Application of miRNA marker of plasma small extracellular vesicles in preparation of product for predicting curative effect of lung cancer immune combined chemotherapy

Abstract

The invention provides an application of a miRNA (micro Ribonucleic Acid) marker of a small plasma extracellular vesicle in preparation of a product for predicting the curative effect of lung cancer immune combined chemotherapy. The miRNA marker of the small plasma extracellular vesicle comprises any one or a combination of at least two of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p. According to the invention, a plurality of sEVs miRNA combinations are modeled, and an effective model containing two or three sEV miRNAs combinations is identified, so that a real response group is determined, and relatively high performance, AUCgt, AUCgt, AUCgt, AUCgt, AUCgt, AUCgt, AUCgt and AUCgt are shown; and 0.9, the screening of potential benefit people of lung adenocarcinoma immune combined chemotherapy from a peripheral blood small extracellular vesicle level is realized.

Description

Technical Field

[0001] The present invention belongs to the field of molecular biological detection, and specifically relates to the application of plasma small extracellular vesicle miRNA markers in the preparation of a product for predicting the efficacy of lung cancer immunotherapy combined with chemotherapy. Background Art

[0002] Lung cancer has become a major threat to human health. In recent years, immunotherapy combined with chemotherapy has become an important interventional therapy for lung cancer. Tumor immunotherapy with immune checkpoint inhibitors represents a major breakthrough in the treatment of lung cancer. Pembrolizumab (pembrolizumab, K drug) as a PD-1 inhibitor has been used in first-line and second-line immunotherapy for lung cancer. Pembrolizumab combined with chemotherapy drugs has a synergistic effect and prolongs the survival of lung cancer patients. Immunotherapy combined with chemotherapy has become an important choice for the clinical treatment of lung cancer patients.

[0003] Several common efficacy prediction biomarkers in immunotherapy-related studies include PD-L1 expression level, tumor mutational burden (TMB) and microsatellite instability (MSI). High-level MSI (MSI-high, MSI-H) is one of the FDA-approved screening markers for immune checkpoint inhibitors (ICIs) in various tumor types (mainly for colorectal cancer), but its incidence is extremely low (<1%) in many studies related to non-small cell lung cancer (NSCLC), making it difficult to be used for NSCLC immune-related efficacy prediction. The expression level of PD-L1 on the surface of tumor cells may affect the efficacy of immune checkpoint inhibitors in lung cancer patients. Among all patients, the objective response rate of pembrolizumab-based immunotherapy strategy for non-small cell lung cancer is 19.4%. Even in the population with the highest PD-L1 expression (≥50%), the response rate of patients is only 45.2%. TMB levels also have many shortcomings, such as high price, long detection time, low success rate, and unclear critical value. In addition, the advantages and disadvantages of tissue tumor mutational burden (tTMB) and blood tumor mutational burden (bTMB) are still controversial. Due to the unclear mechanism of action and practical problems, neither PD-L1 level nor TMB has become a widely selected predictive marker in immunotherapy. Although more and more patients benefit from immunotherapy combined with chemotherapy, effective biomarkers for clinical efficacy evaluation are still lacking. It is crucial to explore predictive biomarkers, accurately screen the responding population to tumor immunotherapy combined with chemotherapy, reduce unnecessary waste of medical resources, avoid potential side effects of non-responders, and improve the therapeutic effect of immunotherapy combined with chemotherapy.

[0004] At present, the imaging examinations (such as chest enhanced CT, chest X-ray, MRI and ultrasound and other auxiliary imaging examinations) and tumor marker tests (such as CEA, NSE, CYFRA21-1, proGRP and SCC-Ag, etc.) widely used in clinical practice have low sensitivity and specificity for lung cancer efficacy detection and are prone to false positives.

[0005] Small extracellular vesicles (sEVs) are biologically active vesicles secreted by multicellular bodies, also known as exosomes, with a diameter of generally 30-150nm and a lipid bilayer membrane structure. The contents of sEVs, such as lipids, proteins, and nucleic acids, play a vital role in intercellular communication. The stability and easy availability of sEVs make them potential biomarkers for precision medicine, especially early tumor screening, diagnosis, and treatment. In addition, sEVs play an important role in tumorigenesis, metastasis, deterioration, and immune escape. Studies have shown that sEVs delivered by tumors interact with stromal cells in the tumor microenvironment, and the information transmission between tumor cells, sEVs, and the tumor microenvironment determines the development and malignancy of the tumor. Small RNAs in sEVs, including microRNAs (miRNAs), have been considered as potential biomarkers for a variety of cancers. However, it is unclear whether sEVs miRNAs can be used as biomarkers to predict the efficacy of immunotherapy combined with chemotherapy.

[0006] At present, there are no reports on the use of highly specific blood small extracellular vesicle miRNA biomarkers for predicting the efficacy of combined immunotherapy and chemotherapy in patients with lung adenocarcinoma; there are only a few studies on predicting the efficacy of combined immunotherapy and chemotherapy in patients with lung adenocarcinoma.

[0007] In CN116574807A, researchers conducted high-throughput sequencing and bioinformatics analysis on plasma extracellular vesicle long RNA (exLRs) in patients with advanced lung adenocarcinoma before and after immunotherapy combined with chemotherapy, combined with treatment response and survival analysis to identify and evaluate efficacy markers, and found that EV-derived CD160 can predict the efficacy of anti-PD-1 immunotherapy combined with chemotherapy for lung adenocarcinoma, and verified it at the RT-qPCR level. A prediction model based on peripheral blood EV-CD160 levels, NLR levels, and bone metastasis was established for predicting the efficacy of immunotherapy combined with chemotherapy in patients with lung adenocarcinoma. However, the markers they discovered in their study had poor predictive performance, with the AUC obtained from ROC curve analysis being only 0.648, and the markers were low in abundance in blood exLRs.

[0008] In CN114220486B, researchers detected the expression of five immune cells and input the expression of the five immune cells into the efficacy prediction model pre-trained by support vector machine to predict the effect of immunotherapy combined with chemotherapy on lung cancer patients. The AUC of the training group and validation group of the vector model were 0.886 and 0.874, respectively, with high prediction performance. However, their research mainly focused on the expression of immune cells and did not involve blood small extracellular vesicle miRNA biomarkers.

[0009] Immunotherapy combined with chemotherapy has become an important option for the clinical treatment of lung cancer patients, and more and more patients have benefited from the immunotherapy combined with chemotherapy treatment regimen. However, biomarkers that effectively identify true responders are still lacking. Therefore, identifying and screening new sEVs miRNA markers has important application value in predicting the therapeutic effect of immunotherapy combined with chemotherapy for lung adenocarcinoma. Summary of the invention

[0010] In view of the shortcomings of the prior art, the purpose of the present invention is to provide the application of plasma small extracellular vesicle miRNA markers in the preparation of a product for predicting the efficacy of combined immunotherapy and chemotherapy for lung cancer. The present invention analyzes the differentially expressed miRNAs (DEMs) in plasma small extracellular vesicles (sEVs) of patients with advanced or metastatic lung adenocarcinoma who receive combined immunotherapy and chemotherapy by small RNA sequencing. A variety of sEVs miRNA combinations were modeled, and an effective model containing 2 or 3 sEVmiRNAs combinations was identified to determine the true responding population, and a high predictive performance (AUC>0.9) was shown, which achieved the screening of potential beneficiaries of combined immunotherapy and chemotherapy for lung adenocarcinoma from the level of peripheral blood small extracellular vesicles.

[0011] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0012] In the first aspect, the present invention provides the use of plasma small extracellular vesicle miRNA markers in the preparation of a product for predicting the efficacy of combined immunotherapy and chemotherapy for lung cancer, wherein the plasma small extracellular vesicle miRNA markers include: any one of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p, or a combination of at least two of them.

[0013] In the present invention, the blood of patients with advanced or metastatic lung adenocarcinoma who received immunotherapy combined with chemotherapy was used as a research sample, and the small extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Siludi Biomedical Technology Co., Ltd. was used to extract blood exosomes. The second-generation sequencing technology (small RNA sequencing) was further used to detect the expression of small extracellular vesicle miRNA in the blood of the responding population and the non-responding population to the combined treatment, thereby discovering and verifying highly specific small extracellular vesicle miRNA biomarkers that can be used to predict the efficacy of immunotherapy combined with chemotherapy. The miRNA biomarkers can be used to establish a prediction model for the efficacy of immunotherapy combined with chemotherapy.

[0014] Preferably, the plasma small extracellular vesicle miRNA markers include: a combination of at least two of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p.

[0015] In the present invention, multiple sEVs miRNA combinations were modeled, and an effective model containing 2 or 3 sEVmiRNAs combinations was identified to determine the true responding population, and showed high performance (AUC>0.9), achieving the screening of potential beneficiaries of lung adenocarcinoma immunotherapy combined with chemotherapy from the level of small extracellular vesicles in peripheral blood.

[0016] Preferably, the lung cancer immunotherapy combined with chemotherapy efficacy prediction product predicts that the subject is a responder to lung cancer immunotherapy combined with chemotherapy, or predicts that the subject is a non-responder to lung cancer immunotherapy combined with chemotherapy based on the expression level of the plasma small extracellular vesicle miRNA marker.

[0017] Preferably, compared with the non-response group, the subject whose expression level of miR-96-5p or miR-6815-5p is increased; or, the subject whose expression level of any one or a combination of at least two of miR-99b-3p, miR-100-5p, miR-193a-5p or miR-320d is decreased is a responder to combined immunotherapy and chemotherapy for lung cancer.

[0018] Preferably, the immunotherapy in the lung cancer immunotherapy combined with chemotherapy includes PD-1 inhibitor therapy, and the chemotherapy includes pemetrexed and / or platinum chemotherapy.

[0019] Preferably, the PD-1 inhibitor is selected from pembrolizumab.

[0020] Preferably, the platinum is selected from carboplatin.

[0021] Immunotherapy combined with chemotherapy has become an important choice for the clinical treatment of lung cancer patients. More and more patients have benefited from the immunotherapy combined with chemotherapy treatment regimen. However, biomarkers that effectively identify true responders are still lacking. In the present invention, based on the expression of miRNA in plasma small extracellular vesicles of lung cancer patients, the samples are grouped according to the imaging evaluation results after immunotherapy combined with chemotherapy treatment, and statistical methods are used to explore plasma small extracellular vesicle miRNAs that can be used for lung cancer immunotherapy combined chemotherapy treatment efficacy evaluation and benefit population screening as biomarkers. The plasma small extracellular vesicle miRNA marker combination is obtained by screening after receiving the "pembrolizumab (K drug, PD-1 inhibitor) + pemetrexed + carboplatin" treatment regimen, which is a biomarker that effectively identifies true responders.

[0022] Preferably, the plasma small extracellular vesicle miRNA marker is a combination of miR-320d and miR-96-5p;

[0023] Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-193a-5p and miR-99b-3p;

[0024] Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-100-5p, miR-6815-5p and miR-96-5p;

[0025] Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-100-5p, miR-6815-5p and miR-193a-5p;

[0026] Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-96-5p, miR-193a-5p and miR-320d;

[0027] Alternatively, the plasma small extracellular vesicle miRNA marker is a combination of miR-99b-3p, miR-100-5p and miR-193a-5p.

[0028] In the present invention, the risk scoring models established with the above 6 combinations all showed good predictive performance and can be used to screen potential beneficiaries of lung cancer immunotherapy combined with chemotherapy. Among them, the risk scoring model established with miR-99b-3p+miR-100-5p+miR-193a-5p or miR-193a-5p+miR-99b-3p had the best predictive performance and can be used to accurately screen potential beneficiaries of lung cancer immunotherapy combined with chemotherapy.

[0029] In a second aspect, the present invention provides a method for screening plasma small extracellular vesicle miRNA markers, the screening method comprising:

[0030] Sample grouping: Based on the imaging evaluation results after lung cancer immunotherapy combined with chemotherapy, the subjects who underwent lung cancer immunotherapy combined with chemotherapy were divided into a response group and a non-response group;

[0031] Screening of miRNA markers: Statistical methods were used to analyze miRNAs with significant expression differences between the response group and the non-response group before lung cancer immunotherapy and chemotherapy; miRNAs with expression CPM greater than 32, a change of more than 1.5 times between the two groups, and a test result of P≤0.05 were selected as candidate molecular markers.

[0032] Preferably, the immunotherapy in the lung cancer immunotherapy combined with chemotherapy includes PD-1 inhibitor therapy, and the chemotherapy includes pemetrexed and / or platinum chemotherapy.

[0033] Preferably, the PD-1 inhibitor is selected from pembrolizumab.

[0034] Preferably, the platinum is selected from carboplatin.

[0035] In the present invention, plasma small extracellular vesicle small RNA sequencing strategy was adopted to discover for the first time 6 lung cancer plasma small extracellular vesicle miRNAs, including: miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d and miR-6815-5p, and the above miRNAs can be used as biomarkers to identify the benefit population of lung cancer immunotherapy combined with chemotherapy. Among them, patients with increased expression levels of miR-96-5p and miR-6815-5p or decreased expression levels of miR-99b-3p, miR-100-5p, miR-193a-5p and miR-320d in the above biomarkers can benefit from immunotherapy combined with chemotherapy.

[0036] In a third aspect, the present invention provides the use of a detection reagent for the expression level of plasma small extracellular vesicle miRNA markers in the preparation of a product for predicting the efficacy of lung cancer combined with immunotherapy and chemotherapy, wherein the detection reagent comprises: a qRT-PCR reagent for detecting any one or a combination of at least two of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p.

[0037] Preferably, the reagents for qRT-PCR include primers and probes, and the primers include stem-loop reverse transcription primers, forward primers and reverse primers.

[0038] Preferably, the stem-loop reverse transcription primer comprises any one of SEQ ID NO: 1, 5, 8, 11, 14, 17 or 20, or a combination of at least two thereof.

[0039] Preferably, the forward primer comprises any one of SEQ ID NO: 2, 6, 9, 12, 15, 18 or 21, or a combination of at least two thereof.

[0040] Preferably, the reverse primer comprises SEQ ID NO: 4 and / or 23.

[0041] Preferably, the detection reagent also includes: a reagent for extracting plasma small extracellular vesicles and / or plasma small extracellular vesicle miRNA.

[0042] In the present invention, the plasma small extracellular vesicles are extracted using the small extracellular vesicle (exosome) separation reagent L3525 independently developed by Shanghai Siludi Biomedical Technology Co., Ltd. In addition, methods for extracting plasma small extracellular vesicles also include: centrifugation (differential centrifugation, density gradient centrifugation), precipitation (PEG precipitation, organic solvent precipitation), particle size separation (ultrafiltration, exclusion chromatography), immunoaffinity, microfluidics and other commercial exosome separation kits.

[0043] Preferably, the immunotherapy in the lung cancer immunotherapy combined with chemotherapy includes PD-1 inhibitor therapy, and the chemotherapy includes pemetrexed and / or platinum chemotherapy.

[0044] Preferably, the PD-1 inhibitor is selected from pembrolizumab.

[0045] Preferably, the platinum is selected from carboplatin.

[0046] In a fourth aspect, the present invention provides a lung cancer immunotherapy combined with chemotherapy efficacy prediction model, wherein the calculation formula of the prediction model includes:

[0047] Risk score = 1.3667 + 0.1051 × (miR-320d) – 0.1641 × (miR-96-5p);

[0048] or, risk score = -0.1994 + 0.1122 × (miR-193a-5p) + 0.0590 × (miR-99b-3p);

[0049] or, risk score = 2.2004 + 0.0573 × (miR-100-5p)–0.1826 × (miR-6815-5p)–0.1230 × (miR-96-5p);

[0050] or, risk score = 0.8594 + 0.0397 × (miR-100-5p) + 0.0966 × (miR-6815-5p) – 0.1917 × (miR-193a-5p);

[0051] or, risk score = 0.9107 + 0.0510 × (miR-193a-5p) + 0.0943 × (miR-320d) – 0.1584 × (miR-96-5p);

[0052] or, risk score = -0.6055 + 0.1197 × (miR-193a-5p) + 0.0311 × (miR-100-5p) + 0.0564 × (miR-99b-3p);

[0053] In the above calculation formula, miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p refers to the expression level of the corresponding plasma small extracellular vesicle miRNA marker.

[0054] Preferably, the efficacy of combined immunotherapy and chemotherapy for lung cancer is judged according to the risk score value of the prediction model.

[0055] Preferably, the judgment standard is: the higher the risk score, the worse the therapeutic effect of the subject receiving lung cancer immunotherapy combined with chemotherapy.

[0056] In a fifth aspect, the present invention provides a method for constructing a lung cancer immunotherapy combined with chemotherapy efficacy prediction model, the method comprising:

[0057] Data acquisition: obtaining the expression level of plasma small extracellular vesicle miRNA markers of the subjects before lung cancer immunotherapy combined with chemotherapy; the plasma small extracellular vesicle miRNA markers include: any one of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p or a combination of at least two;

[0058] Model construction: Based on the expression levels of plasma small extracellular vesicle miRNA markers, an efficacy prediction model was established using any of the analysis methods, including minimum absolute shrinkage and selection operators, support vector machines, neural networks, or random forests.

[0059] Preferably, in the model construction, the LASSO prediction models and risk scores of different miRNAs combinations are analyzed using the glmnet v4.0.2 software package using the least absolute shrinkage and selection operator, and the prediction models with AUC>0.9 are retained.

[0060] Preferably, the calculation formula of the prediction model includes:

[0061] Risk score = 1.3667 + 0.1051 × (miR-320d) – 0.1641 × (miR-96-5p);

[0062] or, risk score = -0.1994 + 0.1122 × (miR-193a-5p) + 0.0590 × (miR-99b-3p);

[0063] or, risk score = 2.2004 + 0.0573 × (miR-100-5p)–0.1826 × (miR-6815-5p)–0.1230 × (miR-96-5p);

[0064] or, risk score = 0.8594 + 0.0397 × (miR-100-5p) + 0.0966 × (miR-6815-5p) – 0.1917 × (miR-193a-5p);

[0065] or, risk score = 0.9107 + 0.0510 × (miR-193a-5p) + 0.0943 × (miR-320d) – 0.1584 × (miR-96-5p);

[0066] or, risk score = -0.6055 + 0.1197 × (miR-193a-5p) + 0.0311 × (miR-100-5p) + 0.0564 × (miR-99b-3p);

[0067] In the above calculation formula, miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p refers to the expression level of the corresponding plasma small extracellular vesicle miRNA marker.

[0068] In the present invention, the risk scoring models of the above 6 miRNAs combinations all showed good predictive performance and can be used to screen potential beneficiaries of lung cancer immunotherapy combined with chemotherapy. Among them, the risk scoring models constructed by combining miR-99b-3p+miR-100-5p+miR-193a-5p and miR-193a-5p+miR-99b-3p had the best predictive performance and can be used to accurately screen potential beneficiaries of lung cancer immunotherapy combined with chemotherapy.

[0069] Preferably, in constructing the model, the efficacy of combined immunotherapy and chemotherapy for lung cancer is judged according to the risk score value of the model.

[0070] Preferably, the judgment standard is: the higher the risk score, the worse the therapeutic effect of the subject receiving lung cancer immunotherapy combined with chemotherapy.

[0071] The present invention uses one of the contents of blood small extracellular vesicles, miRNA, to establish a biomarker model to identify the population that benefits from the immunotherapy combined with chemotherapy treatment for lung cancer. In addition, there are many contents of small extracellular vesicles, such as proteins, lipids, small molecule metabolites, lncRNA, circRNA, mRNA and piRNA, etc. These blood small extracellular vesicle contents may also construct a biomarker model to achieve similar effects as 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) and protein biomarkers can be used as biomarkers to predict the efficacy of immunotherapy combined with chemotherapy for lung cancer.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] At present, the widely used imaging examinations in clinical practice (such as chest enhanced CT, chest X-ray, MRI and ultrasound and other auxiliary imaging examinations) and tumor marker tests (such as CEA, NSE, CYFRA21-1, proGRP and SCC-Ag, etc.) have low sensitivity and specificity for lung cancer efficacy detection, and cannot be directly used to predict the efficacy of immunotherapy combined with chemotherapy. There are no reports on highly specific blood small extracellular vesicle miRNA biomarkers for predicting the efficacy of immunotherapy combined with chemotherapy in patients with lung adenocarcinoma. The following characteristics exist in the research on predicting the efficacy of immunotherapy combined with chemotherapy in patients with lung adenocarcinoma: a. There are few studies focusing on predicting the efficacy of immunotherapy combined with chemotherapy in patients with lung adenocarcinoma; b. There are few studies based on the second-generation sequencing platform; c. There are few studies on the content of small extracellular vesicles as biomarkers for predicting the efficacy of immunotherapy combined with chemotherapy in patients with lung adenocarcinoma; d. There are no reports on the use of small extracellular vesicle miRNA as a highly specific biomarker to screen the population that benefits from immunotherapy combined with chemotherapy.

[0074] The present invention analyzes differentially expressed miRNAs (DEMs) in sEVs of patients with advanced or metastatic lung adenocarcinoma who are treated with immunotherapy combined with chemotherapy by small RNA sequencing. A variety of sEVs miRNA combinations were modeled, and an effective model of sEVs miRNA combinations was proposed and identified for the first time to determine the true responding population, thereby screening out the potential beneficiaries of lung adenocarcinoma immunotherapy combined with chemotherapy from the level of peripheral blood small extracellular vesicles, avoiding potential side effects of non-responders, and reducing additional pain for patients and waste of clinical medical resources. The advantages of the present invention specifically include the following 6 points:

[0075] (1) The present invention adopts the plasma small extracellular vesicle small RNA sequencing strategy and discovers for the first time 6 lung cancer plasma small extracellular vesicle miRNAs: miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d and miR-6815-5p, which can be used as biomarkers to identify people who benefit from the combined immunotherapy and chemotherapy treatment regimen for lung cancer.

[0076] (2) The present invention found that compared with the non-responding group, patients with increased expression levels of miR-96-5p or miR-6815-5p in (1) or decreased expression levels of any one or a combination of at least two of miR-99b-3p, miR-100-5p, miR-193a-5p or miR-320d can benefit from the immunotherapy combined with chemotherapy regimen.

[0077] (3) Based on the six lung cancer plasma small extracellular vesicle miRNAs discovered in (1), the present invention constructed six risk scoring models containing two or three miRNAs in combination: miR-193a-5p+miR-99b-3p, miR-320d+miR-96-5p, miR-100-5p+miR-6815-5p+miR-193a-5p, miR-96-5p+miR-193a-5p+miR-320d, miR-100-5p+miR-6815-5p+miR-96-5p, miR-99b-3p+miR-100-5p+miR-193a-5p. The above six risk scoring models all showed good predictive performance and can be used to screen potential beneficiaries of combined immunotherapy and chemotherapy for lung cancer.

[0078] (4) The present invention further detected the expression levels of the six miRNAs discovered in (1) in the plasma small extracellular vesicles of lung cancer patients using the qRT-PCR method, further confirming the reliability of the risk scoring model, providing a solid foundation for the application and development of the model, and having great market potential.

[0079] (5) Based on the qRT-PCR method, the present invention further verified that two risk scoring models in (3), namely miR-99b-3p+miR-100-5p+miR-193a-5p and miR-193a-5p+miR-99b-3p, have the best prediction performance and can be used to accurately screen the potential beneficiaries of combined immunotherapy and chemotherapy for lung cancer.

[0080] (6) The present invention uses liquid biopsy identification technology, which can be combined with imaging examinations to improve the specificity of identifying people who respond to immunotherapy combined with chemotherapy treatment, reduce false positives, avoid potential side effects of non-responders, and reduce additional pain for patients and waste of clinical medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is the HE and IHC pathological diagnosis results of a patient with lung adenocarcinoma before immunotherapy combined with chemotherapy.

[0082] Figure 2 This is the imaging test result of a representative lung cancer patient who achieved partial remission after immunotherapy combined with chemotherapy. Baseline represents the CT image before treatment, and C2 represents the CT image after treatment.

[0083] Figure 3 This is the TEM detection result of plasma small extracellular vesicles.

[0084] Figure 4 This is the result of NTA detection of small extracellular vesicles in plasma.

[0085] Figure 5 Figure 2 is the ROC curve of two combined models containing two plasma small extracellular vesicle miRNAs.

[0086] Figure 6 Figure 2 is the ROC curve of four combined models containing three plasma small extracellular vesicle miRNAs.

[0087] Figure 7 This is the ROC curve of the plasma small extracellular vesicle miRNAs combined model based on qRT-PCR. DETAILED DESCRIPTION

[0088] The technical solution of the present invention is further described below by specific implementation methods. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0089] If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the field or the product instructions are used. If no manufacturer is specified for the reagents or instruments used, they are all conventional products that can be purchased through regular channels.

[0090] Example 1

[0091] 1. Study cohort and clinical information

[0092] This study included 29 patients diagnosed with lung cancer by existing clinical detection methods. Blood samples were collected from each patient before drug treatment for plasma small extracellular vesicle extraction and subsequent analysis. HE and IHC pathological diagnosis results were obtained for all 29 patients before immunotherapy combined with chemotherapy. The HE and IHC test results of representative patients are shown in Figure 1 .

[0093] After one month of treatment with the "pembrolizumab (K drug) + pemetrexed + carboplatin" regimen, 14 patients had partial remission (PR), 10 patients had stable disease (SD), and 5 patients had progressive disease (PD). The imaging test results of representative lung cancer patients with partial remission after immunotherapy combined with chemotherapy are shown in Figure 2 , where Baseline represents the CT image before treatment, and C2 represents the CT image after treatment.

[0094] 2. Extraction and characterization of plasma small extracellular vesicles

[0095] 2.1 Plasma collection and small extracellular vesicle extraction

[0096] Blood samples from patients were collected before immunotherapy and chemotherapy, stored in 10 mL vacuum blood collection tubes (REF367525, BD, USA), and slowly inverted several times before being placed vertically. The samples were centrifuged at 1600 g for 10 min at 4 °C. After centrifugation, the hemolysis level of the samples was determined according to the standard, and samples with less than level 4 were used for subsequent research. The supernatant was transferred to a 1.5 mL EP tube and centrifuged at 16000 g for 15 min at 4 °C to remove residual cell debris. The supernatant was divided into 1.5 mL EP tubes and stored in a -80 °C refrigerator for later use.

[0097] 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 was added to resuspend the small extracellular vesicles.

[0098] 2.2 Characterization of plasma small extracellular vesicles

[0099] TEM detection: resuspend the small extracellular vesicles with PBS and fix them with 4% paraformaldehyde, take the fixed small extracellular vesicles and drop them on the carbon-coated copper grid. After incubation at room temperature for 5 minutes, rinse with PBS twice, rinse with PBS containing glycine (50mM) once, incubate with PBS containing 0.5% BSA for 10 minutes, and finally negatively stain the copper grid with 2% uranyl acetate for 10 minutes, and observe and photograph using a transmission electron microscope (H-7650, Hitachi, Japan). The results show that the small extracellular vesicles have a typical "horseshoe-shaped" morphology (see Figure 3 ).

[0100] NTA assay: First, plasma small extracellular vesicles were diluted to 1×10 7 -1×10 9 / mL, pipette and mix. Subsequently, turn on the NTA instrument (NanoSight NS300, Malvern, UK) and inject the sample into the sample chamber. Use the 488nm excitation module, set the camera lens parameters, select 890 for the shutter value, 146 for the gain value, and set the detection threshold 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. The results of the particle size distribution of small extracellular vesicles detected by NTA showed that the main peak of the small extracellular vesicle particle size was 106nm, which was consistent with the particle size distribution of small extracellular vesicles (see Figure 4 ).

[0101] 3. Molecular marker discovery

[0102] 3.1 Extraction of plasma small extracellular vesicle miRNA

[0103] According to the product manual, total RNA was extracted from small extracellular vesicles using miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai), and RNA was eluted with 15 μL of nuclease-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).

[0104] 3.2 Construction of plasma small extracellular vesicle miRNA library

[0105] 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 fluorometer and the accompanying reagent Qubit dsDNA HS AssayKit (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.

[0106] 3.3 Sequencing data analysis process

[0107] Based on small RNA sequencing detection technology, the expression of miRNA in small extracellular vesicles in the plasma of lung cancer patients was obtained. The analysis process of sequencing data is as follows:

[0108] 1) Sequencing data alignment: After removing the sequencing adapters of the small RNA sequencing data, the sequencing data were aligned to the human reference genome hg19 (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ) using BWA software (version: 0.7.12-r1039), and the number of reads aligned to the miRNA was counted.

[0109] 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.

[0110] 3) miRNA filtering: Mature miRNAs with a length of 30 nt or less and covered by at least 2 reads per sample were retained for subsequent analysis.

[0111] 4) Standardization of miRNA expression: The trimmed mean of M-values ​​(TMM) method of the edgeR v3.26.8 analysis package in R language and the voom method of the limma v3.40.6 analysis package were used to standardize the miRNA expression of the samples.

[0112] 3.4 Screening of plasma small extracellular vesicle miRNAs

[0113] Based on the expression of miRNA in plasma small extracellular vesicles of lung cancer patients, the samples were grouped according to the imaging evaluation results after immunotherapy combined with chemotherapy, and statistical methods were used to discover plasma small extracellular vesicle miRNA biomarkers that can be used for the evaluation of the efficacy of immunotherapy combined with chemotherapy for lung cancer and the screening of benefit populations. The process is as follows:

[0114] 1) Sample grouping: Based on the imaging evaluation results after immunotherapy combined with chemotherapy, the patients were divided into two groups, the response group (PR+SD) and the non-response group (PD).

[0115] 2) Candidate molecular markers: The linear model fitting (limma-voom) method in the limma analysis package of R language was used to analyze the miRNAs with significant expression differences between the PR group and the PD group. The miRNAs with expression levels CPM (Counts Per Million) greater than 32, changes between the two groups of more than 1.5 times and test results P ≤ 0.05 were selected as candidate molecular markers.

[0116] Statistical methods were used to discover miRNAs as biomarkers that can be used to evaluate the efficacy of combined immunotherapy and chemotherapy for lung cancer and screen the benefit population, including miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d and miR-6815-5p. Compared with the non-response group, patients with increased expression levels of miR-96-5p and miR-6815-5p or decreased expression levels of miR-99b-3p, miR-100-5p, miR-193a-5p and miR-320d can benefit from combined immunotherapy and chemotherapy.

[0117] miR-96-5p (SEQ ID NO:24): UUUGGCACUAGCACAUUUUUGCU.

[0118] miR-99b-3p (SEQ ID NO:25): CAAGCUCGUGUCUGGGGUCCG.

[0119] miR-100-5p (SEQ ID NO:26): AACCCGUAGAUCCGAACUUGUG.

[0120] miR-193a-5p (SEQ ID NO:27): UGGGUCUUUGCGGGCGAGAUGA.

[0121] miR-320d (SEQ ID NO:28): AAAAGCUGGGUUGAGAGGA.

[0122] miR-6815-5p (SEQ ID NO:29): UAGGGUGGCGCCGGAGGAGUCAUU.

[0123] 4. Risk scoring model construction

[0124] The candidate miRNAs that were significantly upregulated or downregulated in the PR sample group were analyzed by the glmnet v4.0.2 software package using the least absolute shrinkage and selection operator (LASSO) to analyze the LASSO prediction model and risk score of different miRNAs combinations, and the prediction model with AUC>0.9 was retained. Based on the above-mentioned 6 lung cancer plasma small extracellular vesicle miRNAs, a total of 6 risk score models containing 2 or 3 miRNAs combinations were constructed to identify the responding population for the efficacy of immunotherapy combined with chemotherapy.

[0125] Among the six risk scoring models, two included a combination of two plasma small extracellular vesicle miRNAs (ROC curves are shown in Figure 2). Figure 5 ): miR-193a-5p+miR-99b-3p and miR-320d+miR-96-5p; there were 4 combinations containing 3 plasma small extracellular vesicle miRNAs (ROC curve see Figure 6 ): miR-100-5p+miR-6815-5p+miR-193a-5p, miR-96-5p+miR-193a-5p+miR-320d, miR-100-5p+miR-6815-5p+miR-96-5p and miR-99b-3p+miR-100-5p+miR-193a-5p. The calculation formulas of the six risk score models are shown in Table 1. The risk score cut-off value of the model is the median of the risk score. The higher the risk score, the worse the efficacy of lung cancer patients receiving immunotherapy combined with chemotherapy. The six risk score models all showed good predictive performance (AUC>0.9). The specificity, sensitivity and AUC values ​​of the six models are shown in Table 2.

[0126] Table 1

[0127]

[0128] Table 2

[0129] miRNA Specificity Sensitivity AUC miR-320d+miR-96-5p 79.20％ 100.00％ 92.50％ miR-193a-5p+miR-99b-3p 87.50％ 100.00％ 92.50％ miR-100-5p+miR-6815-5p+miR-96-5p 87.50％ 100.00％ 97.50％ miR-100-5p+miR-6815-5p+miR-193a-5p 91.70％ 100.00％ 97.50％ miR-96-5p+miR-193a-5p+miR-320d 83.30％ 100.00％ 95.00％ miR-99b-3p+miR-100-5p+miR-193a-5p 91.70％ 100.00％ 94.20％

[0130] 5. Consistency evaluation of qRT-PCR detection and high-throughput sequencing data

[0131] In order to further evaluate whether the constructed risk scoring model can be verified by other detection technologies, this example uses qRT-PCR technology (TaqMan probe method) to detect the expression levels of the 6 plasma small extracellular vesicle miRNAs discovered in the response group (13 patients) and the non-response group (5 patients). First, HiScript III Reverse Transcriptase (Vazyme, R302, Nanjing) was used to synthesize the first-chain cDNA. The 20 μL reaction system was: 4 μL RNA, 8.5 μL nuclease-free water, 4 μL 5×HiScript III buffer, 1 μL 10mM dNTP, 0.5 μL HiScript III reverse transcriptase, 1 μL ribonuclease inhibitor and 1 μL miRNA specific stem-loop reverse transcription primer. The cDNA synthesis reaction program was 12°C, 5min; 42°C, 15min; 85°C, 5min; 4°C maintenance.

[0132] Taq HSDNA polymerase (Vazyme, P132, Nanjing) was then used for qPCR. The 25 μL reaction system was: 2 μL cDNA, 8.75 μL nuclease-free water, 9 μL PCR buffer (prepared by the company), 1.5 μL Taq HSDNA polymerase, 0.75 μL 10 μM probe, 1.25 μL 30 μM forward primer, and 1.75 μL 10 μM reverse primer. The qPCR reaction program was 95°C, 5 min; 95°C, 10 s, 60°C, 35 s, 45 cycles. The experiment used miR-451 as an internal reference. Each experimental reaction was repeated 3 wells. Fluorescence quantification was performed using an ABI7500PCR instrument, and finally 2 (-ΔΔCT) Methods The relative expression of miRNA was analyzed. The primer sequences used for qRT-PCR detection are shown in Table 3.

[0133] Table 3

[0134]

[0135] It was found that the ROC curve of the miRNA scoring model obtained by PCR detection was highly consistent with the ROC curve obtained from the second-generation sequencing data. Two of the six risk scoring models: miR-99b-3p+miR-100-5p+miR-193a-5p and miR-193a-5p+miR-99b-3p, had the best prediction performance. The risk scoring model miR-99b-3p+miR-100-5p+miR-193a-5p had a specificity of 100%, a sensitivity of 80%, and an AUC value of 0.94. The risk scoring model miR-193a-5p+miR-99b-3p had a specificity of 100%, a sensitivity of 80%, and an AUC value of 0.95. Figure 7 is the ROC curve of the above two risk scoring models.

[0136] In summary, the present invention analyzed the differentially expressed miRNAs (DEMs) in plasma small extracellular vesicles (sEVs) of patients with advanced or metastatic lung adenocarcinoma who received combined immunotherapy and chemotherapy by small RNA sequencing. Multiple sEVs miRNA combinations were modeled, and an effective model containing 2 or 3 sEV miRNAs combinations was identified to determine the true responding population, and showed high performance (AUC>0.9), achieving the potential beneficiary population of lung adenocarcinoma immunotherapy combined with chemotherapy from the level of peripheral blood small extracellular vesicles.

[0137] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. Application of plasma small extracellular vesicle miRNA markers in the preparation of a product for predicting the efficacy of lung cancer immunotherapy combined with chemotherapy, characterized in that: The plasma small extracellular vesicle miRNA markers include: any one of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p, or a combination of at least two thereof.

2. The use according to claim 1, characterized in that: The plasma small extracellular vesicle miRNA markers include: a combination of at least two of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p.

3. The use according to claim 2, characterized in that: The lung cancer immunotherapy combined with chemotherapy efficacy prediction product predicts that the subject is a responder to lung cancer immunotherapy combined with chemotherapy, or predicts that the subject is a non-responder to lung cancer immunotherapy combined with chemotherapy based on the expression level of the plasma small extracellular vesicle miRNA marker; Compared with the non-responder group, the subjects whose expression level of miR-96-5p or miR-6815-5p is increased; or the subjects whose expression level of any one or a combination of at least two of miR-99b-3p, miR-100-5p, miR-193a-5p or miR-320d is decreased are responders to immunotherapy combined with chemotherapy for lung cancer; The immunotherapy in the lung cancer immunotherapy combined with chemotherapy includes PD-1 inhibitor therapy, and the chemotherapy includes pemetrexed and / or platinum chemotherapy; The PD-1 inhibitor is selected from pembrolizumab; The platinum is selected from carboplatin.

4. The use according to claim 3, characterized in that: The plasma small extracellular vesicle miRNA marker is a combination of miR-320d and miR-96-5p; Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-193a-5p and miR-99b-3p; Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-100-5p, miR-6815-5p and miR-96-5p; Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-100-5p, miR-6815-5p and miR-193a-5p; Or, the plasma small extracellular vesicle miRNA marker is a combination of miR-96-5p, miR-193a-5p and miR-320d; Alternatively, the plasma small extracellular vesicle miRNA marker is a combination of miR-99b-3p, miR-100-5p and miR-193a-5p.

5. A method for screening plasma small extracellular vesicle miRNA markers, characterized in that: The screening method comprises: Sample grouping: Based on the imaging evaluation results after lung cancer immunotherapy combined with chemotherapy, the subjects who underwent lung cancer immunotherapy combined with chemotherapy were divided into a response group and a non-response group; Screening of miRNA markers: Statistical methods were used to analyze miRNAs with significant differences in expression between the response group and the non-response group before lung cancer immunotherapy combined with chemotherapy; miRNAs with expression CPM greater than 32, a change of more than 1.5 times between the two groups, and a test result of P ≤ 0.05 were selected as candidate molecular markers; The immunotherapy in the lung cancer immunotherapy combined with chemotherapy includes PD-1 inhibitor therapy, and the chemotherapy includes pemetrexed and / or platinum chemotherapy; The PD-1 inhibitor is selected from pembrolizumab; The platinum is selected from carboplatin.

6. Use of a detection reagent for the expression level of plasma small extracellular vesicle miRNA markers in the preparation of a product for predicting the efficacy of lung cancer combined with immunotherapy and chemotherapy, characterized in that: The detection reagents include: qRT-PCR reagents for detecting any one or a combination of at least two of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p; The reagents for qRT-PCR include primers and probes, wherein the primers include stem-loop reverse transcription primers, forward primers and reverse primers; The stem-loop reverse transcription primer comprises any one or a combination of at least two of SEQ ID NO: 1, 5, 8, 11, 14, 17 or 20; The forward primer comprises any one or a combination of at least two of SEQ ID NO: 2, 6, 9, 12, 15, 18 or 21; The reverse primer includes SEQ ID NO: 4 and / or 23.

7. The use according to claim 6, characterized in that: The detection reagent also includes: a reagent for extracting plasma small extracellular vesicles and / or plasma small extracellular vesicle miRNA.

8. A model for predicting the efficacy of combined immunotherapy and chemotherapy for lung cancer, characterized in that: The calculation formula of the prediction model includes: Risk score = 1.3667 + 0.1051 × (miR-320d) – 0.1641 × (miR-96-5p); or, risk score = -0.1994 + 0.1122 × (miR-193a-5p) + 0.0590 × (miR-99b-3p); or, risk score = 2.2004 + 0.0573 × (miR-100-5p)–0.1826 × (miR-6815-5p)–0.1230 × (miR-96-5p); or, risk score = 0.8594 + 0.0397 × (miR-100-5p) + 0.0966 × (miR-6815-5p) – 0.1917 × (miR-193a-5p); or, risk score = 0.9107 + 0.0510 × (miR-193a-5p) + 0.0943 × (miR-320d) – 0.1584 × (miR-96-5p); or, risk score = -0.6055 + 0.1197 × (miR-193a-5p) + 0.0311 × (miR-100-5p) + 0.0564 × (miR-99b-3p); In the above calculation formula, miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p refers to the expression level of the corresponding plasma small extracellular vesicle miRNA marker.

9. The lung cancer immunotherapy combined with chemotherapy efficacy prediction model according to claim 8, characterized in that: Judging the efficacy of lung cancer immunotherapy combined with chemotherapy according to the risk score value of the prediction model; The judgment standard is: the higher the risk score, the worse the efficacy of the subject's lung cancer immunotherapy combined chemotherapy.

10. A method for constructing a lung cancer immunotherapy combined with chemotherapy efficacy prediction model, characterized in that: The method comprises: Data acquisition: obtaining the expression level of plasma small extracellular vesicle miRNA markers of the subjects before lung cancer immunotherapy combined with chemotherapy; the plasma small extracellular vesicle miRNA markers include: any one of miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p or a combination of at least two; Model construction: Based on the expression levels of plasma small extracellular vesicle miRNA markers, an efficacy prediction model was established using any of the analysis methods, including minimum absolute shrinkage and selection operators, support vector machines, neural networks, or random forests.

11. The method for constructing a lung cancer immunotherapy combined with chemotherapy efficacy prediction model according to claim 10, characterized in that: In the model construction, the LASSO prediction models and their risk scores of different miRNAs combinations were analyzed using the glmnet v4.0.2 software package using the least absolute shrinkage and selection operators, and the prediction models with AUC>0.9 were retained; The calculation formula of the prediction model includes: Risk score = 1.3667 + 0.1051 × (miR-320d) – 0.1641 × (miR-96-5p); or, risk score = -0.1994 + 0.1122 × (miR-193a-5p) + 0.0590 × (miR-99b-3p); or, risk score = 2.2004 + 0.0573 × (miR-100-5p)–0.1826 × (miR-6815-5p)–0.1230 × (miR-96-5p); or, risk score = 0.8594 + 0.0397 × (miR-100-5p) + 0.0966 × (miR-6815-5p) – 0.1917 × (miR-193a-5p); or, risk score = 0.9107 + 0.0510 × (miR-193a-5p) + 0.0943 × (miR-320d) – 0.1584 × (miR-96-5p); or, risk score = -0.6055 + 0.1197 × (miR-193a-5p) + 0.0311 × (miR-100-5p) + 0.0564 × (miR-99b-3p); In the above calculation formula, miR-96-5p, miR-99b-3p, miR-100-5p, miR-193a-5p, miR-320d or miR-6815-5p refers to the expression level of the corresponding plasma small extracellular vesicle miRNA marker; Judging the efficacy of lung cancer immunotherapy combined with chemotherapy according to the risk score value of the model; The judgment standard is: the higher the risk score, the worse the efficacy of the subject's lung cancer immunotherapy combined chemotherapy.

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