Application of microRNA combination of blood small extracellular vesicles in diagnosis and differential diagnosis of lung cancer

Abstract

The invention discloses a blood extracellular vesicle miRNA, miRNAs hsa-miR-106b-3p and hsa-miR-106b-5p which can be used as a biomarker for early diagnosis of lung cancer, so that the miRNA, the miRNAs hsa-miR-106b-3p and the hsa-miR-106b-5p 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 and differential diagnosis of lung cancer, especially the combination of blood extracellular vesicle microRNA and its application in the early diagnosis of lung cancer. Background Art

[0002] Primary bronchogenic carcinoma, also known as lung cancer, is a malignant tumor with high morbidity and mortality in my country and other countries around the world, which seriously endangers human health. Lung cancer is roughly divided into two major tissue subtypes: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), of which non-small cell lung cancer accounts for about 80%-85%, including adenocarcinoma, squamous cell carcinoma, and large cell undifferentiated carcinoma. Due to the hidden clinical symptoms and the lack of effective screening methods to guide early clinical diagnosis, about 75% of lung cancer patients are already locally advanced or metastatic when diagnosed, missing the best time for radical surgical treatment, and the 5-year survival rate is about 20%. The five-year survival rate of patients with early lung cancer after surgery is 77%-92%. Therefore, early screening and early diagnosis of lung cancer are crucial to improving the prognosis of patients.

[0003] At present, the main methods used for early diagnosis of lung cancer in clinical practice include imaging examination (CT, PET-CT, etc.), pathological examination and laboratory examination, but these diagnostic methods have certain limitations. Low-dose spiral CT (LDCT), which is widely used in imaging examinations, has problems such as radiation exposure, high false positive rate and overdiagnosis. Pathological examinations such as bronchoscopy and CT-guided percutaneous lung puncture biopsy are invasive operations with the risk of pneumothorax and bleeding. Laboratory examinations such as sputum exfoliative cytology and serum tumor antigen markers (such as CEA, CYFRA21-1, etc.) have poor sensitivity or specificity for the diagnosis of lung cancer and cannot meet the needs of early diagnosis. 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.

[0004] The researchers used small RNA sequencing technology to detect the expression profile of plasma exosome miRNAs in patients with indeterminate pulmonary nodules (IPNs), and constructed a 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) using the LASSO method to distinguish between benign and malignant IPN patients (PMID: 35366907; CN 111218513 A). Other researchers used miRNA chip detection technology to identify specific exosomal miRNAs in the serum of patients with small cell lung cancer, and identified a panel composed of three serum exosomal miRNAs (miR-200b-3p, miR-3124-5p, and miR-92b-5p) as diagnostic and prognostic markers for SCLC (PMID: 37705067). These studies show that serum / plasma exosomal miRNAs can be used as potential biomarkers for early diagnosis of lung cancer. Summary of the invention

[0005] The purpose of the present invention is to improve the specificity of early diagnosis of lung cancer by discovering and verifying a highly specific blood extracellular vesicle miRNA biomarker model.

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

[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 the 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 combination of a miRNA with a sequence as shown in SEQ ID NO: 1 and a miRNA with a sequence as shown in SEQ ID NO: 2; or

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

[0013] In a preferred embodiment, the miRNA is isolated from human.

[0014] In a second aspect, the present invention provides a miRNA chip, wherein the miRNA chip comprises:

[0015] a solid support; and

[0016] Oligonucleotide probes are sequentially fixed on the solid phase carrier, and the oligonucleotide probes specifically bind to miRNA;

[0017] Wherein, the miRNA is:

[0018] (i) a combination of a miRNA with a sequence as shown in SEQ ID NO: 1 and a miRNA with a sequence as shown in SEQ ID NO: 2; or

[0019] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0020] In a preferred embodiment, the oligonucleotide probe comprises:

[0021] complementary binding region; and / or

[0022] A linker region connected to a solid support.

[0023] In a preferred embodiment, the miRNA chip is used for early diagnosis of lung cancer.

[0024] In a preferred embodiment, the miRNA is isolated from human.

[0025] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0026] In a third aspect, the present invention uses the miRNA chip described in the second aspect to prepare an early diagnosis kit for lung cancer.

[0027] In a fourth aspect, the present invention provides a detection kit, wherein the detection kit contains a detection reagent for detecting miRNA;

[0028] Wherein, the miRNA is:

[0029] (i) a combination of a miRNA having a sequence as shown in SEQ ID NO: 1, and a miRNA having a sequence as shown in SEQ ID NO: 2; or

[0030] (ii) a miRNA complementary to the miRNA sequence described in (i)

[0031] Alternatively, the detection kit is equipped with the miRNA chip according to claim 3.

[0032] In a preferred embodiment, the detection kit is used for early diagnosis of lung cancer.

[0033] In a preferred embodiment, the miRNA is isolated from human.

[0034] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0035] In a fifth aspect, the present invention provides a miRNA isolated from extracellular vesicles of blood for early diagnosis of lung cancer;

[0036] Wherein, the miRNA is:

[0037] (i) a combination of a miRNA having a sequence as shown in SEQ ID NO: 1, and a miRNA having a sequence as shown in SEQ ID NO: 2; or

[0038] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[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 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.

[0042] 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.

[0043] In a preferred embodiment, the polynucleotide has a structure shown in Formula I:

[0044] Seq 正向 -X-Seq 反向 Formula I

[0045] In Formula I,

[0046] Seq 正向 A nucleotide sequence that can be expressed as the miRNA in human cells;

[0047] Seq 反向is a nucleotide sequence that is substantially complementary or completely complementary to Seq in the forward direction;

[0048] X is located in Seq 正向 and Seq 反向 and the spacer sequence is 正向 and Seq 反向 Not complementary;

[0049] After the structure shown in Formula I is transferred into human cells, a secondary structure shown in Formula II is formed:

[0050]

[0051] In Formula II, Seq 正向 , Seq 反向 and X is defined as above,

[0052] || means in Seq 正向 and Seq 反向 The base pairing relationship formed between them.

[0053] 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.

[0054] In a ninth aspect, the present invention provides a method for early diagnosis of lung cancer, comprising the steps of:

[0055] (a) detecting the expression level of miRNA in a sample of a subject to be tested;

[0056] (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;

[0057] (c) determining whether the subject is a healthy subject or a lung cancer patient based on the probability obtained in step (b);

[0058] Wherein, the miRNA is:

[0059] (i) a combination of a miRNA having a sequence as shown in SEQ ID NO: 1, and a miRNA having a sequence as shown in SEQ ID NO: 2; or

[0060] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0061] In a preferred embodiment, the miRNA is isolated from human.

[0062] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0063] In a preferred embodiment, the model formula of the risk prediction model in step (b) is as follows:

[0064]

[0065] 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.

[0066] 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.

[0067] In a preferred embodiment, the method has a sensitivity of >95% and a specificity of >95% for diagnosing early lung cancer.

[0068] 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 in the ninth aspect.

[0069] In a preferred embodiment, the device comprises the following means:

[0070] (a) a detection device, wherein the detection device detects the expression level of miRNA in a sample of a subject to be tested;

[0071] (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

[0072] (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);

[0073] Wherein, the miRNA is:

[0074] (i) a combination of a miRNA having a sequence as shown in SEQ ID NO: 1, and a miRNA having a sequence as shown in SEQ ID NO: 2; or

[0075] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0076] In a preferred embodiment, the miRNA is isolated from human.

[0077] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0078] In a preferred embodiment, the model formula of the risk prediction model described in device (b) is as follows:

[0079]

[0080] 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.

[0081] 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.

[0082] 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

[0083] Figure 1 The TEM identification results of extracellular vesicles are shown;

[0084] Figure 2 The results of NTA detection of extracellular vesicles are shown;

[0085] Figure 3 The ROC curve for the training cohort is shown;

[0086] Figure 4 ROC curves for the validation cohort are shown. DETAILED DESCRIPTION

[0087] Lung cancer is a malignant tumor with high morbidity and mortality rates worldwide. Early detection of lung cancer has a significant effect on improving the patient's prognosis. However, the current early diagnosis of lung cancer in clinical practice mainly relies on low-dose spiral CT (LDCT). LDCT has high sensitivity but poor specificity, resulting in a high false positive rate. False positives produced by LDCT can lead to overtreatment, bringing additional risks and expenses to patients. This problem can be improved if there are more specific detection methods. Some biomarkers have been used for the early detection of malignant tumors. However, there is currently a lack of effective biomarkers for early detection of lung cancer (such as blood biomarkers) in clinical practice, which can ensure sensitivity while having high specificity.

[0088] In order to solve the technical problems existing in this field, the present invention has carried out the following two parts of research work:

[0089] (1) Study the blood extracellular vesicle microRNAs (miRNAs) biomarkers in lung cancer patients and healthy people, and construct a highly specific and sensitive blood extracellular vesicle miRNA diagnostic model through statistical and machine learning methods;

[0090] (2) Use independent validation cohort study data to evaluate the effectiveness of the diagnostic model in distinguishing lung cancer from healthy subjects.

[0091] Through (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.

[0092] definition

[0093] 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:

[0094] Extracellular vesicles (EVs), also known as exosomes, are small membrane vesicles with a diameter of about 30-150 nm that are secreted into the extracellular environment after the fusion of intracellular multivesicular bodies (MVBs) with the cell membrane. All cell types can release extracellular vesicles, which exist in biological fluids such as blood, urine, cerebrospinal fluid, saliva, milk, and semen. Extracellular vesicles are loaded with a variety of contents, including proteins, lipids, metabolites, mRNA, microRNA (miRNA), etc., and play a vital role in intercellular communication. The stability and easy availability of EVs make them potential biomarkers for precision medicine, especially early tumor screening, diagnosis, and treatment.

[0095] Blood extracellular vesicle miRNA of the present invention

[0096] 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 currently a lack of highly specific and effective blood biomarkers in the clinic, and there is no biomarker combined with LDCT for clinical application to improve the specificity of diagnosis and reduce the false positive rate. Specifically, the current research on biomarkers for early diagnosis of lung cancer has the following shortcomings: a. The number of studies based on the second-generation sequencing platform is small; b. There are few studies on exosome contents as biomarkers for early diagnosis of lung cancer and verified by another set of data; c. The research cohort is NSCLC or SCLC or subtypes such as lung adenocarcinoma and lung squamous cell carcinoma.

[0097] In view of the deficiencies in the art, the inventors used the blood of patients diagnosed with lung cancer by existing detection methods in the clinic as research samples, and healthy people as the control group, and applied the extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. to extract blood extracellular vesicles, and further used the second-generation sequencing technology small RNA sequencing to detect the expression of extracellular vesicle miRNA in the blood of lung cancer patients and healthy people, and discovered and verified the extracellular vesicle miRNA biomarker diagnostic model that can be used for the early diagnosis of lung cancer in two independent populations.

[0098] At present, there are a few reports on the research of blood extracellular vesicle miRNA biomarkers in the early diagnosis of lung cancer. Some researchers used miRNA-Seq technology to analyze the expression differences of plasma exosome miRNA in healthy people and stage I NSCLC patients, and established an exosome miRNA biomarker model that specifically distinguishes patients with lung adenocarcinoma and lung squamous cell carcinoma, of which the AUC of the model for distinguishing lung squamous cell carcinoma was 0.911. However, this study is aimed at the early diagnosis of patients with lung adenocarcinoma and lung squamous cell carcinoma in NSCLC, which is different from the clinical application scenario of the present invention for differentiating and diagnosing various types of lung cancer patients from healthy people. In addition, the biomarker model constructed by this study is less effective than that of the present invention, and the model AUC of the present invention is as high as 0.99. Other researchers have screened exosome miRNA markers associated with early lung cancer. However, in comparison, the diagnostic efficacy of the blood extracellular vesicle miRNA biomarkers of the present invention is better.

[0099] In the present invention, a content in the extracellular vesicles of the blood, i.e., 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 many contents of extracellular vesicles, such as proteins, lipids, small molecule metabolites, lncRNA, circRNA, mRNA, piRNA, etc., and these extracellular vesicle contents of the blood may also construct a biomarker model to achieve an effect similar to miRNA. In addition, various 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, etc., may be used as biomarkers for early diagnosis of lung cancer, for development into independent liquid biopsy technology.

[0100] Specifically, the inventors used small RNA sequencing technology to detect the blood extracellular vesicle miRNA expression profiles of lung cancer patients and healthy people, and applied statistical methods and machine learning methods to discover two blood extracellular vesicle miRNAs hsa-miR-106b-3p and hsa-miR-10b-5p. Based on the two markers discovered above, the inventors further constructed a risk prediction model with high specificity and high sensitivity. The model has a sensitivity of >95% and a specificity of >95% for the diagnosis of early lung cancer.

[0101] 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, and / or a miRNA with a sequence as shown in SEQ ID NO: 2; or a miRNA complementary thereto.

[0102] In a preferred embodiment, the blood extracellular vesicle miRNA of the present invention is a combination of a miRNA having a sequence as shown in SEQ ID NO: 1 and a miRNA having a sequence as shown in SEQ ID NO: 2.

[0103] 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 further can be used to prepare miRNA chips, detection reagents or detection kits for the early diagnosis of lung cancer.

[0104] 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 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 may contain: a complementary binding region; and / or a connection region connected to the solid phase carrier.

[0105] In a specific embodiment, the detection kit contains a detection reagent for detecting the blood extracellular vesicle miRNA of the present invention, or the miRNA chip.

[0106] Based on the teachings of the present invention and the common knowledge in the field of miRNA, a person skilled in the art can think of an isolated or artificially constructed precursor miRNA, which can be cleaved and expressed in human cells as the blood extracellular vesicle miRNA of the present invention. Further, a person skilled in the art can also think of an isolated polynucleotide, which can be transcribed by human cells into the precursor miRNA, which can be cleaved and expressed in human cells as the miRNA of claim 6.

[0107] In a specific embodiment, the polynucleotide has a structure shown in Formula I:

[0108] Seq 正向 -X-Seq 反向 Formula I

[0109] 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;

[0110] After the structure shown in Formula I is transferred into human cells, a secondary structure shown in Formula II is formed:

[0111]

[0112] In Formula II, Seq forward, Seq reverse and X are defined as above, and || represents a base complementary pairing relationship formed between Seq forward and Seq reverse.

[0113] Based on the teachings of the present invention and the common knowledge in the field of miRNA, those skilled in the art can also conceive of a vector containing the miRNA or polynucleotide.

[0114] Method of the present invention and device for implementing the method of the present invention

[0115] The extracellular vesicle miRNA discovered by the inventors can be used for early diagnosis of lung cancer. Therefore, the inventors also provide a method for early diagnosis of lung cancer using the extracellular vesicle miRNA in blood, comprising the following steps:

[0116] (a) detecting the expression level of miRNA in a sample of a subject to be tested;

[0117] (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;

[0118] (c) Determine whether the subject is a healthy subject or a lung cancer patient based on the probability obtained in step (b).

[0119] The early lung cancer diagnosis method of the present invention has a sensitivity of >95% and a specificity of >95% for early lung cancer diagnosis.

[0120] Based on the understanding of the technical content 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.

[0121] In a specific implementation, the model formula of the risk prediction model is as follows:

[0122]

[0123] 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.

[0124] To implement the diagnostic method of the present invention, the inventors also provide a device for early diagnosis of lung cancer, wherein the device comprises a storage device storing instructions for executing the method for early diagnosis of lung cancer of the present invention.

[0125] 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 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.

[0126] Advantages of the present invention:

[0127] The technical solution of the present invention incorporates samples of various types of lung cancer patients in the real world, and uses the independently developed extraction reagent L3525 to extract blood extracellular vesicles. Subsequently, small RNA sequencing is used to detect the expression of blood extracellular vesicle miRNA, and finally a highly specific blood extracellular vesicle miRNA biomarker is discovered and verified.

[0128] 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.

[0129] 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.

[0130] Example

[0131] Material

[0132] The materials used in the following examples are all commercially available.

[0133] method

[0134] 1. Study cohort and clinical information

[0135] A total of 71 patients diagnosed with lung cancer by existing clinical detection methods were included. Blood samples were collected from patients before surgery, and blood samples from 32 healthy individuals were collected. A total of 103 samples were finally included. 72 samples were randomly selected as the training cohort, including 50 lung cancer patients and 22 healthy individuals. The validation cohort consisted of 31 cases, including 21 lung cancer patients and 10 healthy individuals.

[0136] 2. Extraction and Characterization of Extracellular Vesicles from Blood

[0137] 1) Blood collection and extracellular vesicle extraction

[0138] Blood samples from lung cancer patients and healthy people included in this study were collected in 10mL anticoagulation vacuum blood collection tubes (REF367525, BD, USA) before surgery, slowly inverted several times and placed vertically, and then separated by two-step centrifugation. First, the sample was centrifuged at 1600g for 10min 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. The supernatant was then transferred to a 1.5mL EP tube and centrifuged at 16000g for 15min at 4°C to remove residual cell debris. The supernatant was divided into 1.5mL EP tubes and stored in a -80°C refrigerator for later use.

[0139] 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, the mixture was incubated at 4°C for 30 min, centrifuged at 4700g for 30 min at 4°C, the supernatant was discarded, and 200μL PBS (Phosphate Buffer Saline) was added to resuspend the extracellular vesicles.

[0140] 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:

[0141] 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.

[0142] 2) Characteristics of blood extracellular vesicles

[0143] 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.

[0144] 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 copper grid was first rinsed twice with PBS, then washed with PBS containing 50mM 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.

[0145] 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 small extracellular vesicles were analyzed using NTA version 2.3 analysis software.

[0146] 3. Extraction of plasma extracellular vesicle miRNA

[0147] 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).

[0148] 4. Detection of plasma extracellular vesicle miRNA expression levels

[0149] 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 (740609.250, MN, Germany) kit, 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 chips and reagents Agilent High Sensitivity DNA Kit & Reagents (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.

[0150] In addition to the above methods, the following alternative methods can also be used to detect the expression level of blood extracellular vesicle miRNA: Q-PCR method; miRNA chip detection; third-generation sequencing technology.

[0151] 5. Sequencing data analysis process

[0152] Based on small RNA sequencing detection technology, the expression of miRNA in extracellular vesicles in the blood of lung cancer patients and healthy people was obtained. The analysis process of sequencing data is as follows:

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 6. Discovery of biomarkers

[0158] 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:

[0159] 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.

[0160] 2) Statistical methods for screening 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.

[0161] 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 are discarded to obtain the remaining 8 markers. Subsequently, a total of 6 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 weight of the 8 markers as a whole on the training cohort 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, 2 markers were obtained as candidate markers for subsequent analysis.

[0162] In addition to the model building methods used above, other alternatives include but are not limited to: complex feature engineering, support vector machines, and deep learning.

[0163] 7. Risk scoring model construction

[0164] Taking healthy individuals and lung cancer as the classification prediction targets, using the two markers discovered in (6), a total of 6 machine learning algorithms were used, including gradient boosting machine (GBM), extreme randomized trees, linear model, neural network, random forest 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 cohort sample data. The model formula is as follows:

[0165]

[0166] 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.

[0167] 8. Evaluation and Validation of Risk Scoring Model Performance

[0168] 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.

[0169] 9. Application of risk scoring model

[0170] 1) Collect peripheral blood from patients suspected of lung cancer in clinical diagnosis, obtain peripheral blood extracellular vesicles, and use small RNA sequencing to obtain the expression level of biomarkers;

[0171] 2) The obtained blood extracellular vesicle hsa-miR-106b-3p and hsa-miR-10b-5p expression values ​​were put into the trained model formula to calculate the probability of each patient being predicted to be healthy and lung cancer;

[0172] 3) Select the output result with the highest probability and give the predicted result of lung cancer risk for each patient.

[0173] Example 1

[0174] Two research cohorts were recruited in this example. The specific research cohorts and clinical information are as follows:

[0175] In this example, a total of 71 lung cancer patients were selected into the research cohort, and blood samples were collected from the patients before surgery. In addition, blood samples from 32 healthy individuals were collected, and a total of 103 samples were finally included in the group. 72 samples were randomly selected as the training cohort, and the remaining 31 samples were used as the validation cohort. The specific grouping information is shown in Table 1 below.

[0176]

[0177] Example 2. Extraction and characterization of extracellular vesicles from blood

[0178] In this example, the extracellular vesicle (exosome) extraction reagent L3525 (3DMed, Shanghai) independently developed by Shanghai Silidi 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 114.9 nm, which was consistent with the particle size distribution of extracellular vesicles (see Figure 2 ).

[0179] Example 3. Biomarker Discovery

[0180] In this embodiment, 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 included hsa-miR-106b-3p, hsa-miR-10b-5p, hsa-miR-128-3p, hsa-miR-151a-3p, hsa-miR-16-2-3p, hsa-miR-192-5p, hsa-miR-345-5p and hsa-miR-584-5p, a total of 8, for subsequent analysis. 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 mark markers with feature weights <5% as candidate discarded markers. If a marker was marked as discarded by all algorithms, it was not retained, and a total of 2 markers were obtained as candidate markers, including hsa-miR-106b-3p (SEQ ID NO: 1, CCGCACUGUGGGUACUUGCUGC) and hsa-miR-10b-5p (SEQ ID NO: 2, UACCCUGUAGAACCGAAUUUGUG), which were used to subsequently construct risk scoring models for lung cancer and healthy people.

[0181] Example 4. Risk scoring model for lung cancer and healthy individuals

[0182] 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 included 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 were 0.998 ( Figure 3 ), 0.98, 1, 0.9333, 1 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).

[0183] Table 2. Results of cross-validation evaluation of biomarkers in the training cohort

[0184]

[0185]

[0186] Example 5. Validation of the predictive performance of the risk scoring model for lung cancer and healthy individuals

[0187] 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 0.9905 ( Figure 4 ), 0.9762, 1, 0.9091, 1 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).

[0188] Table 3. Results of the self-sampling method for biomarker assessment in the validation cohort

[0189]

[0190]

[0191] 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 combination of a miRNA with a sequence as shown in SEQ ID NO: 1 and a miRNA with a sequence as shown in SEQ ID NO: 2; 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 combination of a miRNA with a sequence as shown in SEQ ID NO: 1 and a miRNA with a sequence as shown in SEQ ID NO: 2; 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 combination of a miRNA having a sequence as shown in SEQ ID NO: 1, and a miRNA having a sequence as shown in SEQ ID NO: 2; or (ii) a miRNA that is 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 combination of a miRNA having a sequence as shown in SEQ ID NO: 1, and a miRNA having a sequence as shown in SEQ ID NO: 2; 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 subject to be tested; (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 combination of a miRNA having a sequence as shown in SEQ ID NO: 1, and a miRNA having a sequence as shown in SEQ ID NO: 2; 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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