42, wherein the Cox model comprises a univariate Cox model or a multivariate Cox model. 44. The method of embodiment 43, wherein the Cox model comprises the univariate Cox model. 45. The method of embodiment 43, wherein the Cox model comprises the multivariate Cox model. 46. ​​The method of any one of embodiments 30 to 45, wherein the levels of the at least two miRNAs are determined by microarray, sequencing reaction, probe hybridization, polymerase chain reaction (PCR), or a combination thereof. 47. The method of embodiment 46, wherein the levels of the at least two miRNAs are determined by microarray. 48. The method of any one of embodiments 1-47, wherein the ovarian cancer is stage I ovarian cancer, stage II ovarian cancer, stage III ovarian cancer, or stage IV ovarian cancer. 49. The method of embodiment 48, wherein the stage I ovarian cancer comprises stage IA ovarian cancer or stage IB ovarian cancer. 50. The method of embodiment 48, wherein the stage II ovarian cancer comprises stage IIA ovarian cancer or stage IIB ovarian cancer. 51. The method of embodiment 48, wherein the stage III ovarian cancer comprises stage IIIA ovarian cancer, stage IIIB ovarian cancer, or stage IIIC ovarian cancer. 52. The method of any one of embodiments 1-51, wherein the outcome comprises the amount of time during or after the subject is treated for the ovarian cancer that the ovarian cancer does not progress to the next stage. 53. The method of any one of embodiments 1-51, wherein the outcome comprises the amount of time the subject survives after the subject is determined to have ovarian cancer or after the subject is treated for ovarian cancer. 54. The method of any one of embodiments 1 to 51, wherein the outcome comprises progression-free survival (PFS) or overall survival (OS). 55. The method of embodiment 54, wherein the outcome comprises the PFS. 56. The method of embodiment 54, wherein the method comprises the OS. 57. The method of any one of the preceding embodiments, wherein the acellular sample of the subject comprises a bodily fluid of the subject. 58. The method of embodiment 57, wherein the bodily fluid comprises serum, urine, sweat, plasma, tears, semen, vaginal fluid, amniotic fluid, milk, or a combination thereof. 59. The method of embodiment 58, wherein the bodily fluid sample comprises serum. 60. The method of any one of embodiments 1 to 59, wherein the at least two miRNAs are derived from extracellular vesicles of the subject.

[0166] Although the present invention has been described with reference to the above specification, the description and illustration of the embodiments herein are not to be construed in a limiting sense. Numerous variations, modifications, and substitutions can now be made by those skilled in the art without departing from the present invention. It is to be further understood that all aspects of the present invention are not limited to the specific descriptions, configurations, or relative proportions described herein, which depend on various conditions and variables. It can be understood that various alternatives to the embodiments of the present invention described herein can be employed in carrying out the present invention. It is therefore contemplated that any such alternatives, modifications, variations, or equivalents are intended to be covered by the present invention. It is intended that the following claims define the scope of the present invention, and that methods and structures within the scope of these claims and their equivalents are covered by the claims.

Claims

1. (a) obtaining an index derived from a combination of micro ribonucleic acid (miRNA) of a subject with ovarian cancer; (b) determining the outcome of the ovarian cancer in the subject based on the index; the miRNA is obtained from a cell-free sample of the subject; The method, wherein the combination of miRNAs is one or more selected from a combination comprising miR-187-5p, miR-6870-5p, and miR-1908-5p, a combination comprising miR-187-5p, miR-6870-5p, miR-6727-5p, miR-1908-5p, and miR-6850-5p, a combination comprising miR-187-5p, miR-6870-5p, and miR-6727-5p, and a combination comprising miR-150-3p, miR-3195, and miR-7704.

2. The method of claim 1 , wherein the index is derived from the level of the miRNA.

3. The method described in claim 2, wherein the level comprises the expression level of the miRNA or a derivative of the expression level.

4. The method of claim 2 , wherein the indicator is obtained by processing the level with an algorithm.

5. The method of claim 4 , wherein the algorithm is a statistical model.

6. The method of claim 5 , wherein the statistical model is a linear regression model.

7. The method of claim 6 , wherein the linear regression model is a Cox model.

8. The method of claim 7 , wherein the Cox model is the univariate Cox model.

9. The method of claim 7 , wherein the Cox model is the multivariate Cox model.

10. The method of claim 7, wherein the level is determined by microarray, sequencing reaction, probe hybridization, polymerase chain reaction (PCR), or a combination thereof.

11. The method described in claim 7, wherein the level is determined by microarray.

12. 8. The method of claim 7, wherein the outcome comprises the amount of time the ovarian cancer does not progress to the next stage during or after the subject is treated for the ovarian cancer.

13. 8. The method of claim 7, wherein the outcome comprises the amount of time the subject survives after the subject is determined to have the ovarian cancer or after the subject has been treated for the ovarian cancer.

14. 8. The method of claim 7, wherein the outcome is progression-free survival (PFS) or overall survival (OS).

15. 8. The method of claim 7, wherein the outcome is progression-free survival (PFS).

16. 8. The method of claim 7, wherein the outcome is overall survival (OS).

17. The method of claim 7 , wherein the index is obtained by processing the levels of at least two subjects with the ovarian cancer.

18. 8. The method of claim 7, wherein the index is obtained by processing the levels of at least 10 subjects with the ovarian cancer.

19. 8. The method of claim 7, wherein the index is obtained by processing the levels of at least 50 subjects with the ovarian cancer.

20. 8. The method of claim 7, wherein the index is obtained by processing the levels of at least 100 subjects with the ovarian cancer.

21. The method of claim 7 , wherein the index comprises an expression that includes the level.

22. Obtaining a microribonucleic acid (miRNA)-derived index in a subject with ovarian cancer; determining an outcome of the ovarian cancer in the subject based on the index; the miRNA is obtained from a cell-free sample of the subject; The indicator is (a) 0.148 × (level of miR-187-5p) + 0.273 × (level of miR-6870-5p) + 0.186 × (level of miR-1908-5p), or (b) 0.034 × (level of miR‐187‐5p) + 0.236 × (level of miR‐6870‐5p) + 0.504 × (level of miR‐6727‐5p) + 0.048 × (level of miR‐1908‐5p) − 0.251 × (miR‐6850‐5p), or (c) 0.031 × (level of miR-187-5p) + 0.231 (level of miR-6870-5p) + 0.351 × (level of miR-6727-5p) That's the method.

23. Obtaining a microribonucleic acid (miRNA)-derived index in a subject with ovarian cancer; determining an outcome of the ovarian cancer in the subject based on the index; the miRNA is obtained from a cell-free sample of the subject; The indicator is (a) 0.463 × (level of miR-150-3p) + 1.323 × (level of miR-3195) + 0.636 × (level of miR-7704), or (b) 0.399 × (level of miR-150-3p) + 1.426 × (level of miR-3195) + 0.480 × (level of miR-7704) That's the method.

24. 24. The method of any one of claims 1 to 23, wherein the ovarian cancer comprises type I ovarian cancer, type II ovarian cancer, or a combination thereof.

25. 25. The method of claim 24, wherein the type I ovarian cancer comprises endometrioid carcinoma, ovarian clear cell carcinoma, mucinous carcinoma, low-grade serous carcinoma, or a combination thereof, and / or the type II ovarian cancer comprises high-grade serous ovarian carcinoma.

26. 24. The method of any one of claims 1 to 23, wherein the ovarian cancer comprises epithelial ovarian cancer, germ cell tumor, stromal cell tumor, or a combination thereof.

27. 24. The method of any one of claims 1 to 23, wherein the ovarian cancer is stage I ovarian cancer, stage II ovarian cancer, stage III ovarian cancer, or stage IV ovarian cancer.

28. The stage I ovarian cancer is stage IA ovarian cancer or stage IB ovarian cancer. the stage II ovarian cancer is stage IIA ovarian cancer or stage IIB ovarian cancer; or 28. The method of claim 27, wherein the stage III ovarian cancer is stage IIIA ovarian cancer, stage IIIB ovarian cancer, or stage IIIC ovarian cancer.

29. The method of any one of claims 1 to 23, wherein the acellular sample of the subject is a body fluid of the subject.

30. 30. The method of claim 29, wherein the bodily fluid comprises serum, urine, sweat, plasma, tears, semen, vaginal fluid, amniotic fluid, milk, or a combination thereof.

31. The method of any one of claims 1 to 23, wherein the miRNA is derived from extracellular vesicles of the subject.

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