Application of blood small extracellular vesicle microRNA panel in the diagnosis and differential diagnosis of lung cancer

Abstract

The present invention discloses blood extracellular vesicle miRNAs. The miRNAs hsa-miR-106b-3p and hsa-miR-10b-5p can be used as biomarkers for the early diagnosis of lung cancer, thereby enabling the preparation of chips, detection reagents, or detection kits for early lung cancer diagnosis. The blood extracellular vesicle miRNA biomarkers of the present invention have high sensitivity and specificity for the early diagnosis of lung cancer, thereby reducing the false positive rate caused by traditional diagnostic methods and having potential 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, seriously endangering human health. Lung cancer is roughly divided into two major histological 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 in locally advanced or metastatic disease at the time of diagnosis, 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 patients' prognosis.

[0003] Currently, the main methods used in clinical practice for the early diagnosis of lung cancer include imaging examinations (CT, PET-CT, etc.), pathological examinations, and laboratory tests, but these diagnostic methods all have certain limitations. Low-dose computed tomography (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 procedures with the risk of pneumothorax and bleeding. Laboratory tests such as sputum 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 exosomal miRNAs in patients with indeterminate pulmonary nodules (IPNs). Using the LASSO method, they 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) 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 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 suggest that serum / plasma exosomal miRNAs can serve as potential biomarkers for the 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 that 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 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, comprising:

[0015] a solid support; and

[0016] Oligonucleotide probes are sequentially fixed on the solid support, wherein 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 a kit for early diagnosis of lung cancer.

[0027] In a fourth aspect, the present invention provides a detection kit comprising 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 described in the second aspect.

[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 of 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 The spacer sequence between the two sequences is the same as Seq 正向 and Seq 反向 Not complementary;

[0049] After the structure shown in Formula I is introduced into human cells, it forms the secondary structure shown in Formula II:

[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 comprising 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), extremely randomized trees, 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, wherein the apparatus comprises a storage device storing instructions for executing the method for early diagnosis of lung cancer according to the ninth aspect.

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

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

[0071] (b) a computing device that inputs the miRNA expression value detected by the device (a) into a risk prediction model to obtain a 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 the 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), extremely randomized trees, 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 described in detail below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be listed here one by one. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 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 patients' prognosis. However, currently, 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-positive results from LDCT can lead to overtreatment, bringing additional risks and expenses to patients. This problem can be improved if more specific detection methods are available. Some biomarkers have been used for the early detection of malignant tumors. However, there is currently a lack of effective biomarkers for the early detection of lung cancer (such as blood biomarkers) in clinical practice that can ensure sensitivity while also 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 subjects, and construct a highly specific and sensitive blood extracellular vesicle miRNA diagnostic model using 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 those commonly understood by those skilled in the art. To facilitate understanding of the present invention, the following explanations and definitions of the relevant terms are provided:

[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 are present 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] Currently, LDCT, widely used in clinical practice for the early diagnosis of lung cancer, has high sensitivity but poor specificity and a high false-positive rate. There is currently a lack of highly specific and effective blood biomarkers, nor is there a combination of biomarkers and LDCT for clinical application to improve diagnostic specificity and reduce false-positive rates. Specifically, current research on biomarkers for the early diagnosis of lung cancer has the following shortcomings: a. The number of studies based on next-generation sequencing platforms is small; b. Studies using exosome contents as biomarkers for early diagnosis of lung cancer that have been validated by additional data sets are limited; and c. The study cohorts are limited to subtypes such as NSCLC, SCLC, or lung adenocarcinoma and squamous cell carcinoma.

[0097] To address the shortcomings in this field, the inventors used blood from patients diagnosed with lung cancer using existing detection methods in clinical practice as research samples, and healthy people as a control group. They used the extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. to extract extracellular vesicles from the blood. They further used second-generation sequencing technology smallRNA sequencing to detect the expression of extracellular vesicle miRNAs in the blood of lung cancer patients and healthy people. In these two independent populations, they discovered and validated an extracellular vesicle miRNA biomarker diagnostic model that can be used for the early diagnosis of lung cancer.

[0098] Currently, there are a few reports on the use of blood extracellular vesicle miRNA biomarkers in the early diagnosis of lung cancer. Using miRNA-Seq technology, researchers analyzed the differential expression of plasma exosomal miRNAs between healthy controls and stage I NSCLC patients, establishing an exosomal miRNA biomarker model that specifically distinguishes between patients with lung adenocarcinoma and squamous cell carcinoma. The model achieved an AUC of 0.911 for distinguishing between patients with squamous cell carcinoma. However, this study focused on the early diagnosis of patients with lung adenocarcinoma and squamous cell carcinoma in NSCLC, a different clinical application scenario from the present invention, which aims to differentiate between patients with various types of lung cancer in healthy controls. Furthermore, the biomarker model constructed in this study performed less effectively than the present invention, which boasts an AUC of 0.99. Other researchers have also identified exosomal miRNA markers associated with early-stage lung cancer. However, the present invention's blood extracellular vesicle miRNA biomarkers demonstrate superior diagnostic efficacy.

[0099] In the present invention, a content in the extracellular vesicles of the blood, namely 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 that of 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 and used to develop into an independent liquid biopsy technology.

[0100] Specifically, the inventors used small RNA sequencing technology to examine the expression profiles of extracellular vesicle miRNAs in the blood of lung cancer patients and healthy controls. Applying statistical and machine learning methods, they discovered two extracellular vesicle miRNAs, hsa-miR-106b-3p and hsa-miR-10b-5p. Based on these two markers, the inventors further constructed a risk prediction model with high specificity and sensitivity. The model has a sensitivity of >95% and a specificity of >95% for the diagnosis of early-stage 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 can further 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 support; and oligonucleotide probes sequentially immobilized on the solid support, wherein the oligonucleotide probes specifically bind to the blood extracellular vesicle miRNAs of the present invention. Based on conventional techniques in the art, those skilled in the art will understand how to design the oligonucleotide probes. For example, the oligonucleotide probes may comprise: a complementary binding region; and / or a linker region connected to the solid support.

[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 common knowledge in the miRNA field, those skilled in the art can conceive of an isolated or artificially constructed precursor miRNA that can be cleaved and expressed in human cells into the blood extracellular vesicle miRNA of the present invention. Furthermore, those skilled in the art can also conceive of an isolated polynucleotide that can be transcribed by human cells into the precursor miRNA, and the precursor miRNA can be cleaved and expressed in human cells into the miRNA.

[0107] In a specific embodiment, the polynucleotide has the 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 in Seq 正向 and Seq 反向 The spacer sequence between the two sequences is the same as Seq 正向 and Seq 反向 Not complementary;

[0110] After the structure shown in Formula I is introduced into human cells, it forms the secondary structure shown in Formula II:

[0111]

[0112] In Formula II, Seq forward, Seq reverse and X are as defined 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 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 present inventors can be used for the early diagnosis of lung cancer. Therefore, the present 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, namely that specific blood extracellular vesicle miRNAs can be used for the early diagnosis of lung cancer, those skilled in the art can use various methods, such as various algorithms, to construct a lung cancer risk prediction model. In specific embodiments, 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), extremely randomized trees, linear models, neural networks, random forests, and / or ensemble methods.

[0121] In a specific embodiment, 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. 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 this invention incorporated real-world samples from various types of lung cancer patients and extracted blood extracellular vesicles using the independently developed extraction reagent L3525. Subsequently, small RNA sequencing was used to detect the expression of blood extracellular vesicle miRNAs, ultimately discovering and validating a highly specific blood extracellular vesicle miRNA biomarker.

[0128] The blood extracellular vesicle miRNA biomarker and early lung cancer diagnosis method of the present invention have high sensitivity and high specificity for the diagnosis of early lung cancer.

[0129] The present invention will be further described below with reference to 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, for which specific conditions are not specified, were generally performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 2001), or according to the 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 using existing clinical tests were enrolled. Blood samples were collected preoperatively from these patients, and blood samples were collected from 32 healthy individuals, for a total of 103 samples. A training cohort of 72 samples was randomly selected, consisting of 50 lung cancer patients and 22 healthy individuals. The validation cohort consisted of 31 patients, 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 controls enrolled in this study were collected before surgery in 10 mL anticoagulant vacutainer tubes (REF367525, BD, USA). The tubes were gently inverted several times and then placed upright. Plasma samples were then separated by a two-step centrifugation procedure. First, the samples were centrifuged at 1600 g for 10 minutes at 4°C. After centrifugation, the hemolysis grade of the samples was determined according to a standard, and samples with a hemolysis grade of less than 4 were used for subsequent studies. The supernatant was then transferred to a 1.5 mL EP tube and centrifuged at 16,000 g for 15 minutes at 4°C to remove residual cell debris. The supernatant was then aliquoted into 1.5 mL EP tubes and stored at −80°C until further use.

[0139] Frozen plasma samples were thawed in a 37°C water bath and centrifuged at 12,000 g for 10 min at 4°C. The supernatant was filtered sequentially through a 0.45 μm filter column (CLS8163-100EA, Corning, USA) and then a 0.22 μm filter column (CLS8161-100EA, Corning, USA). The supernatant was centrifuged at 12,000 g for 5 min at 4°C and the filtrate was collected into a 2 mL EP tube. The volume of the filtrate was measured, and 1 / 4 volume of L-type exosome precipitation reagent (L3525, 3DMed, Shanghai) was added. After thorough mixing, the tube was incubated at 4°C for 30 min and centrifuged at 4,700 g for 30 min at 4°C. The supernatant was discarded, and the extracellular vesicles were resuspended in 200 μL of PBS (Phosphate Buffer Saline).

[0140] In addition to using the self-developed extracellular vesicle (exosome) extraction reagent L3525 (3DMed, Shanghai), the following alternatives can be used to extract extracellular vesicles from blood:

[0141] Centrifugation (differential centrifugation, density gradient centrifugation), precipitation (PEG precipitation, organic solvent precipitation), particle size separation (ultrafiltration, size exclusion chromatography), immunoaffinity, microfluidics, and other commercial exosome isolation kits. Direct use of commercial exosome isolation kits is preferred.

[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 transmission electron microscope (TEM) to detect the morphology of extracellular vesicles and nanoparticle tracking analysis (NTA) to detect the particle size distribution of extracellular vesicles.

[0144] TEM examination: Extracellular vesicles were resuspended in PBS and fixed with 4% paraformaldehyde. The fixed extracellular vesicles were then dropped onto carbon-coated copper grids. After incubation at room temperature for 5 minutes, the grids were 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, the grids were observed and photographed using a transmission electron microscope (H-7650, Hitachi, Japan).

[0145] NTA detection: First, the plasma extracellular vesicles were diluted with PBS to 1*10^ 7 -1*10^ 9 / mL and pipette to mix thoroughly. Subsequently, the NTA instrument (NanoSight NS300, Malvern, UK) was turned on and the sample was injected into the sample chamber. A 488 nm excitation module was used, and the camera lens parameters were set to a shutter value of 890, a gain of 146, and a detection threshold of 7. At least 200 complete tracks were analyzed and acquired 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 miRNA from plasma extracellular vesicles

[0147] Total RNA was extracted from plasma extracellular vesicles using the miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai) according to the product instructions. RNA was eluted with 15 μL of RNase-free water. The concentration and fragment distribution of miRNAs were determined using an Agilent 2100 Bioanalyzer and the accompanying small RNA analysis kit (5067-1548, Agilent).

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

[0149] miRNA libraries were prepared using the NEBNext Multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA) according to the product instructions. Six μL of each RNA sample was loaded, followed by ligation of 3' end adapters, hybridization with reverse transcription primers, ligation of 5' end adapters, reverse transcription, and 18 cycles of PCR amplification. PCR-enriched products were purified using the NucleoSpin Geland PCR Clean-up Kit (740609.250, MN, Germany), and the library DNA was eluted with 30 μL of nuclease-free water. DNA concentration was quantified using an Invitrogen Qubit 4.0 Fluorometer and the accompanying Qubit dsDNA HSA Assay Kit (Q32854, Thermofisher, USA). The distribution of library DNA fragments was analyzed using an Agilent 2100 Bioanalyzer using the accompanying chip and reagents, Agilent High Sensitivity DNA Kit & Reagents (5067-4626, Agilent, USA). 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] Using small RNA sequencing technology, we determined the expression levels of miRNAs in extracellular vesicles from the blood of lung cancer patients and healthy controls. The analysis process for the 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 using BWA software (version: 0.7.12-r1039) (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ) and count the number of reads aligned to miRNA.

[0154] 2) miRNA annotation. 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 30 nt or less and covered by at least 10 reads in at least g samples in the training cohort data 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 from the training cohort were retained for subsequent analysis.

[0156] 4) Normalization of miRNA expression levels. The raw miRNA expression levels of the training cohort samples were normalized using the trimmed mean of M-values ​​(TMM) method, and the same parameters were used for the validation cohort samples.

[0157] 6. Discovery of biomarkers

[0158] Based on the expression levels of miRNAs in the training cohort, samples were grouped according to pathological test results, and statistical and machine learning methods were used to discover miRNAs that can be used to distinguish lung cancer patients from healthy controls as biomarkers. The process is as follows:

[0159] 1) Grouping of the training cohort: Based on the pathological test results, the samples in the training cohort were divided into two groups: healthy individuals and lung cancer.

[0160] 2) Statistical methods for biomarker screening. First, the U-test and T-test were used to analyze the expression differences of all miRNAs between the two groups. Mutual information (MI) between all miRNA expression and sample groups was calculated. MiRNAs with a P-value ≤ 0.1 between the groups and a mutual information score in the top 10% of all markers were selected. Correlation coefficients were then calculated between these miRNAs. Among miRNAs with correlation coefficients > 0.8, only those with the largest variance were retained as candidate biomarkers for subsequent analysis.

[0161] 3) Machine learning method for biomarker screening. The miRNAs screened in 2) were used as initial biomarkers and were first screened using three machine learning feature screening methods. Method 1: The classification performance of each marker for the sample was evaluated separately, and markers with a performance score <0.8 were marked as candidate markers for discarding. Method 2: All initial markers were first evaluated for classification performance on the sample as a whole. The expression levels of the markers in the sample were then randomly shuffled in sequence. The degree of reduction in classification performance score after the shuffle was calculated for each marker. Markers with a performance score reduction of <0.01 were marked as candidate markers for discarding. Method 3: All initial markers were first evaluated for classification performance on the sample as a whole. The markers were ranked by importance. Starting with the most important marker, each marker was added and the improvement in model score was evaluated. Markers with a score improvement of <0.01 were marked as candidate markers for discarding. The intersection of the candidate markers obtained by the above three methods was discarded, resulting in the remaining eight markers. Subsequently, six machine learning algorithms, including gradient boosting machines (GBMs), extreme randomized trees, linear models, neural networks, random forests, and ensemble methods, were used to evaluate the classification performance and feature weights of the eight markers as a whole on the training cohort data. Markers with feature weights <5% were marked as candidate markers for discarding. If a marker was marked as discarded by all algorithms, it was not retained. Combining these methods, two markers were identified 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] With healthy individuals and lung cancer as the classification prediction targets, the two markers discovered in (6) were used, and six machine learning algorithms were adopted: 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 for 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 for sample i. The trained model is saved as a file on the hard drive. When calling the model, input the sample marker expression value to obtain the model prediction result.

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

[0168] The model's classification performance and feature weights were evaluated using a 5-fold cross-validation method in the training cohort and in the validation cohort. Model evaluation and validation metrics included the area under the receiver operating characteristic curve (AUC, range 0 to 1), accuracy (range 0 to 1), positive predictive value (range 0 to 1), and negative predictive value (range 0 to 1). Model specificity (range 0 to 1) and sensitivity (range 0 to 1) were also assessed; higher values ​​indicate better model classification performance.

[0169] 9. Application of risk scoring models

[0170] 1) Peripheral blood was collected from patients suspected of having lung cancer, and extracellular vesicles were obtained from the peripheral blood. The expression levels of biomarkers were determined using small RNA sequencing.

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

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

[0173] Example 1

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

[0175] In this example, a total of 71 lung cancer patients were enrolled in the study cohort, and blood samples were collected from these patients before surgery. In addition, blood samples were collected from 32 healthy individuals, for a total of 103 samples. 72 of these samples were randomly selected as the training cohort, and the remaining 31 as the validation cohort. 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 used TEM to detect the morphology of the extracellular vesicles. The results showed that the extracellular vesicles showed a typical "horseshoe-shaped" morphology (see Figure 1 The particle size distribution of extracellular vesicles was detected by NTA. 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 example, small RNA sequencing was used to detect the expression levels of extracellular vesicle miRNAs in the blood of lung cancer patients and healthy controls. Statistical methods (U test and T test) were used to analyze the expression differences of all miRNAs between lung cancer patients and healthy controls, as well as the correlation coefficients between miRNAs. Simultaneously, three machine learning feature screening methods were used to screen miRNAs that can be used to distinguish lung cancer patients from healthy controls as biomarkers. Eight candidate molecular markers, including 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, were selected 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. A total of two 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 subjects.

[0181] Example 4. Lung cancer and healthy individual risk scoring model

[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 examination, 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 of 0 to 1), accuracy (range of 0 to 1), positive predictive value (range of 0 to 1), negative predictive value (range of 0 to 1), specificity (range of 0 to 1) and sensitivity (range of 0 to 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 high AUC, accuracy, positive predictive value, negative predictive value, specificity, and sensitivity, and the model had excellent predictive performance (see Table 2).

[0183] Table 2. Cross-validation evaluation results of biomarkers in the training cohort

[0184]

[0185]

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

[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 included 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 were 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 high AUC, accuracy, positive predictive value, negative predictive value, specificity, and sensitivity, and the model had excellent predictive performance (see Table 3).

[0188] Table 3. Biomarker self-sampling assessment results in the validation cohort

[0189]

[0190]

[0191] All documents mentioned in this application are incorporated herein by reference, just as if each document were incorporated herein by reference individually. It should also be understood that after reading the above teachings of the present invention, those skilled in the art may make various changes or modifications to the present invention, and that such equivalents also fall within the scope of the claims appended hereto.

Claims

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

2. The use of the miRNA chip is for preparing a kit for early diagnosis of lung cancer, wherein the miRNA chip comprises: solid phase carrier; as well as Oligonucleotide probes are sequentially fixed on the solid support, wherein 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).

3. A device for early diagnosis of lung cancer, the device comprising a storage device storing instructions for executing a method for early diagnosis of lung cancer; The method for early diagnosis of lung cancer comprises 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).

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