Blood extracellular vesicle miRNA combination and application thereof in early diagnosis of lung cancer

Abstract

The invention discloses a blood extracellular vesicle miRNA (micro Ribonucleic Acid), miRNAs (micro Ribonucleic Acid) hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p and hsa-miR-4732-5p, which can be used as biomarkers for early diagnosis of lung cancer, so that the blood extracellular vesicle miRNA can be used for preparing a chip, a detection reagent or a detection kit for early diagnosis of lung cancer. The blood extracellular vesicle miRNA biomarker disclosed by the invention has high sensitivity and high specificity on early diagnosis of lung cancer, so that false positive caused by a traditional diagnosis method can be reduced, and the miRNA biomarker has a potential good clinical application value.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine. Specifically, the present invention relates to the diagnosis of lung cancer, in particular to the combination of blood extracellular vesicle miRNA and its application in the early diagnosis of lung cancer. Background Art

[0002] Lung cancer refers to a malignant tumor originating from the bronchial mucosal epithelium or alveolar epithelium. It is the second most common type of cancer in the world and the main cause of cancer-related death, which has caused a great burden on society. According to the histopathological subtype, lung cancer can be roughly divided into two types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Among them, non-small cell lung cancer is the main type of lung cancer, accounting for about 80%-85% of all cases, including adenocarcinoma, squamous cell carcinoma, and large cell undifferentiated carcinoma. The clinical symptoms of early lung cancer are hidden, and there is a lack of effective screening methods to guide early clinical diagnosis. Most lung cancer patients (75%) are diagnosed with locally advanced or metastatic disease when they are discovered, missing the best time for radical surgical treatment. Although great progress has been made in treatment methods such as surgery, radiotherapy, chemotherapy and targeted therapy in the past few decades, the 5-year survival rate of advanced lung cancer is still low (10%-20%), while the five-year survival rate of patients with early lung cancer after surgery can reach 77%-92%. Therefore, early diagnosis of lung cancer is of great significance for reducing the mortality rate of lung cancer patients, improving survival prognosis and alleviating the medical and economic burden on society.

[0003] At present, the main methods for early diagnosis of lung cancer include imaging examinations (CT, PET-CT, etc.), laboratory tests, and pathological examinations, but these diagnostic methods have certain limitations. The most commonly used low-dose computed tomography (LDCT) in imaging examinations has limitations such as high false positive rate, overdiagnosis, and radiation exposure. Laboratory tests such as sputum exfoliative cytology and serum tumor antigen markers (such as CEA, CYFRA21-1, NSE, ProGRP, etc.) are limited in their diagnostic utility for lung cancer due to sensitivity and specificity, especially in the detection of early lung cancer. Pathological examinations, such as bronchoscopy and CT-guided percutaneous lung puncture biopsy, are invasive procedures with the risk of pneumothorax and bleeding.

[0004] Therefore, the development of non-invasive / minimally invasive technologies based on liquid biopsy to assist in the differential diagnosis of early lung cancer is an urgent clinical need. Summary of the invention

[0005] The present invention aims to discover and verify a blood extracellular vesicle miRNA biomarker model with high specificity and high sensitivity for early diagnosis of lung cancer to reduce the mortality rate of lung cancer.

[0006] To this end, the present invention provides a biomarker, which can be used clinically as a biomarker for early diagnosis of lung cancer.

[0007] The present invention also provides a chip and a kit that can be used for early diagnosis of lung cancer.

[0008] The present invention also provides a method for early diagnosis of lung cancer using the biomarker.

[0009] 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.

[0010] In a specific embodiment, the blood extracellular vesicle miRNA is:

[0011] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; or

[0012] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0013] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0014] (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, and a miRNA having a sequence as shown in SEQ ID NO: 6.

[0015] In a preferred embodiment, the miRNA is isolated from human.

[0016] In a second aspect, the present invention provides a miRNA chip, wherein the miRNA chip comprises:

[0017] a solid support; and

[0018] Oligonucleotide probes are sequentially fixed on the solid phase carrier, and the oligonucleotide probes specifically bind to miRNA;

[0019] Wherein, the miRNA is:

[0020] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; or

[0021] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0022] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0023] (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, and a miRNA having a sequence as shown in SEQ ID NO: 6.

[0024] In a preferred embodiment, the oligonucleotide probe comprises:

[0025] complementary binding region; and / or

[0026] A linker region connected to a solid support.

[0027] In a preferred embodiment, the miRNA chip is used for early diagnosis of lung cancer.

[0028] In a preferred embodiment, the miRNA is isolated from human.

[0029] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0030] 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.

[0031] In a fourth aspect, the present invention provides a detection kit, wherein the detection kit contains a detection reagent for detecting miRNA;

[0032] Wherein, the miRNA is:

[0033] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; or

[0034] (ii) a miRNA complementary to the miRNA sequence described in (i)

[0035] Alternatively, the detection kit is equipped with the miRNA chip according to claim 3.

[0036] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0037] (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, and a miRNA having a sequence as shown in SEQ ID NO: 6.

[0038] In a preferred embodiment, the detection kit is used for early diagnosis of lung cancer.

[0039] In a preferred embodiment, the miRNA is isolated from human.

[0040] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0041] In a fifth aspect, the present invention provides a miRNA isolated from extracellular vesicles of blood for early diagnosis of lung cancer;

[0042] Wherein, the miRNA is:

[0043] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; or

[0044] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0045] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0046] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6.

[0047] In a preferred embodiment, the miRNA is isolated from human.

[0048] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0049] 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.

[0050] 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.

[0051] In a preferred embodiment, the polynucleotide has a structure shown in Formula I:

[0052] Seq 正向 -X-Seq 反向 Formula I

[0053] In Formula I,

[0054] Seq 正向 A nucleotide sequence that can be expressed as the miRNA in human cells;

[0055] Seq 反向 is a nucleotide sequence that is substantially complementary or completely complementary to Seq in the forward direction;

[0056] X is located in Seq 正向 and Seq 反向 and the spacer sequence is 正向 and Seq 反向 Not complementary;

[0057] After the structure shown in Formula I is transferred into human cells, a secondary structure shown in Formula II is formed:

[0058]

[0059] In Formula II, Seq 正向 , Seq 反向 and X is defined as above,

[0060] || means in Seq 正向 and Seq 反向 The base pairing relationship formed between them.

[0061] 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.

[0062] In a ninth aspect, the present invention provides a method for early diagnosis of lung cancer, comprising the steps of:

[0063] (a) detecting the expression level of miRNA in a sample of a subject to be tested;

[0064] (b) inputting the miRNA expression value obtained in step (a) into the risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient;

[0065] (c) determining whether the subject is a healthy subject or a lung cancer patient based on the probability obtained in step (b);

[0066] Wherein, the miRNA is:

[0067] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; or

[0068] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0069] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0070] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6.

[0071] In a preferred embodiment, the miRNA is isolated from human.

[0072] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0073] In a preferred embodiment, the model formula of the risk prediction model in step (b) is as follows:

[0074]

[0075] Among them, x i represents the marker expression value of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient; the classification with the highest probability is taken as the final prediction result of sample i.

[0076] In a preferred embodiment, the risk prediction model is constructed using a machine learning algorithm; preferably, the machine learning algorithm is a gradient boosting machine (GBM), an extreme randomized tree, a linear model, a neural network, a random forest and / or an ensemble method.

[0077] In a preferred embodiment, the method has a sensitivity of >95% and a specificity of >87% for diagnosing early lung cancer.

[0078] In a tenth aspect, the present invention provides an apparatus for early diagnosis of lung cancer, the apparatus comprising a storage device storing instructions for executing the method for early diagnosis of lung cancer according to the ninth aspect.

[0079] In a preferred embodiment, the device comprises the following means:

[0080] (a) a detection device, wherein the detection device detects the expression level of miRNA in a sample of a subject to be tested;

[0081] (b) a computing device, wherein the computing device inputs the expression value of the miRNA detected by the device (a) into a risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient; and

[0082] (c) an output device, which outputs a conclusion as to whether the subject is a healthy subject or a lung cancer patient based on the probability obtained by device (b);

[0083] Wherein, the miRNA is:

[0084] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; or

[0085] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0086] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0087] (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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6.

[0088] In a preferred embodiment, the miRNA is isolated from human.

[0089] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0090] In a preferred embodiment, the model formula of the risk prediction model described in device (b) is as follows:

[0091]

[0092] Among them, x i represents the marker expression value of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient; the classification with the highest probability is taken as the final prediction result of sample i.

[0093] In a preferred embodiment, the risk prediction model is constructed using a machine learning algorithm; preferably, the machine learning algorithm is a gradient boosting machine (GBM), an extreme randomized tree, a linear model, a neural network, a random forest and / or an ensemble method.

[0094] 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

[0095] Figure 1 The identification results of extracellular vesicles by TEM are shown;

[0096] Figure 2 The results of extracellular vesicle NTA detection are shown;

[0097] Figure 3 The ROC curve for the training cohort is shown;

[0098] Figure 4 ROC curves for the validation cohort are shown. DETAILED DESCRIPTION

[0099] Lung cancer is the second most common type of cancer in the world and the leading cause of cancer-related death. Early detection of lung cancer has a significant effect on improving the patient's prognosis. Currently, early diagnosis of lung cancer in clinical practice mainly relies on low-dose computed tomography (LDCT). LDCT has high sensitivity, but poor specificity, high false positive rate, radiation hazards, and brings additional risks and expenses to patients. Liquid biopsy biomarkers are expected to improve the accuracy of early detection of lung cancer and reduce problems such as false positive rate and overdiagnosis. However, the diagnostic efficiency of most current biomarkers is limited.

[0100] To solve this technical problem, the inventors have explored a method for early diagnosis of lung cancer with higher specificity and sensitivity. Specifically, the research work of the present invention is divided into the following two parts:

[0101] (1) Study the blood extracellular vesicle microRNAs (miRNAs) biomarkers in patients with early lung cancer and healthy people, and construct a highly specific and sensitive blood extracellular vesicle miRNA diagnostic model through statistical and machine learning methods;

[0102] (2) Use independent validation cohort data to evaluate the effectiveness of the miRNA early diagnosis model in distinguishing lung cancer from healthy subjects.

[0103] Through research works (1) and (2), the present inventors discovered and verified a blood extracellular vesicle miRNAs biomarker diagnostic model that can be used for early diagnosis of lung cancer.

[0104] In the present invention, blood extracellular vesicle miRNA is used to establish a biomarker model to improve the specificity of early diagnosis of lung cancer. However, those skilled in the art know that there are various contents in extracellular vesicles, such as proteins, lipids, small molecule metabolites, lncRNA, circRNA and mRNA, which can also be used to construct a biomarker model to achieve similar effects to miRNA. In addition, many components in the patient's blood, such as CTCs (Circulating tumor cells), ctDNA (Circulating tumor DNA), extracellular free nucleic acids (cfRNA and cfDNA), proteins and metabolites, can also be used as biomarkers for early diagnosis of lung cancer, for development into independent liquid biopsy technology.

[0105] definition

[0106] 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:

[0107] Extracellular vesicles

[0108] The term extracellular vesicles (EVs) used in this article has the meaning commonly understood by those skilled in the art. Extracellular vesicles, also known as exosomes, are small membrane vesicles (30-150nm) that are released into the extracellular matrix after the fusion of intracellular multivesicular bodies (MVBs) with the cell membrane. It is an important medium for intercellular information transmission. Exosomes can be produced by various types of cells and are present in body fluids such as blood, urine, saliva, tears, and bile. Exosomes carry a variety of substances, such as proteins, lipids, metabolites, mRNA, microRNAs (miRNAs), etc., among which miRNAs are the most abundant nucleic acid components in exosomes, making them potential for early diagnosis of lung cancer.

[0109] Some researchers used miRNA-Seq technology to analyze the expression differences of plasma exosomal miRNAs in healthy people and stage I NSCLC patients, and established an exosomal miRNA biomarker model that specifically distinguishes patients with lung adenocarcinoma and lung squamous cell carcinoma (PMID: 28606918). Other researchers used miRNA chip detection technology to determine the specific exosomal miRNAs in the serum of patients with small cell lung cancer, and identified the 3-miRNA Panel (miR-200b-3p, miR-3124-5p and miR-92b-5p) composed of serum exosomal miRNAs, which can significantly improve the diagnostic value of SCLC (AUC = 0.93). At the same time, it was found that the 3-miRNA Panel was significantly associated with poor prognosis (PMID: 37705067). These studies show that serum / plasma exosomal miRNAs can be used as potential biomarkers related to the diagnosis and prognosis of lung cancer.

[0110] Blood extracellular vesicle miRNA of the present invention

[0111] At present, LDCT, which is widely used in the clinical diagnosis of early lung cancer, has high diagnostic sensitivity, but poor specificity and high false positive rate. There is still a lack of highly specific and sensitive biomarkers in the clinic, especially blood biomarkers for the early diagnosis of lung cancer, and there is no biomarker combined with LDCT in the clinic to improve the diagnostic efficiency of lung cancer. The research on biomarkers for early diagnosis of lung cancer has the following characteristics: a. The number of studies based on the second-generation sequencing platform is small; b. There are few studies that use exosome contents as biomarkers for early diagnosis of lung cancer and have been verified by another set of data; c. The research cohort is a subtype of NSCLC or SCLC or lung adenocarcinoma and lung squamous cell carcinoma.

[0112] There are only a few reports on the use of blood extracellular vesicle miRNA biomarkers in the early diagnosis of lung cancer. For example, researchers (PMID: 35366907; CN 111218513 A) used small RNA sequencing technology to detect the expression profile of plasma exosome miRNAs in patients with uncertain pulmonary nodules (IPNs). Using the LASSO method, a circulating exosome miRNA (circulating sEV miRNA, CirsEV-miR) diagnostic model based on five miRNAs (let-7b-3p, miR-101-3p, miR-125b-5p, miR-150-5p and miR-3168) was constructed, which can be used to distinguish benign from malignant IPNs, with an AUC of 0.92. The model score is related to the diameter of IPNs, and can also distinguish benign from malignant IPNs with a diameter of less than 1 cm. However, this study focused on the judgment of benign and malignant nature of IPNs, which is different from the clinical application scenario of differentiating and diagnosing various types of lung cancer patients from healthy people.

[0113] In view of the shortcomings of current research, the inventors used the blood of patients with early lung cancer as research samples and healthy people as the control group, and used the self-developed extracellular vesicle (exosome) extraction reagent L3525 (3DMed, Shanghai) to extract extracellular vesicles. The second-generation sequencing technology small RNA sequencing was further used to detect the expression level of extracellular vesicle miRNA in the blood of lung cancer patients and healthy people. The extracellular vesicle miRNA biomarker diagnostic model that can be used for the early diagnosis of lung cancer was discovered and verified in two independent populations, the training cohort and the validation cohort, and finally a blood extracellular vesicle miRNA biomarker with high specificity and high sensitivity was obtained.

[0114] Specifically, the inventors used small RNA sequencing technology to detect the blood extracellular vesicle miRNA expression profiles of early lung cancer patients and healthy people, and applied statistical methods and machine learning methods to find that 6 blood extracellular vesicle miRNAs hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, and hsa-miR-4732-5p can be used as early diagnosis biomarkers for lung cancer. Based on the above-discovered 6 markers, the inventors constructed a risk prediction model with high specificity and high sensitivity, and the model has a sensitivity of >95% and a specificity of >87% for the diagnosis of early lung cancer.

[0115] 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, and / or a miRNA with a sequence as shown in SEQ ID NO: 6; or a miRNA complementary thereto.

[0116] In a preferred embodiment, the blood extracellular vesicle miRNA is 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, and a miRNA having a sequence as shown in SEQ ID NO: 6.

[0117] 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, and can further be used to prepare miRNA chips, detection reagents or detection kits for the early diagnosis of lung cancer.

[0118] In a specific embodiment, the miRNA chip comprises: a solid phase carrier; and oligonucleotide probes fixed in order on the solid phase carrier, wherein the oligonucleotide probes specifically bind to the miRNA. The design of the oligonucleotide probes can be routinely performed in the art. For example, the oligonucleotide probes contain: a complementary binding region; and / or a connection region connected to the solid phase carrier.

[0119] In a specific embodiment, the detection kit contains a detection reagent for detecting the above-mentioned miRNA, or contains the above-mentioned miRNA chip.

[0120] Based on the teachings of the present invention, those skilled in the art can conceive of preparing 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 conceive of an isolated polynucleotide, which can be transcribed by human cells into the precursor miRNA, and then cleaved and expressed in human cells as the miRNA of the present invention.

[0121] In a specific embodiment, the polynucleotide has a structure shown in Formula I:

[0122] Seq 正向-X-Seq 反向 Formula I

[0123] In Formula I, 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;

[0124] After the structure shown in Formula I is transferred into human cells, a secondary structure shown in Formula II is formed:

[0125]

[0126] 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.

[0127] Based on the teachings of the present invention, those skilled in the art can also conceive of vectors containing the above-mentioned miRNA or polynucleotide.

[0128] Method of the present invention and device for implementing the method of the present invention

[0129] The blood extracellular vesicle miRNA of the present invention can be used for the early diagnosis of lung cancer. Therefore, the inventors also provide a method for early diagnosis of lung cancer using the blood extracellular vesicle miRNA, comprising the following steps:

[0130] (a) detecting the expression level of miRNA in a sample of a subject to be tested;

[0131] (b) inputting the miRNA expression value obtained in step (a) into the risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient;

[0132] (c) Determine whether the subject is a healthy subject or a lung cancer patient based on the probability obtained in step (b).

[0133] The early lung cancer diagnosis method of the present invention has a sensitivity of >95% and a specificity of >87% for early lung cancer diagnosis.

[0134] Based on the teachings of the present invention, that is, specific blood extracellular vesicle miRNA can be used for early diagnosis of lung cancer, those skilled in the art can use various methods, such as various algorithms to construct a risk prediction model for lung cancer. In a specific embodiment, the risk prediction model is constructed using a machine learning algorithm; the machine learning algorithm includes but is not limited to a gradient boosting machine (GBM), an extreme random tree, a linear model, a neural network, a random forest, and / or an ensemble method.

[0135] In a preferred embodiment, the model formula of the risk prediction model in step (b) is as follows:

[0136]

[0137] Among them, x i represents the marker expression value of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient; the classification with the highest probability is taken as the final prediction result of sample i.

[0138] To facilitate the implementation of the diagnostic method of the present invention, the inventors also provide a device for early diagnosis of lung cancer, wherein the device has a storage device storing instructions for executing the method for early diagnosis of lung cancer of the present invention.

[0139] Based on the teachings of the present invention, those skilled in the art know how to construct a device for early diagnosis of lung cancer. For example, the device may include 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 expression value of miRNA obtained by the detection device into a risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient; and an output device for outputting a conclusion of whether the subject is a healthy subject or a lung cancer patient based on the probability obtained by the calculation device.

[0140] Advantages of the present invention:

[0141] The inventors included samples of various types of lung cancer patients in the real world, used the independently developed extraction reagent L3525 to extract blood extracellular vesicles, and further used small RNA sequencing to detect the expression of blood extracellular vesicle miRNA. Ultimately, they discovered and verified blood extracellular vesicle miRNA biomarkers with high specificity and high sensitivity.

[0142] The blood extracellular vesicle miRNA biomarker and early lung cancer diagnosis method of the present invention have high sensitivity and high specificity for early lung cancer diagnosis.

[0143] 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.

[0144] Example

[0145] Material

[0146] The materials used in the following examples are all commercially available.

[0147] method

[0148] 1. Study cohort and clinical information

[0149] This study enrolled 71 lung cancer patients, collected blood samples from patients before surgery, and randomly selected 50 patients as a training cohort to build a highly specific and sensitive blood extracellular vesicle miRNA diagnostic model, and 21 patients as a validation cohort to verify the effectiveness of the early diagnosis model for lung cancer. In addition, 52 healthy individuals were collected as a control group, of which 36 were assigned to the training cohort and 16 were used for the validation cohort.

[0150] 2. Extraction and Characterization of Extracellular Vesicles

[0151] 1) Blood collection and extracellular vesicle extraction

[0152] Blood samples were collected in 10 mL anticoagulation vacuum blood collection tubes (REF367525, BD, USA), slowly inverted several times and placed vertically, and then separated by two-step centrifugation. First, the sample was centrifuged at 1600 g for 10 min at 4 ° C. After centrifugation, the hemolysis level of the sample was determined according to the standard, and samples with less than level 4 were used for subsequent research. Then the supernatant was transferred to a 1.5 mL EP tube and centrifuged at 16000 g for 15 min at 4 ° C to remove residual cell debris. The supernatant was divided into 1.5 mL EP tubes and stored in a -80 ° C refrigerator for later use.

[0153] The frozen plasma samples were taken out and thawed in a 37°C water bath, and centrifuged at 12000g for 10 min at 4°C. The supernatant was filtered through a 0.45μm filter column (CLS8163-100EA, Corning, USA) and a 0.22μm filter column (CLS8161-100EA, Corning, USA) in sequence, and the centrifugation conditions were 4°C 12000g for 5 min, and the filtrate was collected into a 2mL EP tube. The volume of the filtrate was measured, and 1 / 4 volume of L-type exosome precipitation agent (L3525, 3DMed, Shanghai) was added. After thorough mixing, it was incubated at 4°C for 30 min, centrifuged at 4700g for 30 min at 4°C, the supernatant was discarded, and 200μL PBS was added to resuspend the extracellular vesicles.

[0154] In addition to using the self-developed extracellular vesicle (exosome) extraction reagent L3525 (3DMed, Shanghai), the following alternatives can also be used to extract blood extracellular vesicles:

[0155] Centrifugation (differential centrifugation, density gradient centrifugation), precipitation (PEG precipitation, organic solvent precipitation), particle size separation (ultrafiltration, exclusion chromatography), immunoaffinity, microfluidics and other commercial exosome isolation kits. Preferably, commercial exosome isolation kits are directly used.

[0156] 2) Characteristics of extracellular vesicles

[0157] In order to detect the characteristics of extracellular vesicles in the blood of lung cancer patients and healthy people, the present invention uses a transmission electron microscope (TEM) to detect the morphology of extracellular vesicles and a nanoparticle tracking analyzer (NTA) to detect the particle size distribution of extracellular vesicles.

[0158] TEM detection: Extracellular vesicles were resuspended in PBS and fixed with 4% paraformaldehyde, and the fixed extracellular vesicles were dropped on a carbon-coated copper grid. After incubation at room temperature for 5 minutes, the grid was first rinsed twice with PBS, then washed with PBS containing 50 mM glycine for 3 minutes, incubated with PBS containing 0.5% BSA for 10 minutes, and negatively stained with 2% uranyl acetate for 10 minutes. Finally, a transmission electron microscope (H-7650, Hitachi, Japan) was used to observe and photograph.

[0159] NTA detection: First, the plasma extracellular vesicles were diluted to 1*10^ 7 -1*10^ 9 / mL, pipetting and mixing. Subsequently, the NTA instrument (NanoSight NS300, Malvern, UK) was turned on and the sample was injected into the sample chamber. 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 plasma extracellular vesicles were analyzed using NTA version 2.3 analysis software.

[0160] 3. Extraction of miRNA from plasma extracellular vesicles

[0161] According to the product manual, total RNA was extracted from plasma extracellular vesicles using miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai), and RNA was eluted using 15 μL RNase-free water. The concentration and fragment distribution of miRNA were detected using Agilent 2100 Bioanalyzer and the matching small RNA analysis kit (5067-1548, Agilent, USA).

[0162] 4. Detection of plasma extracellular vesicle miRNA expression levels

[0163] The miRNA library was prepared using the NEBNext Multiplex Small RNA Library Prep Set for Illumina kit (E7300L, NEB, USA) according to the product manual. 6 μL of each RNA sample was loaded, followed by ligation of the 3' end adapter, hybridization of the reverse transcription primer, ligation of the 5' end adapter, reverse transcription, and 18 cycles of PCR amplification. The PCR enrichment product was purified using the NucleoSpin Geland PCR Clean-up kit (740609.250, MN, Germany), and the library DNA was finally eluted with 30 μL of nuclease-free water. The DNA concentration was quantified using the Invitrogen Qubit 4.0 fluorometer and the accompanying reagent Qubit dsDNA HSAssay Kit (Q32854, Thermofisher, USA). The distribution of library DNA fragments was detected using the Agilent 2100 bioanalyzer and the accompanying chip and reagent Agilent High Sensitivity DNA Kit (5067-4626, Agilent, USA). The sequencing was performed using the Illumina NovaSeq platform, with a sequencing strategy of PE150 and a sequencing data volume of 6G for each library.

[0164] The present invention uses small RNA sequencing technology to detect the expression level of blood extracellular vesicle miRNA. However, those skilled in the art are aware of various alternatives, including but not limited to: qRT-PCR, microarray chip method, nano-biosensor technology, etc.

[0165] 5. Sequencing data analysis process

[0166] Based on small RNA sequencing detection technology, the expression profiles of miRNAs in extracellular vesicles in the blood of lung cancer patients and healthy people were obtained. The analysis process of sequencing data is as follows:

[0167] 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 / ) and count the number of reads mapped to miRNA.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 6. Discovery of biomarkers

[0172] Based on the expression of miRNA 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 as biomarkers that can be used to distinguish lung cancer patients from healthy people. The process is as follows:

[0173] 1) Grouping of training cohort. Based on the results of sample pathology tests, the samples in the training cohort were divided into two groups: healthy individuals and lung cancer patients;

[0174] 2) Statistical methods to screen biomarkers. First, the statistical methods U test and T test were used to analyze the expression differences of all miRNAs between the two groups, and the mutual information (MI) between all miRNA expressions and sample groups was calculated. The miRNAs with statistical differences between groups 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;

[0175] 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 in turn, 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 7 markers. Subsequently, six machine learning algorithms, including gradient boosting machine (GBM), extreme randomized trees, linear model, neural network, random forest and ensemble methods, were used to train and evaluate the classification effect and feature weights of the seven markers as a whole on the training cohort data. Markers with feature weights <5% were marked as candidate discarded markers. If a marker was marked as discarded by all algorithms, it would not be retained. Based on the above methods, 6 markers were obtained as candidate markers, including hsa-miR-150-3p (SEQ ID NO: 1, CUGGUACAGGCCUGGGGGACAG), hsa-miR-150-5p (SEQ ID NO: 2, UCUCCCAACCCUUGUACCAGUG), hsa-miR-335-3p (SEQ ID NO: 3, UUUUUCAUUAUUGCUCCUGACC), hsa-miR-375 (SEQ ID NO: 4, UUUGUUCGUUCGGCUCGCGUGA), hsa-miR-4433b-5p (SEQ ID NO: 5, AUGUCCCACCCCCACUCCUGU) and hsa-miR-4732-5p (SEQ ID NO: 6, UGUAGAGCAGGGAGCAGGAAGCU), for subsequent analysis;

[0176] In addition to the model building method used above, those skilled in the art may also adopt other alternatives, including but not limited to: complex feature engineering, support vector machines and deep learning.

[0177] 7. Risk scoring model construction

[0178] Healthy individuals and lung cancer patients were used as the classification prediction targets. The six markers discovered in (6) were used. Six machine learning algorithms, including gradient boosting machine (GBM), neural network, extremely randomized trees, ensemble methods, random forest and linear model, were used. Different hyper-parameters were preset for each algorithm. The diagnostic models of multiple different machine learning algorithms were trained using the training cohort sample data. The model formula is as follows:

[0179]

[0180] x i represents the marker expression value of sample i, P(x i ) is the probability of the corresponding classification of sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient. 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.

[0181] 8. Evaluation and Validation of Risk Scoring Model Performance

[0182] In the training cohort, the classification effect and feature weight of the model were evaluated using the 5-fold cross-validation method, and in the validation cohort. Model evaluation and validation indicators included the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1), accuracy (ranging from 0 to 1), positive predictive value (ranging from 0 to 1), and negative predictive value (ranging from 0 to 1). The specificity (ranging from 0 to 1) and sensitivity (ranging from 0 to 1) of the model were evaluated, and the higher the value, the better the classification performance of the model.

[0183] 9. Application of risk scoring model

[0184] 1) Collect peripheral blood from patients with suspected lung cancer in clinical diagnosis, obtain peripheral blood extracellular vesicle miRNA, and use small RNA sequencing to obtain the expression level of biomarkers;

[0185] 2) The obtained expression values ​​of blood extracellular vesicles hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, and hsa-miR-4732-5p were put into the trained model formula to calculate the probability of each patient being predicted to be healthy and having lung cancer;

[0186] 3) Select the output result with the highest probability and give the predicted result of lung cancer risk for each patient.

[0187] Example 1.

[0188] In this example, a total of 71 lung cancer patients were enrolled. Blood samples were collected from patients before surgery, and 50 patients were randomly selected as a training cohort to build a highly specific and sensitive blood extracellular vesicle miRNA diagnostic model, and 21 patients were used as a validation cohort to verify the effectiveness of the early diagnosis model for lung cancer. In addition, 52 healthy individuals were collected as a control group, of which 36 were assigned to the training cohort and 16 were used for the validation cohort.

[0189] Specific grouping information is shown in Table 1.

[0190] Table 1

[0191]

[0192] Example 2. Extraction and characterization of blood extracellular vesicles

[0193] In this example, the extracellular vesicle (exosome) extraction reagent L3525 (3DMed, Shanghai) independently developed by Shanghai Siludi Biomedical Technology Co., Ltd. was used to extract extracellular vesicles from the plasma of lung cancer patients and healthy people. In order to detect the characteristics of the isolated extracellular vesicles, the present invention uses TEM to detect the morphology of the extracellular vesicles. The results show that the extracellular vesicles present a typical "horseshoe-shaped" morphology (see Figure 1 The particle size distribution of extracellular vesicles was detected by NTA, and the results showed that the average particle size of extracellular vesicles was 114.2 nm, which was consistent with the particle size distribution of extracellular vesicles (see Figure 2 ).

[0194] Example 3. Biomarker Discovery

[0195] In this example, small RNA sequencing was used to detect the expression level of extracellular vesicle miRNA in the blood of lung cancer patients and healthy people. Statistical methods (U test and T test) were used to analyze the expression differences of all miRNAs between lung cancer patients and healthy people and 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 healthy people as biomarkers. Candidate molecular markers include hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, hsa-miR-4732-5p and hsa-miR-1908-5p, a total of 7, for subsequent analysis. Subsequently, six machine learning algorithms, including gradient boosting machine (GBM), neural network, extreme randomized tree, ensemble method, random forest and linear model, were used to mark markers with feature weight <5% as candidate discarded markers. If a marker was marked as discarded by all algorithms, it was not retained, and a total of six markers were obtained as candidate markers, including hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p and hsa-miR-4732-5p, which were used to construct the risk score model for lung cancer and healthy people.

[0196] Example 4. Risk scoring model for lung cancer and healthy individuals

[0197] In order to construct a risk classification model for lung cancer patients and healthy individuals, the miRNA expression data of the training cohort were used, and the classification effect and feature weight of the model were evaluated using the 5-fold cross-validation method in the training cohort. Combined with the results of pathological detection, a risk scoring model for lung cancer and healthy individuals was constructed, and 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 the area under the receiver operating characteristic curve (AUC, range 0-1), accuracy (range 0-1), positive predictive value (range 0-1), negative predictive value (range 0-1), specificity (range 0-1) and sensitivity (range 0-1), which are 1( Figure 3), 0.9675, 0.9818, 0.9556, 0.975 and 0.96 (Table 2). The results showed that in the training cohort, this risk prediction model had a high AUC, accuracy, positive predictive value, negative predictive value, specificity and sensitivity, and the model prediction performance was better (see Table 2).

[0198] Table 2. Results of cross-validation evaluation of biomarkers in the training cohort

[0199]

[0200] Example 5. Validation of the predictive performance of the risk scoring model for lung cancer and healthy individuals

[0201] In order to verify the performance of the risk score model in predicting lung cancer, another independent cohort was selected as the validation cohort to evaluate the classification effect and feature weight of the model. Taking the pathological test results as the true value, the model evaluation indicators include AUC (range 0 to 1), accuracy (range 0 to 1), positive predictive value (range 0 to 1), negative predictive value (range 0 to 1), specificity (range 0 to 1) and sensitivity (range 0 to 1), which are 0.994 ( Figure 4 ), 0.9137, 0.9091, 0.9333, 0.875 and 0.9524 (Table 3). The results showed that in the validation cohort, this risk prediction model had a high AUC, accuracy, positive predictive value, negative predictive value, specificity and sensitivity, and the model prediction performance was better (see Table 3).

[0202] Table 3. Results of the self-sampling method for biomarker assessment in the validation cohort

[0203]

[0204] 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 chips, detection reagents or detection kits 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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; 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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; 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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; 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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; 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.

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 prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient; (c) determining whether the subject is a healthy subject or a lung cancer patient based on the probability obtained in step (b); 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, and / or a miRNA having a sequence as shown in SEQ ID NO: 6; or (ii) a miRNA that is complementary to the miRNA sequence described in (i).

11. A device for early diagnosis of 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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