Application of blood extracellular vesicle miRNA combination as biomarker in early lung cancer diagnosis

Abstract

The invention discloses a biological marker for early diagnosis of lung cancer, and particularly discloses a biological marker for early diagnosis of lung cancer, which can be used for the blood small extracellular vesicles hsa-let-7d-3p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-miR-379-5p, hsa-miR-98-5p, hsa-miR-100-5p, hsa-miR-126-3p, hsa-miR-21-5p, hsa-miR-140-5p, hsa-miR-1908-5p, hsa-miR-328-3p, hsa-miR-486-5p and hsa-miR-99b-5p. The blood extracellular vesicle miRNA disclosed by the invention can be used for distinguishing early-stage lung cancer patients from non-cancer objects, in particular lung cancer high-risk people and lung positive nodule patients. The blood extracellular vesicle miRNA provided by the invention has better sensitivity and specificity, higher diagnosis specificity and fewer false positive results.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine. Specifically, the present invention relates to the early diagnosis of lung cancer, and in particular to the application of blood extracellular vesicle miRNA as a biomarker in distinguishing early lung cancer patients from high-risk groups and patients with positive lung nodules. Background Art

[0002] Lung cancer is known as the world's "number one cancer killer" due to its high incidence and mortality. In the past 20 years, the 5-year survival rate of lung cancer in China was only 19.7%, far lower than other tumors such as breast cancer (82.0%) and prostate cancer (66.4%). It is currently believed that the key factor affecting the 5-year survival rate of lung cancer is negatively correlated with the clinical stage at the time of diagnosis. Existing studies have confirmed that if lung cancer is discovered in the early stages, its 5-year survival rate can be close to 60%. In the late stages, it is only 5% to 6%. At the same time, the 5-year survival rate of "early-stage" lung cancer <1cm can reach 92%. However, the early diagnosis rate of lung cancer in China is only 19%. Timely lung cancer screening, especially screening among high-risk groups for lung cancer, can effectively improve the early diagnosis rate of lung cancer in clinical practice.

[0003] At present, in the lung cancer screening guidelines or consensus published worldwide, low-dose spiral CT (LDCT) is recommended as a screening method. This technology is simple, easy, highly sensitive, and highly patient-involved, but it is not perfect. There are three main potential hazards brought by LDCT. First, high false positives. In the LDCT group of the National Lung Screening Trial (NLST), the false positive rate was 96.4%, and most positive results disappeared during imaging review. Second, radiation hazards. Although the average radiation dose of LDCT is 0.61-1.50 mSv, which is much lower than the 7-8 mSv of conventional chest CT, it is estimated that 1 in every 108 cases of lung cancer found by screening is radiation-induced lung cancer. Third, overdiagnosis and overtreatment. For slow-growing or non-progressive lung cancer (indolent), imaging shows subsolid adenocarcinoma, which may be overdiagnosed, leading to unnecessary overtreatment, such as damage caused by surgery and psychological pressure on the crowd, affecting the quality of life of patients. Therefore, there is an urgent need to develop tumor markers with high specificity and sensitivity to assist in the early screening and diagnosis of lung cancer. Summary of the invention

[0004] The purpose of the present invention is to discover and verify highly effective blood small extracellular vesicle miRNA biomarkers and establish a highly effective diagnostic model, thereby improving the accuracy of early diagnosis of lung cancer in my country, reducing the false positive rate of lung cancer diagnosis, reducing the phenomenon of over-medicalization, alleviating the additional suffering of patients, and prolonging the survival time of lung cancer patients.

[0005] To this end, the present invention provides a biomarker that can be used clinically as a biomarker for early diagnosis of lung cancer, especially for distinguishing early lung cancer patients from high-risk groups and patients with positive lung nodules.

[0006] The present invention also provides a chip and a kit that can be used for early diagnosis of lung cancer, especially for distinguishing early lung cancer patients from high-risk groups and patients with positive lung nodules.

[0007] The present invention also provides a method for using the biomarker for early diagnosis of lung cancer, especially for distinguishing early lung cancer patients from high-risk groups and patients with positive lung nodules.

[0008] In a first aspect, the present invention provides use of blood extracellular vesicle miRNA in preparing a chip, a detection reagent or a detection kit for early diagnosis of lung cancer.

[0009] In a specific embodiment, the blood extracellular vesicle miRNA is:

[0010] (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or

[0011] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0012] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0013] (i) a combination of a miRNA having a sequence as shown in SEQ ID NO:1, a miRNA having a sequence as shown in SEQ ID NO:2, a miRNA having a sequence as shown in SEQ ID NO:3, a miRNA having a sequence as shown in SEQ ID NO:4, a miRNA having a sequence as shown in SEQ ID NO:5, a miRNA having a sequence as shown in SEQ ID NO:6, a miRNA having a sequence as shown in SEQ ID NO:7, and / or a miRNA having a sequence as shown in SEQ ID NO:8, a miRNA having a sequence as shown in SEQ ID NO:9, a miRNA having a sequence as shown in SEQ ID NO:10, a miRNA having a sequence as shown in SEQ ID NO:11, a miRNA having a sequence as shown in SEQ ID NO:12, and a miRNA having a sequence as shown in SEQ ID NO:13.

[0014] In a preferred embodiment, the miRNA is isolated from human.

[0015] In a preferred embodiment, the early diagnosis of lung cancer is to distinguish early lung cancer patients from non-cancer subjects.

[0016] In a preferred embodiment, the non-cancer subjects are people at high risk of lung cancer and patients with positive lung nodules.

[0017] In a second aspect, the present invention provides a miRNA chip, wherein the miRNA chip comprises:

[0018] a solid support; and

[0019] Oligonucleotide probes are sequentially fixed on the solid phase carrier, and the oligonucleotide probes specifically bind to miRNA;

[0020] Wherein, the miRNA is:

[0021] (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or

[0022] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0023] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0024] (i) a miRNA with a sequence as shown in SEQ ID NO:1, a miRNA with a sequence as shown in SEQ ID NO:2, a miRNA with a sequence as shown in SEQ ID NO:3, a miRNA with a sequence as shown in SEQ ID NO:4, a miRNA with a sequence as shown in SEQ ID NO:5, a miRNA with a sequence as shown in SEQ ID NO:6, a miRNA with a sequence as shown in SEQ ID NO:7, and / or a miRNA with a sequence as shown in SEQ ID NO:8, a miRNA with a sequence as shown in SEQ ID NO:9, a miRNA with a sequence as shown in SEQ ID NO:10, a miRNA with a sequence as shown in SEQ ID NO:11, a miRNA with a sequence as shown in SEQ ID NO:12, and a miRNA with a sequence as shown in SEQ ID NO:13.

[0025] In a preferred embodiment, the oligonucleotide probe comprises:

[0026] complementary binding region; and / or

[0027] A linker region connected to a solid support.

[0028] In a preferred embodiment, the miRNA chip is used for early diagnosis of lung cancer.

[0029] In a preferred embodiment, the early diagnosis of lung cancer is to distinguish early lung cancer patients from non-cancer subjects.

[0030] In a preferred embodiment, the non-cancer subjects are people at high risk of lung cancer and patients with positive lung nodules.

[0031] In a preferred embodiment, the miRNA is isolated from human.

[0032] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0033] In a third aspect, the present invention provides a use of the miRNA chip described in the second aspect for preparing an early diagnosis kit for lung cancer.

[0034] In a preferred embodiment, the early diagnosis of lung cancer is to distinguish early lung cancer patients from non-cancer subjects.

[0035] In a preferred embodiment, the non-cancer subjects are people at high risk of lung cancer and patients with positive lung nodules.

[0036] In a fourth aspect, the present invention provides a detection kit, wherein the detection kit contains a detection reagent for detecting miRNA;

[0037] Wherein, the miRNA is:

[0038] (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or

[0039] (ii) a miRNA complementary to the miRNA sequence described in (i)

[0040] Alternatively, the detection kit is equipped with the miRNA chip according to claim 3.

[0041] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0042] (i) a miRNA with a sequence as shown in SEQ ID NO:1, a miRNA with a sequence as shown in SEQ ID NO:2, a miRNA with a sequence as shown in SEQ ID NO:3, a miRNA with a sequence as shown in SEQ ID NO:4, a miRNA with a sequence as shown in SEQ ID NO:5, a miRNA with a sequence as shown in SEQ ID NO:6, a miRNA with a sequence as shown in SEQ ID NO:7, and / or a miRNA with a sequence as shown in SEQ ID NO:8, a miRNA with a sequence as shown in SEQ ID NO:9, a miRNA with a sequence as shown in SEQ ID NO:10, a miRNA with a sequence as shown in SEQ ID NO:11, a miRNA with a sequence as shown in SEQ ID NO:12, and a miRNA with a sequence as shown in SEQ ID NO:13.

[0043] In a preferred embodiment, the detection kit is used for early diagnosis of lung cancer.

[0044] In a preferred embodiment, the early diagnosis of lung cancer is to distinguish early lung cancer patients from non-cancer subjects.

[0045] In a preferred embodiment, the non-cancer subjects are people at high risk of lung cancer and patients with positive lung nodules.

[0046] In a preferred embodiment, the miRNA is isolated from human.

[0047] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0048] In a fifth aspect, the present invention provides a miRNA isolated from extracellular vesicles of blood for early diagnosis of lung cancer;

[0049] Wherein, the miRNA is:

[0050] (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or

[0051] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0052] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0053] (i) a miRNA with a sequence as shown in SEQ ID NO:1, a miRNA with a sequence as shown in SEQ ID NO:2, a miRNA with a sequence as shown in SEQ ID NO:3, a miRNA with a sequence as shown in SEQ ID NO:4, a miRNA with a sequence as shown in SEQ ID NO:5, a miRNA with a sequence as shown in SEQ ID NO:6, a miRNA with a sequence as shown in SEQ ID NO:7, and / or a miRNA with a sequence as shown in SEQ ID NO:8, a miRNA with a sequence as shown in SEQ ID NO:9, a miRNA with a sequence as shown in SEQ ID NO:10, a miRNA with a sequence as shown in SEQ ID NO:11, a miRNA with a sequence as shown in SEQ ID NO:12, and a miRNA with a sequence as shown in SEQ ID NO:13.

[0054] In a preferred embodiment, the miRNA is isolated from human.

[0055] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0056] In a preferred embodiment, the early diagnosis of lung cancer is between early lung cancer patients and non-cancer subjects.

[0057] In a preferred embodiment, the non-cancer subjects are people at high risk of lung cancer and patients with positive lung nodules.

[0058] In a sixth aspect, the present invention provides an isolated or artificially constructed precursor miRNA, which can be cleaved and expressed in human cells into the miRNA described in the fifth aspect.

[0059] In a seventh aspect, the present invention provides an isolated polynucleotide, which can be transcribed into a precursor miRNA by a human cell, and the precursor miRNA can be cleaved and expressed into the miRNA described in the fifth aspect in a human cell.

[0060] In a preferred embodiment, the polynucleotide has a structure shown in Formula I:

[0061] Seq 正向 -X-Seq 反向 Formula I

[0062] In Formula I,

[0063] Seq 正向 A nucleotide sequence that can be expressed as the miRNA in human cells;

[0064] Seq 反向 is a nucleotide sequence that is substantially complementary or completely complementary to Seq in the forward direction;

[0065] X is located in Seq 正向 and Seq 反向 and the spacer sequence is 正向 and Seq 反向 Not complementary;

[0066] After the structure shown in Formula I is transferred into human cells, a secondary structure shown in Formula II is formed:

[0067]

[0068] In Formula II, Seq 正向 , Seq 反向 and X is defined as above,

[0069] || means in Seq 正向 and Seq 反向 The base pairing relationship formed between them.

[0070] In an eighth aspect, the present invention provides a vector, wherein the vector contains the miRNA described in the fifth aspect or the polynucleotide described in the seventh aspect.

[0071] In a ninth aspect, the present invention provides a method for early diagnosis of lung cancer, comprising the steps of:

[0072] (a) detecting the expression level of miRNA in a sample of a test subject;

[0073] (b) inputting the miRNA expression value obtained in step (a) into the risk scoring model to obtain the probability that the subject is a lung cancer patient or a high-risk group for lung cancer or a patient with positive lung nodules;

[0074] (c) based on the probability obtained in step (b), providing a prediction result that the subject suffers from lung cancer;

[0075] Wherein, the miRNA is:

[0076] (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or

[0077] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0078] In a preferred embodiment, the sample is a peripheral blood sample.

[0079] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0080] (i) a miRNA with a sequence as shown in SEQ ID NO:1, a miRNA with a sequence as shown in SEQ ID NO:2, a miRNA with a sequence as shown in SEQ ID NO:3, a miRNA with a sequence as shown in SEQ ID NO:4, a miRNA with a sequence as shown in SEQ ID NO:5, a miRNA with a sequence as shown in SEQ ID NO:6, a miRNA with a sequence as shown in SEQ ID NO:7, and / or a miRNA with a sequence as shown in SEQ ID NO:8, a miRNA with a sequence as shown in SEQ ID NO:9, a miRNA with a sequence as shown in SEQ ID NO:10, a miRNA with a sequence as shown in SEQ ID NO:11, a miRNA with a sequence as shown in SEQ ID NO:12, and a miRNA with a sequence as shown in SEQ ID NO:13.

[0081] In a preferred embodiment, the miRNA is isolated from human.

[0082] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0083] In a preferred embodiment, the model formula of the risk prediction model in step (b) is as follows:

[0084]

[0085] Among them, x i represents the marker expression level of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents non-cancerous (high risk of lung cancer and positive lung nodules) and 1 represents lung cancer. The classification with the highest probability is taken as the final prediction result of sample i.

[0086] In a preferred embodiment, the risk prediction model is constructed using a machine learning algorithm; preferably, the machine learning algorithm is a linear model (LinearModel), random forest (random forest), extremely randomized trees (extremely randomized trees), neural network (neural network), gradient boosting machine (GBM) and ensemble methods (Ensemble methods).

[0087] In a preferred embodiment, the method has a sensitivity > 80% and a specificity > 78%.

[0088] In a preferred embodiment, the early diagnosis of lung cancer is to distinguish early lung cancer patients from non-cancer subjects.

[0089] In a preferred embodiment, the non-cancer subjects are people at high risk of lung cancer and patients with positive lung nodules.

[0090] In a tenth aspect, the present invention provides an early diagnosis device for lung cancer, the device comprising a storage device storing instructions for executing the method for early diagnosis of lung cancer according to the ninth aspect.

[0091] In a preferred embodiment, the device comprises the following means:

[0092] (a) a detection device, wherein the detection device detects the expression level of miRNA in a sample of a subject to be tested;

[0093] (b) a computing device, wherein the computing device inputs the miRNA expression value obtained by the device (a) into a risk scoring model to obtain the probability that the subject is a lung cancer patient or a high-risk group for lung cancer and a lung-positive nodule patient; and

[0094] (c) an output device, which outputs a prediction result that the subject suffers from lung cancer based on the probability obtained by device (b);

[0095] Wherein, the miRNA is:

[0096] (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or

[0097] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0098] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0099] (i) a miRNA with a sequence as shown in SEQ ID NO:1, a miRNA with a sequence as shown in SEQ ID NO:2, a miRNA with a sequence as shown in SEQ ID NO:3, a miRNA with a sequence as shown in SEQ ID NO:4, a miRNA with a sequence as shown in SEQ ID NO:5, a miRNA with a sequence as shown in SEQ ID NO:6, a miRNA with a sequence as shown in SEQ ID NO:7, and / or a miRNA with a sequence as shown in SEQ ID NO:8, a miRNA with a sequence as shown in SEQ ID NO:9, a miRNA with a sequence as shown in SEQ ID NO:10, a miRNA with a sequence as shown in SEQ ID NO:11, a miRNA with a sequence as shown in SEQ ID NO:12, and a miRNA with a sequence as shown in SEQ ID NO:13.

[0100] In a preferred embodiment, the miRNA is isolated from human.

[0101] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0102] In a preferred embodiment, the model formula of the risk prediction model described in device (b) is as follows:

[0103]

[0104] Among them, x i represents the marker expression level of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents non-cancerous (high risk of lung cancer and positive lung nodules) and 1 represents lung cancer. The classification with the highest probability is taken as the final prediction result of sample i.

[0105] In a preferred embodiment, the risk prediction model is constructed using a machine learning algorithm; preferably, the machine learning algorithm is a linear model (LinearModel), random forest (random forest), extremely randomized trees (extremely randomized trees), neural network (neural network), gradient boosting machine (GBM) and ensemble methods (Ensemble methods).

[0106] In a preferred embodiment, the early diagnosis of lung cancer is to distinguish early lung cancer patients from non-cancer subjects.

[0107] In a preferred embodiment, the non-cancer subjects are people at high risk of lung cancer and patients with positive lung nodules.

[0108] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a new or preferred technical solution. Due to space limitations, they will not be described one by one here. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 Transmission electron microscopic identification of small extracellular vesicles is shown;

[0110] Figure 2 The size distribution of small extracellular vesicles is shown;

[0111] Figure 3 The ROC curve for the training cohort is shown;

[0112] Figure 4 ROC curves for the validation cohort are shown. DETAILED DESCRIPTION

[0113] Lung cancer is a malignant tumor with high morbidity and mortality. However, the LDCT screening technology widely used in clinical practice has radiation hazards, and the high false positive rate for early diagnosis of lung cancer can easily lead to overdiagnosis and overtreatment. At present, there are no blood tumor biomarkers or models for distinguishing high-risk groups for lung cancer and patients with positive nodules from patients with early lung cancer.

[0114] In order to provide efficient biomarkers to assist in distinguishing lung cancer and non-lung cancer patients, the inventors have developed a liquid biopsy technology for the detection of blood small extracellular vesicle microRNA (miRNA) for early screening and diagnosis of lung cancer. Specifically, the inventors have carried out the following three parts of work:

[0115] (1) Study blood extracellular vesicle miRNA biomarkers for lung cancer diagnosis and build a highly accurate diagnostic model through statistical and machine learning methods;

[0116] (2) The cross-validation method and self-sampling method and multiple evaluation indicators were used to evaluate the effectiveness of the diagnostic model in distinguishing lung cancer from non-lung cancer in the training set and validation set, and to clarify and verify the accuracy, area under the receiver operating characteristic curve (AUC), positive predictive value, negative predictive value, sensitivity, and specificity of the model.

[0117] Through (1) and (2), we discovered and validated blood small extracellular vesicle miRNA biomarkers and their diagnostic models that can be used for early screening and differential diagnosis of lung cancer.

[0118] In the present invention, a content in the small extracellular vesicles of the blood, namely miRNA, is selected to construct a lung cancer and non-cancer diagnostic model. However, those skilled in the art know that there are many contents of small extracellular vesicles, such as lipids, mRNA and proteins, which can also be used as tumor biomarkers and achieve similar effects to miRNA. In addition, there are also various components in the patient's blood, such as lipids, proteins, ctDNA (Circulating tumor DNA), CTC (Circulating tumor cell), cytokines and other substances, which may be found as biomarkers for early diagnosis of lung cancer and developed into independent liquid biopsy diagnostic technology.

[0119] definition

[0120] The scientific and technical terms used herein are consistent with the conventional understanding of those skilled in the art. To facilitate understanding of the present invention, the relevant terms are explained and defined as follows:

[0121] Small extracellular vesicles

[0122] The terms "small extracellular vesicles (sEVs)", "small extracellular vesicles", "blood small extracellular vesicles" or "plasma small extracellular vesicles" used in this article have the same meaning and refer to a biologically active vesicle with a diameter of about 30-150nm secreted by multicellular bodies, also known as exosomes. The double lipid membrane structure of exosomes allows them to be preserved for a long time, thereby ensuring the integrity of their content microRNAs. Its stability and easy accessibility make it a promising auxiliary means for precision medicine, especially early tumor screening, diagnosis and treatment. Small RNAs in sEVs, including microRNAs (miRNAs), have been considered as potential biomarkers for the early diagnosis of various cancers. Exosomes secreted by lung cancer cells can reflect the biological characteristics of their parent cells and can provide specific microRNAs expression profiles associated with tumors.

[0123] People at high risk of lung cancer

[0124] The terms “high-risk population” or “high-risk population for lung cancer” or “high-risk population for lung cancer” used in this article have the same meaning, referring to a population with a relatively high incidence of lung cancer or a relatively high risk of developing lung cancer.

[0125] Specifically, according to the "Guidelines for Lung Cancer Screening, Early Diagnosis and Treatment in China 2021", the criteria for high-risk groups for lung cancer include: (1) Smoking: number of smoking pack-years ≥ 30 pack-years, including former smokers ≥ 30 years but less than 15 years since quitting smoking; (2) Passive smoking: living or working in the same room with smokers for ≥ 20 years; (3) Suffering from COPD; (4) History of occupational exposure (to asbestos, radon, beryllium, chromium, cadmium, nickel, silicon, coal smoke and coal dust) for at least 1 year; (5) Having a first-degree relative diagnosed with lung cancer.

[0126] Positive lung nodules

[0127] In the present invention, "positive lung nodules" and "positive lung nodules" have the same meaning, both of which refer to the presence of nodules in the lungs. The causes of nodules in the lungs are usually inflammation; tuberculosis; sarcoidosis; tumors; and other immune diseases.

[0128] Blood small extracellular vesicle miRNA of the present invention

[0129] At present, there are few studies based on the second-generation sequencing to identify the expression of blood small extracellular vesicle miRNA for distinguishing early lung cancer from non-cancer with high sensitivity and specificity. Some researchers (PMID: 23945385) detected 742 microRNAs in the exosomes of 10 lung adenocarcinomas, 10 lung granulomas and 10 healthy smokers, and selected 4 microRNA combinations (miR-378a, miR-379, miR-139-5p and miR-200-5p) that can be used to screen nodules and healthy people. The sensitivity and specificity were 97.5% and 72.0% respectively. A combination of six microRNAs (miR-151a-5p, miR-30a-3p, miR-200b-5p, miR-629, miR-100, and miR-154-3p) was further used to diagnose patients with nodules to distinguish between lung adenocarcinoma and lung granuloma, with a sensitivity of 96% and a specificity of 60%. However, the diagnostic model used in this study to distinguish between lung cancer and non-cancer was not effective, and other exosomal miRNA markers with better diagnostic effects could not be found due to chip-based or RT-PCR technology.

[0130] The inventors of CN 117106919 A ​​discovered, through extensive screening, exosomal miRNA markers that can be used for lung cancer detection and prognosis with high sensitivity and specificity. The markers include 8 miRNAs and corresponding multiple different combinations, including miR-27b, miR-328-5p, miR-152, miR-106a, miR-148a-3p, miR-140-5p, miR-146b and miR-142-5p. Seven of the miRNAs (miR-27b, miR-328-5p, miR-152, miR-106a, miR-148a-3p, miR-140-5p and miR-146b) were selected to construct a lung cancer exosomal miRNA detection model. The sensitivity of the diagnostic model in the training cohort and the validation cohort were 86.7% and 77.8%, respectively. The clinical application scenarios of this study are different, and it cannot be well applied to early lung cancer in high-risk groups and patients with positive nodules. At the same time, the diagnostic efficacy of early lung cancer is insufficient.

[0131] The above research cohorts do not consider the difference between the real high-risk population and the healthy population. No research on markers that can distinguish early lung cancer and high-risk populations for lung cancer has been reported.

[0132] The present invention uses the blood of patients suspected of lung cancer by various clinical detection methods as the research sample, and takes the high-risk group of lung cancer and patients with positive lung nodules as the control group. The small extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. is used to enrich plasma exosomes, and the second-generation sequencing technology small RNA sequencing is further used to detect the expression of plasma small extracellular vesicle miRNA in different groups. Finally, a plasma small extracellular vesicle miRNA biomarker diagnostic model that can be used for lung cancer diagnosis is discovered and verified in two independent cohorts, thereby discovering a group of plasma small extracellular vesicle miRNAs that can be used as high-efficiency biomarkers for lung cancer screening and early diagnosis.

[0133] In the present invention, 13 blood small extracellular vesicle miRNAs were found by statistical methods and machine learning methods, including hsa-let-7d-3p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-miR-379-5p, hsa-miR-98-5p, hsa-miR-100-5p, hsa-miR-126-3p, hsa-miR-21-5p, hsa-miR-140-5p, hsa-miR-1908-5p, hsa-miR-328-3p, hsa-miR-486-5p and hsa-miR-99b-5p. These blood small extracellular vesicle miRNAs can be used to establish a lung cancer risk prediction model with high specificity and high sensitivity. Finally, the inventors used another batch of independent data to verify the effect of the combination of 13 blood small extracellular vesicle miRNA biomarkers in distinguishing lung cancer and non-lung cancer populations.

[0134] In a specific embodiment, the blood extracellular vesicle miRNA of the present invention is a miRNA with a sequence as shown in SEQ ID NO: 1, a miRNA with a sequence as shown in SEQ ID NO: 2, a miRNA with a sequence as shown in SEQ ID NO: 3, a miRNA with a sequence as shown in SEQ ID NO: 4, a miRNA with a sequence as shown in SEQ ID NO: 5, a miRNA with a sequence as shown in SEQ ID NO: 6, a miRNA with a sequence as shown in SEQ ID NO: 7, and / or a miRNA with a sequence as shown in SEQ ID NO: 8, a miRNA with a sequence as shown in SEQ ID NO: 9, a miRNA with a sequence as shown in SEQ ID NO: 10, a miRNA with a sequence as shown in SEQ ID NO: 11, a miRNA with a sequence as shown in SEQ ID NO: 12, and / or a miRNA with a sequence as shown in SEQ ID NO: 13; or a miRNA complementary to the miRNA sequence described in (i).

[0135] In a preferred embodiment, the blood extracellular vesicle miRNA is a miRNA with a sequence as shown in SEQ ID NO: 1, a miRNA with a sequence as shown in SEQ ID NO: 2, a miRNA with a sequence as shown in SEQ ID NO: 3, a miRNA with a sequence as shown in SEQ ID NO: 4, a miRNA with a sequence as shown in SEQ ID NO: 5, a miRNA with a sequence as shown in SEQ ID NO: 6, a miRNA with a sequence as shown in SEQ ID NO: 7, and / or a miRNA with a sequence as shown in SEQ ID NO: 8, a miRNA with a sequence as shown in SEQ ID NO: 9, a miRNA with a sequence as shown in SEQ ID NO: 10, a miRNA with a sequence as shown in SEQ ID NO: 11, a miRNA with a sequence as shown in SEQ ID NO: 12, and a combination of a miRNA with a sequence as shown in SEQ ID NO: 13.

[0136] Based on the teachings of the present invention, those skilled in the art can understand that the blood extracellular vesicle miRNA of the present invention can be used for the early diagnosis of lung cancer, especially to distinguish early lung cancer patients from non-cancer subjects. In a specific embodiment, the non-cancer subjects are high-risk groups for lung cancer and patients with positive lung nodules. Therefore, the blood extracellular vesicle miRNA of the present invention can be used to prepare miRNA chips, detection reagents or detection kits for early diagnosis of lung cancer, especially to distinguish early lung cancer patients from non-cancer subjects (including high-risk groups for lung cancer and patients with positive lung nodules).

[0137] In a specific embodiment, the miRNA chip of the present invention comprises: a solid phase carrier; and oligonucleotide probes fixed in order on the solid phase carrier, wherein the oligonucleotide probes specifically bind to the blood extracellular vesicle miRNA of the present invention. Based on conventional technical means in the art, those skilled in the art know how to design the oligonucleotide probes. For example, the oligonucleotide probes contain: a complementary binding region; and / or a connection region connected to the solid phase carrier.

[0138] In a specific embodiment, the detection kit contains a detection reagent for detecting the miRNA of the present invention; or, the miRNA chip.

[0139] Based on the teachings of the present invention and the common knowledge in the field of miRNA, those skilled in the art know how to construct the miRNA of the present invention into an isolated or artificially constructed precursor miRNA, which can be cleaved and expressed in human cells as the miRNA of the present invention. Further, those skilled in the art can also know how to construct the precursor miRNA into an isolated polynucleotide, which can be transcribed into the precursor miRNA by human cells, and the precursor miRNA can be cleaved and expressed in human cells as the miRNA of the present invention.

[0140] In a specific embodiment, the polynucleotide has a structure shown in Formula I:

[0141] Seq 正向 -X-Seq 反向 Formula I

[0142] In Formula I,

[0143] Seq 正向 is a nucleotide sequence that can be expressed as the miRNA in human cells; Seq 反向 is a nucleotide sequence substantially complementary or completely complementary to Seq; X is a nucleotide sequence located at Seq 正向 and Seq 反向 and the spacer sequence is 正向 and Seq 反向 Not complementary;

[0144] After the structure shown in Formula I is transferred into human cells, a secondary structure shown in Formula II is formed:

[0145]

[0146] In Formula II, Seq 正向 , Seq 反向 and X are defined as above, || means in Seq 正向 and Seq反向 The base pairing relationship formed between them.

[0147] Based on the teaching of the present invention and the common knowledge in the field of miRNA, those skilled in the art will construct a vector containing the miRNA or polynucleotide of the present invention.

[0148] Method of the present invention and device for implementing the method of the present invention

[0149] The blood extracellular vesicle miRNA of the present invention can be used for early diagnosis of lung cancer, especially to distinguish early lung cancer patients from non-cancer subjects (such as high-risk populations for lung cancer and patients with positive lung nodules). Therefore, the present invention also provides a method for early diagnosis of lung cancer, comprising the steps of:

[0150] (a) detecting the expression level of miRNA in a sample of a test subject;

[0151] (b) inputting the miRNA expression value obtained in step (a) into the risk scoring model to obtain the probability that the subject is a lung cancer patient or a high-risk group for lung cancer or a patient with positive lung nodules;

[0152] (c) Based on the probability obtained in step (b), a prediction result is given that the subject suffers from lung cancer.

[0153] On the basis of understanding the technical content of the present invention, that is, specific blood extracellular vesicle miRNA can be used to distinguish early lung cancer patients from non-cancer subjects (such as high-risk lung cancer populations and patients with positive lung nodules), those skilled in the art can use various methods, such as various algorithms to build risk prediction models. In a specific embodiment, the risk prediction model is constructed using a machine learning algorithm; preferably, the machine learning algorithm is a linear model (LinearModel), a random forest (random forest), an extreme randomized tree (extremely randomized trees), a neural network (neuralnetwork), a gradient boosting machine (GBM) and an ensemble model (Ensemble methods).

[0154] In a specific implementation, the model formula of the risk prediction model in step (b) is as follows:

[0155]

[0156] Among them, x i represents the marker expression level of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents non-cancerous (high risk of lung cancer and positive lung nodules) and 1 represents lung cancer. The classification with the highest probability is taken as the final prediction result of sample i.

[0157] The method of the present invention for distinguishing early-stage lung cancer patients from non-cancer subjects has a sensitivity of >80% and a specificity of >78%.

[0158] To facilitate the implementation of the method of the present invention for distinguishing early lung cancer patients from non-cancer subjects, the present invention also provides an early diagnosis device for lung cancer, the device having a storage device storing instructions for executing the early diagnosis method for lung cancer of the present invention.

[0159] In a specific embodiment, the device includes a detection device for detecting the expression level of miRNA in a sample of a subject to be tested; a calculation device for inputting the obtained miRNA expression value into a risk scoring model to obtain the probability that the subject is a lung cancer patient or a high-risk group for lung cancer and a patient with positive lung nodules; and an output device for giving a prediction result that the subject suffers from lung cancer based on the obtained probability.

[0160] Advantages of the present invention:

[0161] 1. The miRNA of the present invention can be used to distinguish high-risk groups for lung cancer and patients with positive nodules from patients with early lung cancer;

[0162] 2. Compared with existing tumor biomarkers, the miRNA of the present invention has better sensitivity and specificity;

[0163] 3. Compared with LDCT screening technology, the method of the present invention has higher diagnostic specificity and fewer false positives.

[0164] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The experimental methods in the following examples where specific conditions are not specified are usually performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 2001), or under conditions recommended by the manufacturer. Unless otherwise stated, percentages and parts are calculated by weight.

[0165] Example

[0166] Material

[0167] The materials used in the following examples are all commercially available.

[0168] method

[0169] 1. Study cohort and clinical information

[0170] A total of 178 subjects were included in this study, including 71 patients with early lung cancer, 77 high-risk lung cancer patients, and 30 patients with positive lung nodules. Samples were collected from lung cancer patients before surgery. People at high risk of lung cancer are those who meet the following conditions through questionnaire screening, and blood samples are collected after informed consent. Criteria for high-risk lung cancer population (Guidelines for Screening, Early Diagnosis and Treatment of Lung Cancer in China 2021): (1) Smoking: ≥30 pack-years of smoking, including former smokers for ≥30 years, but less than 15 years of quitting smoking. (2) Passive smoking: Living or working in the same room with smokers for ≥20 years. (3) Suffering from COPD. (4) History of occupational exposure (asbestos, radon, beryllium, chromium, cadmium, nickel, silicon, coal smoke and coal smoke dust) for at least 1 year. (5) A first-degree relative diagnosed with lung cancer.

[0171] 30% of the samples were randomly selected as the validation cohort, and the rest were used as the training cohort. The training cohort included 49 lung cancer patients, 54 high-risk lung cancer patients, and 21 patients with positive lung nodules. The validation cohort included 22 lung cancer patients, 23 high-risk lung cancer patients, and 9 patients with positive lung nodules. The high-risk lung cancer patients and patients with positive lung nodules were collectively referred to as the non-cancer population.

[0172] 2. Extraction and Characterization of Blood Small Extracellular Vesicles

[0173] 1) Blood Collection and Extraction of Small Extracellular Vesicles

[0174] Blood samples from non-cancer people and lung cancer patients included in this study were separated into plasma samples by a two-step centrifugation method. First, the blood collection tube was placed in a pre-cooled centrifuge at 1600g and centrifuged at 4°C for 10 minutes, and the supernatant was transferred to a new 1.5ml centrifuge tube. The hemolysis level of the sample was graded and recorded, and samples with a level of less than or equal to 4 were subjected to subsequent experiments. Secondly, the supernatant from the previous step was centrifuged again at 16000g and 4°C for 10 minutes, and finally the supernatant was divided into 1ml per tube and stored in a negative 80 degree refrigerator. The steps for extracting small extracellular vesicles are as follows: take out the plasma sample and incubate it in a 37℃ water bath until it is completely thawed, centrifuge it at 12000g, 4℃ for 10min, transfer the supernatant to a 1.5ml centrifuge tube and add 1 / 4 volume of exosome precipitant (L3525, 3DMed, Shanghai, China) to mix, incubate it at 4℃ for 30min, then centrifuge it at 4700g, 4℃ for 30min, discard the supernatant after centrifugation, and finally add 200μl PBS (phosphate buffer saline) to suspend the small extracellular vesicle precipitate.

[0175] The present invention uses the small extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Siludi Biomedical Technology Co., Ltd. to extract plasma small extracellular vesicles. Based on the teachings of the present invention and conventional technical means in the prior art, those skilled in the art know that reagents from other manufacturers can also be used.

[0176] Those skilled in the art may also use other separation methods to extract extracellular vesicles, including but not limited to: a. ultrafiltration centrifugation; b. ultracentrifugation; c. microfluidics; d. magnetic bead capture; preferably other commercial exosome extraction kits.

[0177] 2) Characteristics of blood small extracellular vesicles

[0178] In order to detect the characteristics of small extracellular vesicles in the blood of lung cancer and non-cancer people, the present invention uses a transmission electron microscope to detect the morphology of small extracellular vesicles, and uses a nanoparticle tracking analyzer (NTA) to detect the particle size distribution of small extracellular vesicles. Identification of small extracellular vesicle morphological characteristics: First, the separated small extracellular vesicles are resuspended with PBS, and 4% paraformaldehyde is added to fix the small extracellular vesicles, and then the small extracellular vesicles are transferred to a carbon-coated 200-mesh electron microscope copper grid. The copper grid is first washed twice with PBS, and then the copper grid is washed with a fresh PBS containing glycine (50mM) for 3 minutes, and then the copper grid is washed again with a fresh PBS containing 0.5% BSA for 10 minutes, and finally the copper grid is stained with 2% uranyl acetate. After staining, the morphology of small extracellular vesicles is observed using a transmission electron microscope (H-7650, Hitachi High-Technologies, Japan).

[0179] Detection of the particle size distribution of small extracellular vesicles: First, take the separated small extracellular vesicles and dilute them with PBS to 1*10^ 7 -1*10^ 9 / ml. Subsequently, NTA (NanoSight NS300, Malvern, UK) was turned on to inject 1ml of the diluted sample into the sample chamber, and the 488nm excitation module was used. The lens parameters of the camera were set, the shutter value was selected as 890, the gain value was selected as 146, and the detection threshold was set to 7. At least 200 complete tracks were analyzed and obtained for each video. Finally, the nanoparticle tracking data of small extracellular vesicles were analyzed using NTA analysis software (version 2.3).

[0180] 3. Extraction and expression of blood small extracellular vesicle miRNA

[0181] 1) Extraction of miRNA from blood extracellular vesicles

[0182] The miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai, China) was used to separate blood extracellular vesicle miRNAs. The specific experimental steps were in accordance with the product manual. Subsequently, the Agilent 2100 analyzer and the matching chip (5067-1548, Agilent, USA) were used to perform miRNA quantification and fragment distribution detection.

[0183] 2) Expression detection of blood small extracellular vesicles

[0184] The present invention uses small RNA sequencing to detect the expression level of blood small extracellular vesicle miRNA. The library construction uses the NEBNext, Multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA) kit, and the specific experimental operation steps are in accordance with the product manual. The amount of miRNA loaded in each plasma sample is 100 ng, and the total volume does not exceed 6 μl. The library construction is completed through the steps of connecting 3' adapters, hybridizing reverse transcription primers, connecting 5' adapters, reverse transcription, PCR amplification, etc. Subsequently, the NucleoSpin Gel and PCR Clean-up (740609.50, MACHEREY-NAGEL, Germany) kit is used to purify the PCR product, and finally the DNA is eluted with 30 μl NE buffer. The DNA library quality control uses the Agilent 2100 bioanalyzer and the matching chips and reagents Agilent High Sensitivity DNA Kit & Reagents (5067-4626, Agilent, USA). The DNA was sequenced using the Illumina Nova 6000 platform (sequencing strategy: PE150).

[0185] In the present invention, the second generation sequencing technology, small RNA sequencing, is used to detect the expression of blood small extracellular vesicle miRNA. Based on the teachings of the present invention and conventional technical means in the prior art, those skilled in the art know other alternative methods, including but not limited to: a. other second generation sequencing methods; b. third generation sequencing methods; c. chip detection; dQ-PCR detection, etc.

[0186] 4. Sequencing data analysis process

[0187] Based on small RNA sequencing detection technology, the expression level of miRNA in small extracellular vesicles in the patient's blood was obtained. The analysis process of sequencing data is as follows:

[0188] 1) Sequencing data alignment. After removing the sequencing adapters of the small RNA sequencing data, the sequencing data were aligned to the human reference genome hg19 (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ) using BWA software (version: 0.7.12-r1039), and the number of reads aligned to miRNA was counted.

[0189] 2) miRNA annotation. The miRNAs were annotated using the Gencode v25 and miRBase v21 databases, and those annotated as known mature miRNAs were retained for subsequent analysis.

[0190] 3) miRNA filtering. For the training cohort, mature miRNAs with a length of less than or equal to 30 nt and covered by at least g samples in the training cohort data with no less than 10 reads were retained for subsequent analysis, where g is the minimum number of samples in the group after the cohort was grouped; for the validation cohort, miRNAs screened by the training cohort were retained for subsequent analysis.

[0191] 4) Normalization of miRNA expression. The original expression of miRNA in the training cohort samples was normalized using the trimmed mean of M-values ​​(TMM) method, and the same parameters were used to process the validation cohort samples.

[0192] 5. Discovery of biomarkers

[0193] Based on the expression of miRNAs in the training cohort, the samples were grouped according to the pathological test results, and statistical and machine learning methods were used to discover miRNAs that can be used to distinguish non-cancer people from lung cancer patients as biomarkers. The process is as follows:

[0194] 1) Grouping of training cohort: Based on the results of sample pathology tests, the patient samples in the training cohort were divided into two groups: lung cancer and non-cancer groups.

[0195] 2) Statistical methods for screening biomarkers. First, the Kruskal-Wallis H test and Conover test were used to analyze the expression differences of all miRNAs between the two groups. At the same time, the mutual information (MI) between all miRNA expressions and sample groups was calculated, and miRNAs with inter-group statistical differences p value <= 0.1 and mutual information scores in the top 10% of all markers were screened. Then, the correlation coefficients between these miRNAs were calculated, and only the miRNAs with the largest variance were retained as candidate biomarkers among the miRNAs with correlation coefficients > 0.8 for subsequent analysis.

[0196] 3) Screening biomarkers using machine learning methods. The miRNAs screened in 2) were used as initial markers and first screened using three machine learning feature screening methods. Method 1: Evaluate the classification performance of each marker on the sample separately, and mark the markers with a performance score <0.8 as candidate discarded markers; Method 2: First, take all the initial markers as a whole to evaluate the classification performance of the sample, then randomly shuffle the expression of the markers in the sample, calculate the degree of reduction in the classification performance score after the expression of each marker was shuffled, and mark the markers with a performance score reduction of <0.01 as candidate discarded markers; Method 3: First, take all the initial markers as a whole to evaluate the classification performance of the sample, and sort the markers by marker importance, then add one marker at a time starting from the marker with the highest importance and evaluate the improvement of the model score, and mark the markers with a score improvement of <0.01 as candidate discarded markers. The intersection markers of the candidate discarded markers obtained by the above three methods were discarded to obtain the remaining 13 markers. Subsequently, a total of 6 machine learning algorithms, including linear model, random forest, extremely randomized trees, neural network, gradient boosting machine (GBM) and ensemble methods, were used to train and evaluate the classification effect and feature weight of the 13 markers as a whole on the training set data, and markers with feature weights <5% were marked as candidate discarded markers. If a marker was marked as discarded by all algorithms, it was not retained. Combining the above methods, 13 markers were obtained as candidate markers for subsequent analysis.

[0197] In addition to the above-mentioned model construction method, based on the teachings of the present invention and conventional technical means in the prior art, those skilled in the art may also adopt other construction methods, including but not limited to: a. support vector machine; b. deep learning; c. complex feature engineering.

[0198] 6. Construction of risk scoring model for lung cancer and non-cancer populations

[0199] Taking lung cancer and non-cancer populations as the classification prediction targets, the 13 markers discovered in (5) were used, and a total of 6 machine learning algorithms were adopted, including linear model, random forest, extremely randomized trees, neural network, gradient boosting machine (GBM) and ensemble methods. Different hyper-parameters were preset for each algorithm, and the diagnostic models of multiple different machine learning algorithms were trained using the training set sample data. The model formula is as follows:

[0200]

[0201] x i represents the marker expression level of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents non-cancerous (high risk of lung cancer and positive lung nodules) and 1 represents lung cancer. The classification with the highest probability is taken as the final prediction result of sample i. The trained model is saved in the form of a file on the hard disk. When calling the model, the sample marker expression value is input to obtain the model prediction result.

[0202] 7. Evaluation and Validation of Risk Scoring Model Performance

[0203] In the training set, the classification effect and feature weight of the model were evaluated using the 5-fold cross-validation method, and the classification effect and feature weight of the model were evaluated using the independent validation set. The model evaluation and validation indicators included accuracy (range 0-1), area under the receiver operating characteristic curve (AUC, range 0-1), positive predictive value (range 0-1), and negative predictive value (range 0-1). The two types of samples, high-risk lung cancer and positive lung nodules, were integrated into the non-cancerous category to evaluate specificity (range 0-1) and sensitivity (range 0-1). The higher the value, the better the model classification performance.

[0204] 8. Application of lung cancer and non-cancer risk scoring models

[0205] 1) Collect peripheral blood from patients suspected of lung cancer in clinical diagnosis, obtain peripheral blood small extracellular vesicles, and use small RNA sequencing to obtain the expression of biomarkers;

[0206] 2) Using the risk scoring model, the expression values ​​of blood extracellular vesicles hsa-let-7d-3p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-miR-379-5p, hsa-miR-98-5p, hsa-miR-100-5p, hsa-miR-126-3p, hsa-miR-21-5p, hsa-miR-140-5p, hsa-miR-1908-5p, hsa-miR-328-3p, hsa-miR-486-5p and hsa-miR-99b-5p were obtained, and the values ​​were substituted into the trained model formula to calculate the probability of each patient being predicted to have lung cancer;

[0207] 3) Select the output result with the highest probability and give the predicted result of lung cancer risk for each patient.

[0208] Example 1.

[0209] In this embodiment, blood samples from 178 patients were collected, 30% of the samples were randomly selected as the validation cohort, and the rest were used as the training cohort. The training cohort consisted of 124 cases, including 49 lung cancer patients and 75 non-cancer people. Further pathological diagnosis of the non-cancer population included 54 patients at high risk of lung cancer and 21 patients with positive lung nodules. The validation cohort consisted of 54 cases, including 22 lung cancer patients and 32 non-cancer patients, specifically 23 patients at high risk of lung cancer and 9 patients with positive lung nodules. Table 1 shows the clinical information of the patients. The analysis results show that there is no significant difference in the proportion of samples of different classifications between the two groups of patients.

[0210] Table 1. Clinical information of non-cancer population and lung cancer patients.

[0211]

[0212] Example 2. Extraction and characterization of small extracellular vesicles from blood

[0213] In this example, the small extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Siludi Biomedical Technology Co., Ltd. was used to extract small extracellular vesicles in the plasma of lung cancer and non-cancer people. In order to detect the characteristic distribution of small extracellular vesicles in the blood of lung cancer and non-cancer people, the present invention uses transmission electron microscopy to detect the morphology of small extracellular vesicles and uses NTA to detect the particle size distribution of small extracellular vesicles. The transmission electron microscopy results show that the small extracellular vesicles have a typical "horseshoe-shaped" morphology (see Figure 1 The NTA test results show that the average particle size of the small extracellular vesicles in the representative samples extracted in this patent is 121.4nm, which is consistent with the particle size distribution of small extracellular vesicles (see Figure 2 ).

[0214] Example 3. Biomarker Discovery

[0215] In this example, small RNA sequencing was used to detect the expression levels of blood small extracellular vesicle miRNAs in lung cancer patients and non-cancer people. The statistical methods U test and T test were used to analyze the expression differences of all miRNAs between lung cancer patients and those at high risk of lung cancer and those with positive lung nodules, as well as the correlation coefficients between miRNAs. At the same time, three machine learning feature screening methods were used to screen miRNAs that can be used to distinguish lung cancer patients from those at high risk of lung cancer and those with positive lung nodules as biomarkers. The candidate molecular markers included hsa-let-7d-3p (CUAUACGACCUGCUGCCUUUCU, SEQ ID NO: 1), hsa-let-7e-5p (UGAGGUAGGAGGUUGUAUAGUU, SEQ ID NO: 2), hsa-let-7f-5p (UGAGGUAGUAGAUUGUAUAGUU, SEQ ID NO: 3), hsa-miR-379-5p (UGGUAGACUAUGGAACGUAGG, SEQ ID NO: 4), hsa-miR-98-5p (UGAGGUAGUAAGUUGUAUUGUU, SEQ ID NO: 5), and hsa-miR-106-5p (UGAGGUAGUAAGUUGUAUUGUU, SEQ ID NO: 6). NO: 5), hsa-miR-100-5p (AACCCGUAGAUCCGAACUUGUG, SEQ ID NO: 6), hsa-miR-126-3p (UCGUACCGUGAGUAAAUAAUGCG, SEQ ID NO: 7), hsa-miR-21-5p (UAGCUUAUCAGACUGAUGUUGA, SEQ ID NO: 8), hsa-miR-140-5p (CAGUGGUUUUACCCUAUGGUAG, SEQ ID NO: 9), hsa-miR-1908-5p (CGGCGGGGACGGCGAUUGGUC, SEQ ID NO: 10), hsa-miR-328-3p (CUGGCCCUCUCUGCCUUCCGU, SEQ ID NO:11), hsa-miR-486-5p(UCCUGUACUGAGCUGCCCCGAG, SEQ ID NO:12) and hsa-miR-99b-5p (CACCCGUAGAACCGACCUUGCG, SEQ ID NO:13), a total of 13, were used for subsequent analysis.Then we used linear models, random forests, extremely randomized trees, neural networks, gradient boosting machines (GBM), and ensemble models. Methods) a total of 6 machine learning algorithms, markers with feature weights < 5% were marked as candidate discarded markers. If a marker was marked as discarded by all algorithms, it was not retained. A total of 13 markers were obtained as candidate markers, including hsa-let-7d-3p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-miR-379-5p, hsa-miR-98-5p, hsa-miR-100-5p, hsa-miR-126-3p, hsa-miR-21-5p, hsa-miR-140-5p, hsa-miR-1908-5p, hsa-miR-328-3p, hsa-miR-486-5p and hsa-miR-99b-5p, which were used to subsequently construct a risk scoring model for lung cancer and non-cancer (high-risk lung cancer and lung positive nodules).

[0216] Example 4. Risk scoring model for lung cancer and non-cancer (high-risk lung cancer and positive lung nodules)

[0217] In order to construct a risk classification model for lung cancer and non-cancer (high-risk lung cancer and positive lung nodules), the miRNA expression data of the training cohort was used. In the training set, the classification effect and feature weight of the model were evaluated using the 5-fold cross-validation method. Combined with the results of pathological detection, a scoring model for lung cancer and non-cancer (high-risk lung cancer and positive lung nodules) was constructed. The prediction results of the risk of lung cancer for each patient were given according to the model formula and procedure. The model evaluation indicators include accuracy (range 0 to 1), area under the receiver operating characteristic curve (AUC, range 0 to 1), sensitivity (range 0 to 1) and specificity (range 0 to 1), which are 0.8556, 0.9136 ( Figure 3 ), 0.8366 and 0.8823, 0.8178 and 0.8933 (Table 2). The results showed that in the training cohort, this risk prediction model had high accuracy, AUC, positive predictive value, negative predictive value, sensitivity and specificity, and the model prediction efficiency was better.

[0218] Table 2. Cross-validation evaluation results of biomarkers in the training set

[0219]

[0220] Example 5. Validation of the predictive efficacy of the risk scoring model for lung cancer and non-cancer (high-risk lung cancer and positive lung nodules)

[0221] In order to verify the effectiveness of the risk score model in predicting lung cancer, patients with high risk of lung cancer and positive lung nodules were combined into a non-lung cancer control group, and another independent cohort was selected as a validation cohort to evaluate the classification effect and feature weights of the model. Taking the pathological test results as the true value, the model evaluation indicators included accuracy (range 0-1), area under the receiver operating characteristic curve (AUC, range 0-1), sensitivity (range 0-1) and specificity (range 0-1), which were 0.7997, 0.8906 ( Figure 4 ), 0.72 and 0.8621, 0.8182 and 0.7812 (Table 3). The results showed that in the validation cohort, this risk prediction model had high accuracy, AUC, sensitivity and specificity, and the model prediction efficiency was better.

[0222] Table 3. Results of the self-sampling method for biomarker evaluation in the validation set

[0223]

[0224] All documents mentioned in the present invention are cited as references in this application, just as each document is cited as reference individually. In addition, it should be understood that after reading the above teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. Use of blood extracellular vesicle miRNA in the preparation of a chip, a detection reagent or a detection kit for early diagnosis of lung cancer.

2. The use according to claim 1, characterized in that The blood extracellular vesicle miRNA is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or (ii) a miRNA that is complementary to the miRNA sequence described in (i).

3. A miRNA chip, comprising: Solid phase carrier; as well as Oligonucleotide probes are sequentially fixed on the solid phase carrier, and the oligonucleotide probes specifically bind to miRNA; Wherein, the miRNA is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or (ii) a miRNA that is complementary to the miRNA sequence described in (i).

4. Use of the miRNA chip according to claim 3 for preparing an early diagnosis kit for lung cancer.

5. A detection kit containing a detection reagent for detecting miRNA; in, The miRNA is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or (ii) a miRNA complementary to the miRNA sequence described in (i) Alternatively, the detection kit is equipped with the miRNA chip according to claim 3.

6. A miRNA isolated from extracellular vesicles in blood for early diagnosis of lung cancer; in, The miRNA is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or (ii) a miRNA that is complementary to the miRNA sequence described in (i).

7. An isolated or artificially constructed precursor miRNA, which can be cleaved and expressed in human cells to become the miRNA according to claim 6.

8. An isolated polynucleotide, which can be transcribed into a precursor miRNA by a human cell, and which can be cleaved and expressed into the miRNA of claim 6 in a human cell. In a preferred embodiment, the polynucleotide has a structure shown in Formula I: Seq 正向 -X-Seq 反向 Formula I In Formula I, Seq 正向 A nucleotide sequence that can be expressed as the miRNA in human cells; Seq 反向 is a nucleotide sequence that is substantially complementary or completely complementary to Seq in the forward direction; X is located in Seq 正向 and Seq 反向 and the spacer sequence is 正向 and Seq 反向 Not complementary; After the structure shown in Formula I is transferred into human cells, a secondary structure shown in Formula II is formed: In Formula II, Seq 正向 , Seq 反向 and X is defined as above, || means in Seq 正向 and Seq 反向 The base pairing relationship formed between them.

9. A vector comprising the miRNA according to claim 6 or the polynucleotide according to claim 8.

10. A method for early diagnosis of lung cancer, comprising the steps of: (a) detecting the expression level of miRNA in a sample of a test subject; (b) inputting the miRNA expression value obtained in step (a) into the risk scoring model to obtain the probability that the subject is a lung cancer patient or a high-risk group for lung cancer or a patient with positive lung nodules; (c) based on the probability obtained in step (b), providing a prediction result that the subject suffers from lung cancer; in, The miRNA is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, a miRNA having a sequence as shown in SEQ ID NO: 6, a miRNA having a sequence as shown in SEQ ID NO: 7, and / or a miRNA having a sequence as shown in SEQ ID NO: 8, a miRNA having a sequence as shown in SEQ ID NO: 9, a miRNA having a sequence as shown in SEQ ID NO: 10, a miRNA having a sequence as shown in SEQ ID NO: 11, a miRNA having a sequence as shown in SEQ ID NO: 12, and / or a miRNA having a sequence as shown in SEQ ID NO: 13; or (ii) a miRNA that is complementary to the miRNA sequence described in (i).

11. An early diagnosis device for lung cancer, the device comprising a storage device storing instructions for executing the method for early diagnosis of lung cancer according to claim 10.

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