MiRNA marker for lung cancer diagnosis, risk assessment and / or prognosis prediction and application thereof

Abstract

The invention belongs to the technical field of gene detection, and particularly relates to a miRNA marker for lung cancer diagnosis, risk assessment and / or prognosis prediction and application of the miRNA marker. The peripheral blood extracellular vesicle miRNA marker combination capable of being effectively used for early screening of the lung cancer is searched and selected based on the expression quantity of miRNA in clinical actual data by means of hypothesis testing and other methods, the selected miRNA marker has higher specificity and sensitivity in diagnosis of lung cancer patients, and the miRNA marker combination can be used for early screening of the lung cancer patients. The research result is of great significance to screening and detection of lung cancer patients.

Description

Technical Field

[0001] The present invention belongs to the technical field of gene detection, and specifically relates to miRNA markers and uses thereof for lung cancer diagnosis, risk assessment and / or prognosis prediction. Background Art

[0002] Lung cancer is one of the leading causes of cancer deaths worldwide. In 2018 alone, there were an estimated 2.09 million new cases of lung cancer and 1.76 million deaths from lung cancer worldwide. In terms of pathological histology, lung cancer can be divided into small cell lung cancer and non-small cell lung cancer, the latter of which can be further divided into squamous cell carcinoma, large cell carcinoma and adenocarcinoma. Among them, non-small cell carcinoma is currently the main type of lung cancer, accounting for more than 80% of all lung cancer cases.

[0003] The development of lung cancer is complex, with genetic and environmental factors interacting throughout the development process. Environmental risks include, but are not limited to, smoking, radiation exposure, and environmental toxins such as asbestos, arsenic, and chromium. Early-stage, non-metastatic lung tumors are most often treated with surgical resection, but advanced or metastatic lung cancer requires chemotherapy alone or in combination with radiation therapy. The disease presentation of advanced lung cancer, the heterogeneity of histological subtypes, the limited understanding of tumor biology, and the development of drug resistance are key reasons for the poor prognosis of lung cancer.

[0004] At present, the early diagnosis of lung cancer has improved slightly. Through the use of low-dose computed tomography (LDCT) for lung cancer screening, the mortality rate of high-risk groups for lung cancer was reduced by 20% in 2010. However, the LDCT screening method has a high false positive rate, which can lead to overdiagnosis, unnecessary radiation exposure, and economic and psychological burden on patients. In order to improve the diagnosis and understanding of lung cancer, it is still necessary to have a deeper understanding of the pathogenesis of lung cancer and its tumors from the perspective of genetics and genomics.

[0005] In recent years, small molecules of about 20-24 nucleotides called microRNAs have attracted attention. As a type of non-coding short RNA, they play a role in regulating gene expression. miRNAs can recognize and bind to complementary sites in the 3' untranslated region (UTR) of target mRNAs to inhibit translation or degradation of target gene mRNAs, thereby controlling the activity of most protein-coding genes and playing a prominent role in tumorigenesis by regulating cell cycle, metastasis, angiogenesis, metabolism and apoptosis.

[0006] The expression levels of miRNAs vary greatly in different organisms and can serve as potential cancer markers. Summary of the invention

[0007] Based on the miRNA expression levels in actual clinical data, the present invention uses hypothesis testing and other methods to find and select effective miRNA markers for screening lung cancer.

[0008] In order to achieve the above technical objectives, the present invention proposes the following technical solutions:

[0009] In a first aspect, the present invention provides miRNA markers for lung cancer diagnosis, risk assessment and / or prognosis prediction, wherein the miRNA markers are selected from the following miRNA group:

[0010] HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P, HSA-MIR-99A-5P. ,

[0011] In some embodiments, the miRNA signature consists of 75 or at least 70, at least 60, at least 50, at least 40, at least 30, at least 25, at least 20 miRNAs.

[0012] In some preferred embodiments, the miRNA marker includes at least the following 20 miRNAs or consists of the following 20 miRNAs:

[0013] HSA-MIR-125A-5P, HSA-MIR-193A-5P, HSA-MIR-361-3P, HSA-MIR-483-3P, HSA-MIR-130B-5 P, HSA-MIR-223-5P, HSA-MIR-424-3P, HSA-MIR-542-3P, HSA-MIR-143-3P, HSA-MIR-320C, HS A-MIR-424-5P, HSA-MIR-582-3P, HSA-MIR-148A-3P, HSA-MIR-328-3P, HSA-MIR-4433B-5P, HSA-MIR-6803-3P, HSA-MIR-18B-3P, HSA-MIR-338-5P, HSA-MIR-450B-5P and HSA-MIR-744-5P.

[0014] The second aspect of the present invention provides the use of any of the aforementioned miRNA markers for lung cancer diagnosis, risk assessment and / or prognosis prediction and the use of the same in the preparation of products for lung cancer diagnosis, risk assessment and / or prognosis prediction.

[0015] In some embodiments, the product is used to test a sample from a subject to achieve lung cancer diagnosis, risk assessment, and / or prognosis prediction.

[0016] In some preferred embodiments, the sample includes, but is not limited to: blood, tissue, body fluid, excrement or a combination thereof.

[0017] In some preferred embodiments, the blood includes whole blood and peripheral blood.

[0018] In some more preferred embodiments, the sample is peripheral blood.

[0019] In some embodiments, the product comprises a reagent or a kit.

[0020] In some embodiments, the lung cancer diagnosis includes identifying and / or classifying subjects suffering from lung cancer and subjects not suffering from lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the subject sample, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold, the subject is identified and / or classified as a subject suffering from lung cancer; when the predicted value is less than the preset threshold, the subject is identified and / or classified as a subject not suffering from lung cancer.

[0021] In some embodiments, the risk assessment includes evaluating and / or rating the possibility of a subject who has not suffered from lung cancer suffering from lung cancer or progressing to lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold value, the subject is evaluated and / or rated as having a high possibility or high risk of suffering from lung cancer or progressing to lung cancer; when the predicted value is less than the preset threshold value, the subject is evaluated and / or rated as having a low possibility or low risk of suffering from lung cancer or progressing to lung cancer.

[0022] In some embodiments, the prognosis prediction includes predicting and / or evaluating the treatment effect or survival rate of a subject who has received anti-lung cancer treatment and suffers from lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or any 20 of them) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold, the subject has a poor treatment effect or a low survival rate; when the predicted value is lower than the preset threshold, the subject has a good treatment effect or a high survival rate.

[0023] In some preferred embodiments, when the miRNA marker consists of the following, the preset threshold is 0.235:

[0024] HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P and HSA-MIR-99A-5P. ,

[0025] In some preferred embodiments, when the miRNA marker consists of the following, the preset threshold is 0.480:

[0026] HSA-MIR-125A-5P, HSA-MIR-193A-5P, HSA-MIR-361-3P, HSA-MIR-483-3P, HSA-MIR-130B-5 P, HSA-MIR-223-5P, HSA-MIR-424-3P, HSA-MIR-542-3P, HSA-MIR-143-3P, HSA-MIR-320C, HS A-MIR-424-5P, HSA-MIR-582-3P, HSA-MIR-148A-3P, HSA-MIR-328-3P, HSA-MIR-4433B-5P, HSA-MIR-6803-3P, HSA-MIR-18B-3P, HSA-MIR-338-5P, HSA-MIR-450B-5P and HSA-MIR-744-5P.

[0027] In some embodiments, the predicted value is determined by:

[0028] (1) determining the expression levels of all miRNAs in any of the aforementioned miRNA markers (e.g., comprising 75 miRNA markers or comprising any 20 miRNA markers among the 75 miRNA markers) based on a sample from a subject;

[0029] (2) The expression levels of all miRNAs are trained by a machine learning model to obtain predicted values.

[0030] In some embodiments, the machine learning model includes, but is not limited to: a support vector machine algorithm and / or a leave-one-out cross-validation method.

[0031] In some embodiments, the calculation formula of the support vector machine algorithm is:

[0032]

[0033] Where w is the normal vector, b is the bias term, and x iis a vector that may contain k variables (e.g., 75 miRNA markers or the expression levels of all miRNAs of any 20 of the 75 miRNA markers), i is the predicted value.

[0034] In some embodiments, the model training comprises the following steps:

[0035] 1) Obtain the expression levels (x) of all miRNAs in the miRNA markers (75 miRNA markers or any 20 miRNA markers among the 75 miRNA markers) in the sample (which can be a certain sample or multiple samples) from the subject; i ), the predicted value (y i , each sample corresponds to a y i value);

[0036] 2) Draw a ROC curve based on the predicted value obtained in step 1), and determine a preset threshold based on the ROC curve.

[0037] In some embodiments, the preset threshold is based on the predicted value (y i ) is determined by the ROC curve drawn.

[0038] In some preferred embodiments, the determination of the preset threshold value includes:

[0039] A suitable threshold point is selected on the ROC curve and the area under the ROC curve is calculated.

[0040] The third aspect of the present invention provides the use of any of the aforementioned miRNA markers in lung cancer diagnosis, risk assessment and / or prognosis prediction.

[0041] In some embodiments, the lung cancer diagnosis comprises identifying and / or classifying subjects suffering from lung cancer and subjects not suffering from lung cancer.

[0042] In some embodiments, the risk assessment includes evaluating and / or ranking the likelihood of a subject who does not have lung cancer developing lung cancer or developing lung cancer.

[0043] In some embodiments, the prognosis prediction includes predicting and / or evaluating the treatment effect or survival rate of a subject suffering from lung cancer who has received anti-lung cancer treatment.

[0044] In some embodiments, the product is used to test a sample from a subject to achieve lung cancer diagnosis, risk assessment, and / or prognosis prediction.

[0045] In some preferred embodiments, the sample includes, but is not limited to: blood, tissue, body fluid and / or excrement.

[0046] In some preferred embodiments, the blood includes whole blood and peripheral blood.

[0047] In some more preferred embodiments, the sample is peripheral blood.

[0048] In some embodiments, the lung cancer diagnosis includes identifying and / or classifying subjects suffering from lung cancer and subjects not suffering from lung cancer, and the specific method is: obtaining the expression level of all miRNAs of any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold, the subject is identified and / or classified as a subject suffering from lung cancer; when the predicted value is less than the preset threshold, the subject is identified and / or classified as a subject not suffering from lung cancer.

[0049] In some embodiments, the risk assessment includes evaluating and / or rating the possibility of a subject who has not suffered from lung cancer suffering from lung cancer or progressing to lung cancer, and the specific method is: obtaining the expression levels of all miRNAs of any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold value, the subject is evaluated and / or rated as having a high possibility or high risk of suffering from lung cancer or progressing to lung cancer; when the predicted value is less than the preset threshold value, the subject is evaluated and / or rated as having a low possibility or low risk of suffering from lung cancer or progressing to lung cancer.

[0050] In some embodiments, the prognosis prediction includes predicting and / or evaluating the treatment effect or survival rate of a subject who has received anti-lung cancer treatment and suffers from lung cancer, and the specific method is: obtaining the expression level of all miRNAs of any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold value, the subject has a poor treatment effect or a low survival rate; when the predicted value is lower than the preset threshold value, the subject has a good treatment effect or a high survival rate.

[0051] In some preferred embodiments, when the miRNA marker consists of the following, the preset threshold is 0.235:

[0052] HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P and HSA-MIR-99A-5P. ,

[0053] In some preferred embodiments, when the miRNA marker consists of the following, the preset threshold is 0.480:

[0054] HSA-MIR-125A-5P, HSA-MIR-193A-5P, HSA-MIR-361-3P, HSA-MIR-483-3P, HSA-MIR-130B-5 P, HSA-MIR-223-5P, HSA-MIR-424-3P, HSA-MIR-542-3P, HSA-MIR-143-3P, HSA-MIR-320C, HS A-MIR-424-5P, HSA-MIR-582-3P, HSA-MIR-148A-3P, HSA-MIR-328-3P, HSA-MIR-4433B-5P, HSA-MIR-6803-3P, HSA-MIR-18B-3P, HSA-MIR-338-5P, HSA-MIR-450B-5P and HSA-MIR-744-5P.

[0055] In some embodiments, the predicted value is determined by the following steps:

[0056] (1) determining the expression levels of all miRNAs in any of the aforementioned miRNA markers (e.g., comprising 75 miRNA markers or comprising any 20 miRNA markers among the 75 miRNA markers) based on a sample from a subject;

[0057] (2) The expression levels of all miRNAs are trained by a machine learning model to obtain predicted values.

[0058] In some embodiments, the machine learning model includes, but is not limited to: a support vector machine algorithm and / or a leave-one-out cross-validation method.

[0059] In some embodiments, the calculation formula of the support vector machine algorithm is:

[0060]

[0061] Where w is the normal vector, b is the bias term, and x iis a vector that may contain k variables (e.g., 75 miRNA markers or the expression levels of all miRNAs of any 20 of the 75 miRNA markers), i is the predicted value.

[0062] In some embodiments, the model training comprises the following steps:

[0063] 1) Obtaining the expression levels (x) of all miRNAs in the miRNA markers (75 miRNA markers or any 20 miRNA markers among the 75 miRNA markers) of a sample (which may be a single sample or multiple samples) from the subject; i ), the predicted value (y i , each sample corresponds to a y i value);

[0064] 2) Draw a ROC curve based on the predicted value obtained in step 1), and determine a preset threshold based on the ROC curve.

[0065] In some embodiments, the preset threshold is based on the predicted value (y i ) is determined by the ROC curve drawn.

[0066] In some preferred embodiments, the determination of the preset threshold value includes:

[0067] A suitable threshold point is selected on the ROC curve and the area under the ROC curve is calculated.

[0068] A fourth aspect of the present invention provides a product for lung cancer diagnosis, risk assessment and / or prognosis prediction, wherein the product comprises a substance specific to any of the aforementioned miRNA markers.

[0069] In some embodiments, the product comprises a reagent or a kit.

[0070] In some embodiments, the substance includes a nucleic acid molecule that specifically recognizes and / or binds to the miRNA marker.

[0071] In some embodiments, the nucleic acid molecule includes, but is not limited to, DNA, RNA, nucleotide analogs, or a combination thereof.

[0072] In some preferred embodiments, the nucleic acid molecule includes, but is not limited to: a primer and / or a probe.

[0073] In some embodiments, the product also includes a device used in conjunction with the reagent or kit:

[0074] A device based on microarray detection, a device based on PCR detection, a device based on next-generation sequencing (NGS), or a combination thereof.

[0075] A fifth aspect of the present invention provides a method for screening cancer miRNA markers, the method comprising the following steps:

[0076] (1) The expression levels of all miRNAs were obtained based on samples from cancer patients and healthy subjects through high-throughput sequencing (or next-generation sequencing, NGS);

[0077] (2) determining the statistical test value by hypothesis testing according to the miRNA expression level;

[0078] (3) Obtaining the significance of the difference based on the statistical test value, and screening and obtaining the cancer miRNA marker according to the significance of the difference.

[0079] In some embodiments, the cancer includes lung cancer, colorectal cancer, liver cancer, ovarian cancer, pancreatic cancer, esophageal cancer, gastric cancer, gallbladder cancer, head and neck cancer, bile duct cancer, breast cancer, cervical cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, myeloid leukemia, lymphoid leukemia, melanoma, lymphoma, preferably lung cancer.

[0080] In some embodiments, the hypothesis testing method includes t-test, chi-square test, F-test, z-test, correlation coefficient or regression coefficient, preferably t-test.

[0081] In some embodiments, when the hypothesis test is performed by a t-test, the calculation formula of the statistical test value is as follows:

[0082]

[0083] in and is the average expression level of the miRNA in healthy people and cancer patients, n healthy and n camcer are the number of samples of healthy people and cancer patients, respectively, p is the mean of the variance of cancer patients and healthy people.

[0084] In some embodiments, s p The calculation of s can be p 2 Get p 2 The calculation formula is as follows:

[0085]

[0086] In some embodiments, the cancer miRNA markers obtained by screening are selected from the following miRNA group:

[0087] HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P, HSA-MIR-99A-5P. ,

[0088] In some preferred embodiments, the cancer miRNA markers obtained by screening are composed of the following:

[0089] HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P and HSA-MIR-99A-5P. ,

[0090] In some preferred embodiments, the miRNA marker consists of 75 or at least 70, at least 60, at least 50, at least 40, at least 30, at least 25, or at least 20 miRNAs.

[0091] In some preferred embodiments, the miRNA marker includes at least the following 20 miRNAs or consists of the following 20 miRNAs:

[0092] HSA-MIR-125A-5P, HSA-MIR-193A-5P, HSA-MIR-361-3P, HSA-MIR-483-3P, HSA-MIR-130B-5 P, HSA-MIR-223-5P, HSA-MIR-424-3P, HSA-MIR-542-3P, HSA-MIR-143-3P, HSA-MIR-320C, HS A-MIR-424-5P, HSA-MIR-582-3P, HSA-MIR-148A-3P, HSA-MIR-328-3P, HSA-MIR-4433B-5P, HSA-MIR-6803-3P, HSA-MIR-18B-3P, HSA-MIR-338-5P, HSA-MIR-450B-5P and HSA-MIR-744-5P.

[0093] The sixth aspect of the present invention provides a device comprising a model for lung cancer diagnosis, risk assessment and / or prognosis prediction, wherein the model is obtained by training a computer based on the expression levels of all miRNAs from any of the aforementioned miRNA markers in a sample from a subject.

[0094] In some embodiments, the training computer is implemented by a computer learning model.

[0095] In some preferred embodiments, the computer learning model includes, but is not limited to: a support vector machine algorithm and / or a leave-one-out cross-validation method.

[0096] In some preferred embodiments, the computer learning model calculates a predicted value.

[0097] In some preferred embodiments, the lung cancer diagnosis includes identifying and / or classifying subjects suffering from lung cancer and subjects not suffering from lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold, the subject is identified and / or classified as a subject suffering from lung cancer; when the predicted value is less than the preset threshold, the subject is identified and / or classified as a subject not suffering from lung cancer.

[0098] In some preferred embodiments, the risk assessment includes evaluating and / or rating the possibility of a subject who has not suffered from lung cancer suffering from lung cancer or progressing to lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold value, the subject is evaluated and / or rated as having a high possibility or high risk of suffering from lung cancer or progressing to lung cancer; when the predicted value is less than the preset threshold value, the subject is evaluated and / or rated as having a low possibility or low risk of suffering from lung cancer or progressing to lung cancer.

[0099] In some preferred embodiments, the prognosis prediction includes predicting and / or evaluating the treatment effect or survival rate of a subject who has received anti-lung cancer treatment and suffers from lung cancer, and the specific method is: obtaining the expression level of all miRNAs of any of the aforementioned miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 of the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold value, the subject has a poor treatment effect or a low survival rate; when the predicted value is lower than the preset threshold value, the subject has a good treatment effect or a high survival rate.

[0100] A seventh aspect of the present invention provides the use of any of the aforementioned miRNA markers in constructing a model for lung cancer diagnosis, risk assessment and / or prognosis prediction.

[0101] An eighth aspect of the present invention provides a method for constructing a model for lung cancer diagnosis, risk assessment and / or prognosis prediction, the method comprising the following steps:

[0102] 1) determining the expression levels of all miRNAs in the miRNA markers (including 75 miRNA markers or any 20 miRNA markers among the 75 miRNA markers) based on a sample from a subject;

[0103] 2) The expression levels of all miRNAs are trained by a machine learning model to obtain predicted values;

[0104] 3) Compare the predicted value with a preset threshold to obtain a result.

[0105] In some preferred embodiments, the computer learning model includes, but is not limited to: a support vector machine algorithm and / or a leave-one-out cross-validation method.

[0106] In some embodiments, the model training comprises the following steps:

[0107] 1) Obtain the expression levels (x) of all miRNAs in the miRNA markers (75 miRNA markers or any 20 miRNA markers among the 75 miRNA markers) of the sample (which can be a single sample or multiple samples) from the subject; i ), the predicted value (y i , each sample corresponds to a y i value);

[0108] 2) Draw a ROC curve based on the predicted value obtained in step 1), and determine a preset threshold based on the ROC curve.

[0109] In some embodiments, the preset threshold is determined by a ROC curve drawn based on the predicted value (yi).

[0110] In some preferred embodiments, the determination of the preset threshold value includes:

[0111] A suitable threshold point is selected on the ROC curve and the area under the ROC curve is calculated.

[0112] In some preferred embodiments, the lung cancer diagnosis includes identifying and / or classifying subjects suffering from lung cancer and subjects not suffering from lung cancer, wherein when the predicted value is greater than or equal to a preset threshold, the subject is identified and / or classified as a subject suffering from lung cancer; when the predicted value is less than the preset threshold, the subject is identified and / or classified as a subject not suffering from lung cancer.

[0113] In some preferred embodiments, the risk assessment includes evaluating and / or rating the likelihood of a subject who does not suffer from lung cancer suffering from lung cancer or progressing to lung cancer, wherein when the predicted value is greater than or equal to a preset threshold, the subject is evaluated and / or rated as having a high likelihood or high risk of suffering from lung cancer or progressing to lung cancer; when the predicted value is less than the preset threshold, the subject is evaluated and / or rated as having a low likelihood or low risk of suffering from lung cancer or progressing to lung cancer.

[0114] In some preferred embodiments, the prognosis prediction includes predicting and / or evaluating the treatment effect or survival rate of a subject who has received anti-lung cancer treatment and suffers from lung cancer, wherein when the predicted value is greater than or equal to a preset threshold, the subject has a poor treatment effect or a low survival rate; when the predicted value is lower than the preset threshold, the subject has a good treatment effect or a high survival rate.

[0115] A ninth aspect of the present invention provides a computer-readable storage medium, which includes a computer program, and when the computer program is run, it executes any of the aforementioned construction methods.

[0116] A tenth aspect of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement any of the aforementioned construction methods.

[0117] The eleventh aspect of the present invention provides a method for lung cancer diagnosis, risk assessment and / or prognosis prediction, the method comprising the following steps:

[0118] (i) determining the predictive value of a miRNA marker selected from the group consisting of the following miRNAs in a sample from a subject:

[0119] HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P, HSA-MIR-99A-5P;,

[0120] (ii) comparing the predicted value of the miRNA marker obtained in step (i) with a preset threshold;

[0121] (iii) interpreting the results of the comparison in step (ii) to achieve lung cancer diagnosis, risk assessment and / or prognosis prediction.

[0122] In some preferred embodiments, the miRNA markers are HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P, HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA-MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MIR-27A-3P, HSA-MIR-320A-3P, HSA-MIR-320B, HSA-MIR-320C, HSA-MIR-320D, HSA-MIR-328-3P, HSA-MIR-331-3P, HSA-MIR-338-3P, HSA-MIR-338-5P, HSA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424-5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483-5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA-MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3PHSA-MIR-629-5P, HSA-MIR-636, HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, H SA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P and HSA-MIR-99A-5P. ,

[0123] In some more preferred embodiments, the miRNA marker is HSA-MIR-125A-5P, HSA-MIR-193A-5P, HSA-MIR-361-3P, HSA-MIR-483-3P, HSA-MIR-130B-5P, HSA-MIR-223-5P, HSA-MIR-424-3P, HSA-MIR-542-3P, HSA-MIR-143-3P, HSA -MIR-320C, HSA-MIR-424-5P, HSA-MIR-582-3P, HSA-MIR-148A-3P, HSA-MIR-328-3P, HSA-MIR-443 3B-5P, HSA-MIR-6803-3P, HSA-MIR-18B-3P, HSA-MIR-338-5P, HSA-MIR-450B-5P and HSA-MIR-744-5P.

[0124] In some preferred embodiments, the sample includes, but is not limited to: blood, tissue, body fluid and / or excrement.

[0125] In some preferred embodiments, the blood includes whole blood and peripheral blood.

[0126] In some more preferred embodiments, the sample is peripheral blood.

[0127] In some embodiments, the lung cancer diagnosis includes identifying and / or classifying subjects suffering from lung cancer and subjects not suffering from lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in the miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 miRNA markers among the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold, the subject is identified and / or classified as a subject suffering from lung cancer; when the predicted value is less than the preset threshold, the subject is identified and / or classified as a subject not suffering from lung cancer.

[0128] In some embodiments, the risk assessment includes evaluating and / or rating the possibility of a subject who has not suffered from lung cancer suffering from lung cancer or progressing to lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in the miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 miRNA markers among the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold value, the subject is evaluated and / or rated as having a high possibility or high risk of suffering from lung cancer or progressing to lung cancer; when the predicted value is less than the preset threshold value, the subject is evaluated and / or rated as having a low possibility or low risk of suffering from lung cancer or progressing to lung cancer.

[0129] In some embodiments, the prognosis prediction includes predicting and / or evaluating the treatment effect or survival rate of a subject who has received anti-lung cancer treatment and suffers from lung cancer, and the specific method is: obtaining the expression levels of all miRNAs in the miRNA markers (for example, comprising 75 miRNA markers or comprising any 20 miRNA markers among the 75 miRNA markers) in the sample of the subject, and calculating a predicted value based on the expression levels of all miRNAs, when the predicted value is greater than or equal to a preset threshold value, the subject has a poor treatment effect or a low survival rate; when the predicted value is lower than the preset threshold value, the subject has a good treatment effect or a high survival rate.

[0130] In some embodiments, the predicted value is determined by:

[0131] i) determining the expression levels of all miRNAs in the miRNA marker based on a sample from the subject;

[0132] ii) The expression levels of all the miRNAs are trained by the machine learning model to obtain predicted values.

[0133] In some embodiments, the machine learning model includes, but is not limited to: a support vector machine algorithm and / or a leave-one-out cross-validation method.

[0134] In some embodiments, the calculation formula of the support vector machine algorithm is:

[0135]

[0136] Where w is the normal vector, b is the bias term, and x i is a vector that may contain k variables (e.g., 75 miRNA markers or the expression levels of any 20 of the 75 miRNA markers), i is the predicted value.

[0137] In some embodiments, the model training comprises the following steps:

[0138] 1) Obtain the expression levels (x) of all miRNAs in the miRNA markers (75 miRNA markers or any 20 miRNA markers among the 75 miRNA markers) of the sample (which can be a single sample or multiple samples) from the subject; i ), the predicted value (y i , each sample corresponds to a y i value);

[0139] 2) Draw a ROC curve based on the predicted value obtained in step 1), and determine a preset threshold based on the ROC curve.

[0140] In some embodiments, the preset threshold is based on the predicted value (y i ) is determined by the ROC curve drawn.

[0141] In some preferred embodiments, the determination of the preset threshold value includes:

[0142] A suitable threshold point is selected on the ROC curve and the area under the ROC curve is calculated.

[0143] The present invention is based on the microRNA expression level of peripheral blood of real people, and adopts methods such as hypothesis testing to find suitable miRNA markers to distinguish and screen lung cancer patients. The present invention discloses peripheral blood extracellular vesicle miRNA markers that can be used for early screening of lung cancer. Based on the peripheral blood of lung cancer patients and healthy people in the clinic as research samples, the selected miRNA markers have higher specificity and sensitivity in the diagnosis of lung cancer patients using NGS sequencing technology. The research results are of great significance for the screening and detection of lung cancer patients. In addition, the present invention also found that only any 20 miRNA markers out of 75 miRNA markers can be used for detection, which significantly saves the cost in clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0144] Figure 1 ROC curves of 75 miRNA markers selected based on the training cohort are shown.

[0145] Figure 2 Shown is the ROC curve of the training cohort using all miRNA markers (2611 miRNAs).

[0146] Figure 3 The mean and 99th quantile of AUC for different numbers of markers among the 75 miRNA markers are shown.

[0147] Figure 4 ROC curves for the 20 miRNAs with the highest algorithm scores in the training cohort are shown. DETAILED DESCRIPTION

[0148] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0149] The present invention may be implemented in other specific forms without departing from the basic attributes of the present invention. It should be understood that, without conflict, any and all embodiments of the present invention may be combined with the technical features in any other embodiment or multiple other embodiments to obtain other embodiments. The present invention includes other embodiments obtained by such combination.

[0150] All publications and patents mentioned in this disclosure are hereby incorporated by reference into this disclosure in their entirety. If the usage or terminology used in any publication or patent incorporated by reference conflicts with the usage or terminology used in this disclosure, the usage and terminology of this disclosure shall prevail.

[0151] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0152] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly used in the field to which the present invention belongs. For the purpose of interpreting this specification, the following definitions will apply, and where appropriate, terms used in the singular will also include the plural form, and vice versa.

[0153] Unless the context clearly dictates otherwise, the expressions "a", "an" and "an" as used herein include plural references. For example, reference to "a cell" includes a plurality of such cells and equivalents thereof known to those skilled in the art, and so forth.

[0154] As shown in the present disclosure, the term "about" means a range of ±20% of the value thereafter. In some embodiments, the term "about" means a range of ±10% of the value thereafter. In some embodiments, the term "about" means a range of ±5% of the value thereafter.

[0155] The numerical ranges used in the present disclosure should be understood to have listed all the numbers within the range. For example, a range of 1 to 20 should be understood to include any number, number combination or sub-range from the following group: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20. At least 20 should be understood to include any number, number combination or sub-range greater than or equal to 20: 20, 21, 22, 23, 24, 25, 30, 40, 50, ..., 75.

[0156] As used in this disclosure, the term "comprises" or "comprising" means "including but not limited to". The term is intended to be open-ended to specify the presence of any stated features, elements, integers, steps, or components, but does not exclude the presence or addition of one or more other features, elements, integers, steps, components, or groups thereof. Therefore, the term "comprising" includes the more restrictive terms "consisting of" and "consisting essentially of". In one embodiment, the term "comprising" used throughout the application, especially in the claims, may be replaced by the term "consisting of".

[0157] As used herein, the terms "optional", "either", "any", or "any" mean that the subsequently described event or circumstance may but need not occur, and the description includes instances where the event or circumstance occurs or does not occur. The terms "a" and "an" used herein are used herein to refer to one or more than one of the grammatical objects.

[0158] As shown in the present disclosure, “and / or” should be understood to mean any one of the optional items or a combination of any two or more of the optional items.

[0159] As shown in the present disclosure, the term "miRNA" or "microRNA" refers to a class of non-coding single-stranded RNA molecules with a length of 19-25nt encoded by endogenous genes. MicroRNA is partially complementary to one or more messenger RNA (mRNA) molecules, and its main function is to downregulate gene expression in various ways, including translation inhibition, mRNA shearing and deadenylation. The miRNA gene is replicated and transcribed in the cell, and the primary transcription product pri-miRNA with a length of 300-1000 bases is first formed during transcription. Pri-miRNA is cut into a hairpin structure precursor miRNA with a length of 70-90nt by the Drosha enzyme, called pre-miRNA, which is not a mature miRNA at this time and needs to be further processed in the cytoplasm to become a mature and functional miRNA.

[0160] As shown in the present disclosure, the term "lung cancer", which means primary bronchial lung cancer, is the most common primary lung malignancy originating from the trachea, bronchial mucosa or glands. Lung cancer refers to a disease in which cells in the lung tissue grow out of control. This cell growth may cause metastasis, that is, invasion of adjacent tissues and infiltration outside the lungs. In some instances, the lung cancer includes non-small cell lung cancer and small cell lung cancer.

[0161] As shown in the present disclosure, the term "risk" is understood as the possibility that a subject or patient will develop or achieve a certain disease outcome. In the context of the present invention, the term "risk" is not intended to carry any positive or negative connotations with respect to the patient's health, but only refers to the probability or possibility of a given event or condition occurring or forming.

[0162] As used in this disclosure, the term "detectable amount" refers to, for example, a determined level of expression of a nucleic acid of interest.

[0163] As shown in the present disclosure, the term "computer learning method" or "machine learning model" generally refers to a system or program instruction and / or a collection of data configured to implement an algorithm, process or mathematical model. In the present application, the algorithm, process or mathematical model can evaluate and provide a desired output based on a given input. In the present application, the parameters of the machine learning model may not be explicitly programmed, and in the traditional sense, the machine learning model may not be explicitly designed to follow specific rules in order to provide a desired output for a given input. For example, the use of the machine learning model may mean that the machine learning model and / or the data structure / a set of rules as a machine learning model are trained by a machine learning algorithm. Examples of discriminant functions that allow classification include, but are not limited to, discriminant functions defined by support vector machines (SVM), k-nearest-neighbors (kNN), (naive) Bayesian models, or segmentally defined functions, such as in subset discovery, decision trees, logical analysis of data (LAD), etc.

[0164] As used herein, the term "sample" or "biological sample" is a sample derived from or in contact with a biological organism. The biological sample includes, but is not limited to, cells, tissues, body fluids, biopsy specimens, blood, urine, saliva, sputum, plasma, serum, cell culture supernatant, and others. In some preferred embodiments, the sample is derived from peripheral blood.

[0165] As used herein, the term "probe" is a molecule or substance that is capable of specifically binding to or interacting with a particular biomolecule. The terms "primer," "primer pair," or "probe" shall have the conventional meaning of these terms as known to those skilled in the art of molecular biology. In preferred embodiments of the present invention, "primer," "primer pair," and "probe" refer to oligonucleotide or polynucleotide molecules having a specific sequence that is identical, complementary, a homolog of, or homologous to a target molecule or region of a target sequence to be detected or quantified, such that the primer, primer pair, or probe can specifically bind to a target molecule, including but not limited to a target nucleic acid, RNA, DNA, cDNA, gene, transcript, peptide, polypeptide, or protein to be detected or quantified. As understood herein, a primer can function as a probe by itself. As understood herein, a "probe" may also include, for example, a combination of a primer pair and an internally labeled probe, which is common in many commercial qPCR methods.

[0166] As shown in the present disclosure, the terms "next generation gene sequencing (NGS)", "high-throughput sequencing" or "second-generation gene sequencing" generally refer to the second-generation high-throughput sequencing technology and the higher-throughput sequencing methods developed thereafter. Next-generation sequencing platforms include, but are not limited to, existing sequencing platforms such as Illmina. With the continuous development of sequencing technology, those skilled in the art will appreciate that sequencing methods and devices of other methods can also be used for the present method. For example, second-generation gene sequencing can have the advantages of high sensitivity, high throughput, high sequencing depth, or low cost. According to the development history, influence, sequencing principles and technologies, there are mainly the following types: Massively Parallel Signature Sequencing (MPSS), polymerase cloning (Polony Sequencing), 454 pyrophosphate sequencing (454pyro sequencing), Illumina (Solexa) sequencing, ion semiconductor sequencing (Ion semiconductor sequencing), DNA nano-ball sequencing (DNA nano-bal sequencing), Complete Genomics' DNA nanoarray and combined probe anchor ligation sequencing method, etc. The second generation gene sequencing can make it possible to conduct a detailed and comprehensive analysis of the transcriptome and genome of a species, so it is also called deep sequencing. For example, the method of the present application can also be applied to first generation gene sequencing, second generation gene sequencing, third generation gene sequencing or single molecule sequencing (SMS).

[0167] As used herein, the term "array" refers to an arrangement of addressable locations on a device (e.g., a chip device). The number of locations can vary from a few to at least several hundred or several thousand. Each location represents an independent reaction site. Arrays include, but are not limited to, nucleic acid arrays, protein arrays, and antibody arrays. "Nucleic acid array" refers to an array containing nucleic acid probes (such as oligonucleotides, polynucleotides, or larger portions of genes). The nucleic acids on the array are preferably single-stranded. "Microarray" refers to a biochip or biological chip, i.e., a microarray having at least about 100 / cm 2 An array of areas of density containing discrete areas of immobilized probes.

[0168] As shown in the present disclosure, the term "PCR-based method" refers to a method comprising polymerase chain reaction PCR. This is a method for exponentially amplifying nucleic acids (e.g., DNA or RNA) by enzymatic replication in vitro using one, two or more primers. For RNA amplification, reverse transcription can be used as a first step. PCR-based methods include kinetic or quantitative PCR (qPCR), which are particularly suitable for analyzing expression levels. When it is achieved to determine the expression level, a PCR-based method can be used, for example, to detect the presence of a given mRNA, which is performed by (1) reverse transcribing a complete mRNA library (so-called transcriptome) into cDNA with the assistance of a reverse transcriptase, and (2) detecting the presence of a given cDNA with the assistance of corresponding primers. This method is generally referred to as reverse transcriptase PCR (RT-PCR). The term "PCR-based method" includes endpoint PCR applications and kinetic / real-time PCR techniques that use special fluorophores or intercalating dyes that emit a fluorescent signal as a function of the target being amplified and allow monitoring and quantification of the target. Quantitative methods can be absolute or relative to comparative internal standards based on an external standard curve.

[0169] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with embodiment. If the specific conditions are not specified in the embodiment, it is carried out according to the conditions of normal conditions or manufacturer's suggestion. If the manufacturer is not specified in all reagents or instruments, they are all conventional products that can be purchased commercially. In order to better illustrate the present invention, numerous specific details are provided in the specific embodiments below. The specific embodiments described herein are only used to explain the present invention and are not intended to constitute any limitation of the present invention. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention. Such structures and technologies are also described in the publication of many publications.

[0170] With respect to the expression distribution of miRNA in actual clinical practice, the technical solutions adopted by the present invention are mainly:

[0171] First, the detailed information of all 2611 miRNAs was obtained from miRBase (https: / / www.mirbase.org);

[0172] Then, based on the samples of clinical lung cancer patients and healthy people, the data of the training cohort and the validation cohort were randomly divided. Further, hypothesis testing methods such as t-test were used to screen and select 75 miRNAs with significant differences between healthy people and lung cancer people from 2611 miRNAs, which are miRNA markers for lung cancer, including but not limited to HSA-MIR-1260B, HSA-MIR-1290, HSA-MIR-199A-5P, HSA-MIR-328-3P, HSA-MIR-451A, HSA-MIR-582-5P, HSA-MIR-627-5P, HSA-MIR-654-3P, HSA-MIR-744-5P and other miRNAs;

[0173] Finally, the present invention analyzed the actual clinical lung cancer data, used a training cohort to establish the model and a validation cohort to verify the results, and the results showed that the 75 screened miRNA markers could effectively and relatively accurately predict the samples, and randomly selecting 20 miRNA markers from the 75 miRNA markers could achieve better sample discrimination ability on the basis of controlling the number of miRNA markers as much as possible.

[0174] Example 1: Screening for miRNA markers of lung cancer in peripheral blood

[0175] 1.1: Study cohort and clinical information

[0176] The present invention also incorporates 96 actual Chinese clinical samples to screen miRNA markers for lung cancer in peripheral blood in clinical scenarios. The actual clinical research data includes 33 healthy people, 20 stage I lung cancer patients, 18 stage II lung cancer patients and 25 stage III patients, and all samples were collected from different hospital centers in China. Each enrolled patient was given an accurate lung cancer diagnosis based on the results of pathological examination. Among the 96 samples, they were randomly divided into a training cohort and a validation cohort, with a total of 64 samples in the training cohort and a total of 32 samples in the validation cohort. Table 1 shows the clinical information of all enrolled samples, including gender, age, pathological diagnosis results and other related information. The analysis results show that there is no significant difference in the proportion of gender, age and pathological diagnosis between the two groups of samples.

[0177] Table 1. Clinical information of 96 samples

[0178]

[0179] 1.2: Screening of miRNA markers for lung cancer

[0180] Based on actual clinical samples, the present invention uses NGS sequencing technology to construct and sequence the RNA extracted from whole blood to analyze the expression of miRNA. Based on the expression of all miRNAs, the present invention uses a two-sample t-test hypothesis test method to calculate the statistical test value for samples from lung cancer patients and samples from healthy people. The formula is as follows:

[0181]

[0182] in and are the average expression levels of the miRNA in healthy people and lung cancer patients, n healthy and n cancer are the number of samples of healthy people and lung cancer patients, s p is the mean variance of lung cancer patients and healthy people. Assume that the sample standard deviations of healthy people and lung cancer patients are s healthy and cancer ,but:

[0183]

[0184] According to the test statistic, the p value of the difference between a certain miRNA in healthy people and lung cancer population is obtained, and the miRNA markers that can distinguish healthy people from lung cancer patients are screened in turn. In the actual clinical data of this time, a total of 2611 miRNAs entered the screening algorithm of miRNA markers, and finally 75 lung cancer miRNA markers were obtained as shown in Table 2.

[0185] Table 2. Summary of lung cancer miRNA markers

[0186]

[0187]

[0188]

[0189] Example 2: Construction of miRNA marker model for lung cancer screening

[0190] Based on the data of the training cohort provided in Example 1, a support vector machine (SVM) model was used, that is, for the expression level of each miRNA marker selected from healthy and cancer samples, assuming that the interval between cancer and healthy samples is:

[0191] wx+b=0;

[0192] Where w is the normal vector, b is the bias term, and x is the vector. Based on this calculation, the division model of healthy and cancer populations is obtained:

[0193]

[0194] Where yi is the predicted value. ⅰ is a vector that can contain k variables (e.g., the expression levels of 75 miRNA markers).

[0195] Using the Leave One Out Cross Validation (LOOCV) method, 75 selected miRNA markers were used in all samples of the training cohort to finally obtain the model parameters based on the training cohort.

[0196] Based on the data of the training cohort (data of 75 miRNAs), the receiver operating characteristic curve (ROC curve) was calculated and plotted. Through a comprehensive evaluation of the specificity and sensitivity values ​​of each point on the ROC curve, an appropriate value was comprehensively selected as the final judgment threshold. When the predicted value of the model is greater than or equal to the threshold, the model can be considered to have determined that the sample is a lung cancer patient; when the predicted value of the model is less than the threshold, the model can be considered to have determined that the sample is a healthy person.

[0197] In the actual clinical data of this study, a training cohort consisting of 64 samples was used. That is, the leave-one-out cross-validation method (LOOCV) and SVM model were used to calculate and plot the ROC curves at each numerical point for 75 miRNAs in the 64 samples of the training cohort ( Figure 1 ), select a suitable threshold point and calculate the area under the ROC curve (Area Under Curve, AUC value). The optimal threshold value based on the ROC curve is threshold = 0.235.

[0198] That is, when the model prediction value of a sample is greater than or equal to 0.235, it is judged as a cancer patient, otherwise, it is judged as a healthy person. Figure 1 It is calculated that under this threshold, this group of lung cancer miRNA biomarkers can predict the training cohort with a specificity of 0.955 and a sensitivity of 0.929. At the same time, the AUC value of the ROC curve is 0.965.

[0199] Example 3: Expression of lung cancer miRNA markers in clinical samples

[0200] In order to evaluate the lung cancer miRNA markers screened, the samples of the above validation cohort were selected for analysis and calculation. The threshold value of 0.235 selected in the training cohort was used to establish an SVM model for predictive evaluation of the expression levels of 75 miRNAs in the 32 validation cohorts, and its specificity and sensitivity were calculated:

[0201] Specificity = number of truly negative samples / number of truly negative samples + number of false positive samples,

[0202] Sensitivity = number of true positive samples / number of true positive samples + number of false negative samples.

[0203] The analysis results are shown in Table 3. The lung cancer miRNA markers obtained by the screening algorithm of the present invention not only have good performance in the training cohort, but also have a specificity of 0.818 (9 / 9+2) and a sensitivity of 0.714 (15 / 15+6) in the validation cohort. The evaluation and verification of actual clinical data once again proves that the lung cancer miRNA markers of the present invention are effective and accurate, and can distinguish between healthy people and sick people.

[0204] Table 3. Prediction results of the validation cohort based on the model established based on lung cancer miRNA markers

[0205]

[0206] Example 4: Performance comparison of different numbers of lung cancer miRNA markers

[0207] In the training cohort, all 2611 miRNAs were used as lung cancer miRNA markers. In the same training cohort of 64 samples, the specificity was 0.909 and the sensitivity was 0.929 (e.g. Figure 2 shown).

[0208] It can be seen that in the training cohort, the specificity and sensitivity of the 75 lung cancer miRNA markers screened out are basically equivalent to those of all 2611 miRNA markers, which can largely reflect the miRNA differences between lung cancer and healthy people, and provide a good basis for subsequent judgment.

[0209] Based on the 75 lung cancer miRNA markers screened above, this experiment used the random algorithm package in the python program and the Mersenne Twister algorithm to randomly select any 5 to 25 different numbers of miRNA markers from the 75 lung cancer miRNA markers. Similarly, the LOOCV and SVM models were used in the 64 samples of the training cohort to obtain the ROC curves under different numbers of markers and calculate the corresponding AUC values. Repeat and randomly perform this process 3000 times to obtain the AUC values ​​under different numbers of lung cancer miRNA biomarkers each time, and calculate the average and standard deviation of the 3000 repetitions (such as Figure 3 It was found that among the 75 miRNA markers, when the number of randomly selected miRNA markers reached 20, it could not only provide a basis for subsequent analysis with a relatively small number of markers, but also ensure the ability to distinguish between lung cancer and healthy people to the greatest extent possible.

[0210] In order to further illustrate the discriminative ability of 20 miRNA markers selected from 75 lung cancer miRNA markers, this experiment used the random algorithm package in the python program and the Mersenne Twister algorithm to randomly select 20 non-repeated samples and screen out the 20 miRNA markers with the highest scores for analysis. The specific miRNAs are shown in Table 4. The LOOCV and SVM models were used to finally obtain the ROC curve ( Figure 4 ), and its AUC was 0.971. Based on the evaluation of each point on the ROC, 0.480 was selected as the threshold for screening healthy people and lung cancer patients, and a prediction evaluation was performed on the validation cohort of 32 samples, and its specificity and sensitivity were calculated:

[0211] Specificity = number of truly negative samples / number of truly negative samples + number of false positive samples,

[0212] Sensitivity = number of true positive samples / number of true positive samples + number of false negative samples.

[0213] The final results showed that randomly selecting 20 of the 75 lung cancer miRNA markers, as shown in Table 5, also achieved good results of 0.818 specificity and 0.810 sensitivity in the evaluation of the validation cohort, which once again proved that these miRNA markers have good accuracy in distinguishing healthy people from lung cancer patients.

[0214] Table 4. Top 20 lung cancer miRNA markers

[0215] HSA-MIR-125A-5P HSA-MIR-130B-5P HSA-MIR-143-3P HSA-MIR-148A-3P HSA-MIR-18B-3P HSA-MIR-193A-5P HSA-MIR-223-5P HSA-MIR-320C HSA-MIR-328-3P HSA-MIR-338-5P HSA-MIR-361-3P HSA-MIR-424-3P HSA-MIR-424-5P HSA-MIR-4433B-5P HSA-MIR-450B-5P HSA-MIR-483-3P HSA-MIR-542-3P HSA-MIR-582-3P HSA-MIR-6803-3P HSA-MIR-744-5P

[0216] Table 5. Prediction results of the validation cohort using the model established with the top 20 miRNA markers

[0217]

[0218] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A miRNA marker for lung cancer diagnosis, risk assessment and / or prognosis prediction, characterized in that: The miRNA marker is selected from the group consisting of the following miRNAs: HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P, and HSA-MIR-99A-5P.

2. The miRNA marker according to claim 1, characterized in that The miRNA marker consists of 75 or at least 70, at least 60, at least 50, at least 40, at least 30, at least 25, or at least 20 miRNAs.

3. The miRNA marker according to claim 1 or 2, characterized in that The miRNA markers include at least the following 20 miRNAs or consist of the following 20 miRNAs: HSA-MIR-125A-5P, HSA-MIR-193A-5P, HSA-MIR-361-3P, HSA-MIR-483-3P, HSA-MIR-130B-5 P, HSA-MIR-223-5P, HSA-MIR-424-3P, HSA-MIR-542-3P, HSA-MIR-143-3P, HSA-MIR-320C, HS A-MIR-424-5P, HSA-MIR-582-3P, HSA-MIR-148A-3P, HSA-MIR-328-3P, HSA-MIR-4433B-5P, HSA-MIR-6803-3P, HSA-MIR-18B-3P, HSA-MIR-338-5P, HSA-MIR-450B-5P and HSA-MIR-744-5P.

4. Use of the miRNA marker according to any one of claims 1 to 3 in the preparation of a product for lung cancer diagnosis, risk assessment and / or prognosis prediction; Preferably, the product is used to detect samples from a subject to achieve lung cancer diagnosis, risk assessment and / or prognosis prediction; Preferably, the sample comprises blood, tissue, body fluid, excrement or a combination thereof; Preferably, the product comprises a reagent or a kit.

5. A product for lung cancer diagnosis, risk assessment and / or prognosis prediction, characterized in that: The product comprises a substance specific for the miRNA marker described in any one of claims 1 to 3; Preferably, the product comprises a reagent or a kit; Preferably, the substance comprises a nucleic acid molecule that specifically recognizes and / or binds to the miRNA marker; Preferably, the product also includes equipment used in conjunction with the reagent or kit: PCR detection-based devices, next-generation sequencing-based devices, or a combination thereof; More preferably, the nucleic acid molecule comprises DNA, RNA, nucleotide analogs or a combination thereof; More preferably, the nucleic acid molecule comprises a primer and / or a probe.

6. A method for screening cancer miRNA markers, characterized in that: The method comprises the following steps: (1) The expression levels of all miRNAs were obtained based on samples from cancer patients and healthy subjects through high-throughput sequencing; (2) determining a statistical test value by hypothesis testing based on the miRNA expression level; (3) Obtaining the significance of the difference based on the statistical test value, and screening and obtaining the cancer miRNA marker according to the significance of the difference.

7. The method according to claim 6, characterized in that The cancer includes lung cancer, colorectal cancer, liver cancer, ovarian cancer, pancreatic cancer, esophageal cancer, gastric cancer, gallbladder cancer, head and neck cancer, bile duct cancer, breast cancer, cervical cancer, endometrial cancer, kidney cancer, prostate cancer, thyroid cancer, myeloid leukemia, lymphoid leukemia, melanoma, lymphoma, preferably lung cancer; And / or, the hypothesis testing method includes t-test, chi-square test, z-test, correlation coefficient or regression coefficient, preferably t-test.

8. The method according to claim 7, characterized in that When the hypothesis test method is a t-test, the calculation formula of the statistical test value is as follows: in and is the average expression level of the miRNA in healthy people and cancer patients, n healthy and n cancer are the number of samples of healthy people and cancer patients, respectively, p is the mean of the variance of cancer patients and healthy people.

9. The method according to any one of claims 6 to 8, characterized in that: The cancer miRNA markers obtained by the screening include or consist of the following miRNAs: HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P, and HSA-MIR-99A-5P.

10. The method according to claim 9, characterized in that The cancer miRNA markers obtained by the screening are selected from the following miRNA group: HSA-LET-7D-3P, HSA-MIR-10401-3P, HSA-MIR-1229-3P, HSA-MIR-1249-3P, HSA-MIR-125A-5P, HSA-MIR-125B-5P, HSA-MIR-1260B, HSA-MIR-1277-5P HSA-MIR-1290, HSA-MIR-1306-5P, HSA-MIR-130B-5P, HSA-MIR-132-3P, HSA-MIR-142-3P, HSA-MIR-143-3P, HSA-MIR-145-3P, HSA-MIR-148A-3P, HSA- MIR-152-3P, HSA-MIR-18B-3P, HSA-MIR-193A-5P, HSA-MIR-199A-5P, HSA-MIR-199B-5P, HSA-MIR-202-5P, HSA-MIR-223-5P, HSA-MIR-26B-3P, HSA-MI R-27A-3P、HSA-MIR-320A-3P、HSA-MIR-320B、HSA-MIR-320C、HSA-MIR-320 D、HSA-MIR-328-3P、HSA-MIR-331-3P、HSA-MIR-338-3P、HSA-MIR-338-5P、H SA-MIR-361-3P, HSA-MIR-361-5P, HSA-MIR-3611, HSA-MIR-3615, HSA-MIR-365A-3P, HSA-MIR-365B-3P, HSA-MIR-421, HSA-MIR-424-3P, HSA-MIR-424 -5P, HSA-MIR-4433B-5P, HSA-MIR-450A-5P, HSA-MIR-450B-5P, HSA-MIR-451A, HSA-MIR-4685-3P, HSA-MIR-4772-5P, HSA-MIR-483-3P, HSA-MIR-483- 5P, HSA-MIR-503-5P, HSA-MIR-505-3P, HSA-MIR-532-3P, HSA-MIR-542-3P, HSA-MIR-551A, HSA-MIR-551B-3P, HSA-MIR-570-3P, HSA-MIR-576-3P, HSA -MIR-582-3P, HSA-MIR-582-5P, HSA-MIR-590-5P, HSA-MIR-618, HSA-MIR-625-3P, HSA-MIR-627-5P, HSA-MIR-628-3P, HSA-MIR-629-5P, HSA-MIR-636HSA-MIR-654-3P, HSA-MIR-660-5P, HSA-MIR-664A-3P, HSA-MIR-6803-3P, HSA-MIR-7-1-3P, HSA-MIR-744-5P, HSA-MIR-885-5P and HSA-MIR-99A-5P.

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