Biomarker for predicting curative effect of lung squamous cell carcinoma immune combined chemotherapy and application of biomarker

Abstract

The invention discloses a biomarker for predicting the curative effect of lung squamous cell carcinoma immune combined chemotherapy and application of the biomarker. The biomarker comprises any one of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p or hsa-miR-143-3p or a combination of at least two of the hsa-miR-610, the hsa-miR-4536-5p, the hsa-miR-204-5p, the hsa-miR-5196-3p, the hsa-miR-6513-5p and the According to the invention, expression difference of plasma exosome miRNA in patients with good prognosis and poor prognosis of advanced lung squamous cell carcinoma on immune combined chemotherapy is analyzed through small RNA sequencing, modeling is carried out, a biomarker which can be used for prediction of curative effect of lung squamous cell carcinoma immune combined chemotherapy and screening of beneficial people is screened, and prediction efficiency of the combined model is verified at the same time. A non-invasive detection solution is provided aiming at how to more conveniently and accurately distinguish the immune combined chemotherapy benefit people and the benefit-deficient people.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology and relates to a biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for lung squamous cell carcinoma. Background Art

[0002] Non-small cell lung cancer (NSCLC) accounts for the majority of all lung cancers (about 80%), of which about 20%-30% are squamous cell carcinomas of the lung, which are highly heterogeneous malignancies with molecular characteristics significantly different from those of lung adenocarcinoma. Due to the lack of driver gene changes suitable for targeted therapy, patients with squamous cell carcinoma of the lung have limited treatment options except chemotherapy. With the development of immunotherapy, PD-1 inhibitors have been widely used in the first-line and second-line treatment of lung cancer.

[0003] At present, PD-1 inhibitors combined with chemotherapy are the main treatment options for patients with squamous cell lung cancer without driver gene mutations. Therefore, it is crucial to screen out potential beneficiaries with efficient efficacy and prognosis evaluation methods. For example, CN114672567A discloses a prognosis evaluation system for squamous cell lung cancer patients based on CD47 and TIGIT dual targets and its application, and discloses the application of substances for detecting CD47 and TIGIT expression levels in the preparation of products for prognosis evaluation of squamous cell lung cancer patients; the preparation of products for postoperative risk prediction of squamous cell lung cancer patients; and the application of products for prognosis evaluation of squamous cell lung cancer patients. At the same time, CD47 and TIGIT are also potential immunotherapy targets for squamous cell lung cancer. While evaluating the prognosis, clarifying the expression level will also provide a new method for predicting the efficacy of immunotherapy in the future. CN114686591A discloses a prediction model for the efficacy of immunotherapy of squamous cell lung cancer based on gene expression, as well as a construction method and application thereof. The ssGSEA method is used to calculate the enrichment scores of 28 types of immune cells in each squamous cell lung cancer sample in the training set, the above score matrix is ​​normalized and NMF typed, survival analysis is performed, differentially expressed genes and genes affecting OS and DFS are screened, the intersection is merged, and an IPTS model is constructed using the screened genes. The model is used to predict the efficacy of immunotherapy for patients with squamous cell lung cancer, which can improve the accuracy of screening patients with squamous cell lung cancer who benefit from immunotherapy such as immune checkpoint inhibitors.

[0004] However, there is a lack of research on the prediction of the efficacy of immunotherapy combined with chemotherapy in patients with squamous cell lung cancer, especially in the advanced stage. Therefore, developing new markers that can be used to predict the efficacy of immunotherapy combined with chemotherapy in patients with advanced squamous cell lung cancer and expanding the prediction methods are of great significance in the field of squamous cell lung cancer. Summary of the invention

[0005] In view of the deficiencies of the prior art and actual needs, the present invention provides biomarkers for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma, in order to improve the ability to evaluate the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma and the specificity of screening the beneficiary population.

[0006] To achieve this object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma, characterized in that the biomarker includes any one of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p or hsa-miR-143-3p, or a combination of at least two of them.

[0008] The present invention uses plasma of patients with advanced squamous cell lung cancer as a research sample, extracts exosomes (small extracellular vesicles) in the plasma of patients with advanced squamous cell lung cancer, and then detects the expression level of exosome miRNA in the plasma of patients with advanced squamous cell lung cancer based on the second-generation sequencing technology of small RNA sequencing, and finds plasma exosome miRNA biomarkers that can be used for evaluating the efficacy of immunotherapy combined with chemotherapy for advanced squamous cell lung cancer and screening the benefited population. Advanced squamous cell lung cancer patients with increased or present expression levels of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, and hsa-miR-5196-3p in plasma small extracellular vesicles, or decreased or absent expression levels of hsa-miR-6513-5p and hsa-miR-143-3p can benefit from the immunotherapy combined chemotherapy regimen, and there is a significant difference in the expression of the six miRNAs between patients with squamous cell lung cancer and healthy people.

[0009] In a second aspect, the present invention provides the use of the biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma and / or its detection product as described in the first aspect in the preparation of a product for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma.

[0010] The present invention discovers and verifies biomarkers that can be used to accurately predict the efficacy of combined immunotherapy and chemotherapy for advanced squamous cell lung carcinoma and the population that benefits from it. This can further develop corresponding detection products, improve the specificity of identifying the population that responds to the combined immunotherapy regimen for squamous cell lung carcinoma, reduce false positives, avoid excessive medical treatment, reduce the economic burden on patients, and accurately screen the population that benefits from combined immunotherapy and chemotherapy in patients with advanced squamous cell lung carcinoma.

[0011] Preferably, the detection product includes reagents and / or instruments for detecting the expression level of the biomarkers for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma as described in the first aspect.

[0012] Preferably, the reagents and / or instruments include reagents and / or instruments for small RNA sequencing.

[0013] It is understood that the products commonly used in the art for detecting miRNA levels in exosomes are applicable to the present invention, such as the commercially available exosome extraction reagent (EXO Quick-TC TM Exosome Isolation Reagent (System Biosciences, USA), etc.) as well as second-generation sequencing technology products, miRNA chips or third-generation sequencing technology products, etc.

[0014] In a third aspect, the present invention provides a product for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung cancer, the product comprising reagents and / or instruments for detecting the expression level of the biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung cancer as described in the first aspect.

[0015] In a fourth aspect, the present invention provides a method for constructing a prediction model for the efficacy of combined immunotherapy and chemotherapy in patients with squamous cell lung cancer, the method comprising: obtaining expression level data of target markers in samples that benefit and do not benefit from combined immunotherapy and chemotherapy as a training set, wherein the target marker is the biomarker for predicting the efficacy of combined immunotherapy and chemotherapy for squamous cell lung cancer as described in the first aspect.

[0016] A prediction model is constructed using a machine learning algorithm based on the classification information of the sample and the level data of the target marker.

[0017] Preferably, the machine learning algorithm comprises a linear support vector classifier (LinearSVC).

[0018] In a fifth aspect, the present invention provides a prediction model for the therapeutic effect of lung squamous cell carcinoma patients after immunotherapy combined with chemotherapy, and the prediction model is obtained by the method for constructing a prediction model for lung squamous cell carcinoma patients after immunotherapy combined with chemotherapy as described in the fourth aspect.

[0019] In a sixth aspect, the present invention provides a device for predicting the efficacy of immunotherapy combined with chemotherapy for lung squamous cell carcinoma, the device comprising:

[0020] A data module, used to provide expression level data of a target marker of a sample to be tested, wherein the target marker is a biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma as described in the first aspect;

[0021] A scoring module, used to obtain a probability value through a prediction model based on the expression level data of the target marker of the sample to be tested; wherein the prediction model is the prediction model for the efficacy of combined immunotherapy and chemotherapy for patients with squamous cell carcinoma of the lung as described in the fifth aspect;

[0022] A judgment module is used to predict the efficacy of immunotherapy combined with chemotherapy based on the probability value.

[0023] In a seventh aspect, the present invention provides an electronic device comprising one or more processors and a memory for storing executable instructions, wherein the one or more processors are configured to call the executable instructions stored in the memory to implement the functions of the device for predicting the efficacy of combined immunotherapy and chemotherapy for squamous cell lung carcinoma as described in the sixth aspect.

[0024] In an eighth aspect, the present invention provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the functions of the device for predicting the efficacy of combined immunotherapy and chemotherapy for squamous cell lung carcinoma as described in the sixth aspect.

[0025] In the ninth aspect, the present invention relates to the use of an agent for detecting any one or a combination of at least two of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p or hsa-miR-143-3p in the preparation of a composition or kit for evaluating and / or predicting the efficacy of lung squamous cell carcinoma cells or patients to immunotherapy combined chemotherapy.

[0026] Preferably, compared with the control, an increased or present expression level of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, or a decreased or absent expression level of hsa-miR-6513-5p, hsa-miR-143-3p indicates that the lung squamous cell carcinoma cells or the patient can benefit from combined immunotherapy and chemotherapy.

[0027] Preferably, immunotherapy combined with chemotherapy is PD-1 inhibitor combined with chemotherapy, the PD-1 inhibitor is tislelizumab, and the immunotherapy combined with chemotherapy regimen is tislelizumab + albumin paclitaxel + carboplatin.

[0028] Preferably, the marker hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p or hsa-miR-143-3p is from small extracellular vesicles.

[0029] Preferably, the small extracellular vesicles are derived from plasma.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) The present invention first discovered that plasma small extracellular vesicle miRNA combinations can be used as high-efficiency biomarkers for screening people who benefit from the immunotherapy combined with chemotherapy treatment regimen for lung squamous cell carcinoma. Advanced lung squamous cell carcinoma patients whose plasma small extracellular vesicle hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p expression levels are increased or present, or whose hsa-miR-6513-5p, hsa-miR-143-3p expression levels are decreased or absent can benefit from the immunotherapy combined with chemotherapy regimen.

[0032] (2) The present invention used healthy subjects as the control group and further detected the expression levels of the 6 miRNAs discovered in the plasma small extracellular vesicles of actual lung squamous cell carcinoma patients, which provided a solid foundation for the development and transformation of the model. The PFS and OS of the patients in the good prognosis group predicted by the further constructed immunotherapy combined with chemotherapy prediction model were significantly better than those in the poor prognosis group. Therefore, the prediction model has great clinical application value in the evaluation of the efficacy of immunotherapy combined with chemotherapy for lung squamous cell carcinoma and the screening of the benefit population;

[0033] (3) The technical solution for evaluating the efficacy of combined immunotherapy and chemotherapy in patients with advanced squamous cell lung cancer and identifying the beneficiary population based on the markers of the present invention is a liquid biopsy-based detection method with the advantages of minimally invasive, sensitive and convenient, and has a wide range of application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a morphological diagram of plasma small extracellular vesicles;

[0035] Figure 2 This is the distribution diagram of the particle size of plasma small extracellular vesicles;

[0036] Figure 3 This is the ROC curve diagram for the training cohort;

[0037] Figure 4 ROC curve diagram for the validation cohort;

[0038] Figure 5 This is the expression level diagram of miRNA in the prediction model of immunotherapy combined with chemotherapy for lung squamous cell carcinoma;

[0039] Figure 6 The survival analysis results of the training cohort for the prediction model of immunotherapy combined with chemotherapy for squamous cell lung cancer are shown in the figure;

[0040] Figure 7 This is the survival analysis result of the validation cohort for the prediction model of immunotherapy combined with chemotherapy for squamous cell lung carcinoma. DETAILED DESCRIPTION

[0041] To further illustrate the technical means and effects of the present invention, the present invention is further described below in conjunction with the embodiments and drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention, rather than to limit the present invention.

[0042] 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.

[0043] Example 1

[0044] Study cohort and clinical information

[0045] A total of 48 patients diagnosed with advanced squamous cell lung cancer by existing clinical detection methods were included, and blood samples were collected from patients before drug treatment. The inclusion criteria for patients were as follows: 1) Patients with pathological diagnosis of stage IV squamous cell carcinoma without driver gene mutations, and Eastern Cooperative Oncology Group (ECOG) functional status (PS) score of 0 or 1; 2) First receiving "tislelizumab + albumin paclitaxel + carboplatin" treatment regimen, and no history of chemotherapy treatment; 3) There are efficacy evaluation data.

[0046] After at least 4 complete cycles of drug treatment, the best response (BOR) and progression-free survival (PFS) within 4 cycles were evaluated for efficacy. The median PFS was 7.6 months. Patients with PFS higher than the median 7.6 months were divided into the good prognosis group, and those with PFS lower than the median were divided into the poor prognosis group. 30% of the samples were randomly selected as the validation cohort, and the rest were used as the training cohort. At the same time, blood samples from 10 healthy people were collected as the control group.

[0047] Example 2

[0048] This example extracts and characterizes small extracellular vesicles from the plasma of patients with lung squamous cell carcinoma and healthy subjects.

[0049] (1) Plasma collection and small extracellular vesicle extraction

[0050] Blood samples from patients and healthy subjects were collected before immunotherapy and chemotherapy and stored in 10 mL vacuum blood collection tubes (REF367525, BD, USA), slowly inverted several times and then placed vertically. Freshly collected blood samples were centrifuged twice to obtain plasma. The first centrifugation was performed at 1600 g for 10 min (4 ° C). After the centrifugation, the supernatant was transferred to a new 1.5 mL centrifuge tube. The hemolysis level of the blood sample was determined and recorded. Samples with hemolysis level ≤ 4 were included in the subsequent study. The second centrifugation was performed at 16000 g for 15 min (4 ° C). After the centrifugation, 1 mL of the supernatant was transferred to each tube and stored in a -80 ° C refrigerator for use.

[0051] The isolated and stored plasma samples of lung squamous cell carcinoma and healthy subjects were used to extract plasma small extracellular vesicles using the small extracellular vesicle separation reagent independently developed by SiliDi. The frozen plasma samples were taken out and thawed in a 37°C water bath, centrifuged at 12000g for 10min (4°C), and 500μL of supernatant was transferred to a 0.45μm filter column (Costar, CLS8163-100EA, Corning, USA), centrifuged at 12000g for 5min (4°C), and then the filtrate was transferred to a 0.22μm filter column (Costar, CLS8161-100EA, USA), centrifuged at 12000g for 5min (4°C), and the filtrate was collected in a 1.5mL EP tube. The volume of the filtrate was measured, and 1 / 4 volume of small extracellular vesicle isolation reagent (L3525, 3DMed, Shanghai, China) was added. After thorough mixing, the mixture was incubated at 4°C for 30 min, centrifuged at 4700 g for 30 min (4°C), the supernatant was discarded, and 200 μL PBS (Phosphate Buffer Saline) was added to resuspend the small extracellular vesicles.

[0052] (2) Characterization of plasma small extracellular vesicles

[0053] In order to detect the characteristics of plasma small extracellular vesicles in patients with lung squamous cell carcinoma and healthy people, the morphology of small extracellular vesicles was detected by transmission electron microscopy (TEM), and the size distribution of plasma small extracellular vesicles was further detected by nanoparticle tracking analysis (NTA). TEM detection: Small extracellular vesicles were resuspended in PBS and fixed with 4% paraformaldehyde, and the fixed small extracellular vesicles were dropped on carbon-coated copper grids. After incubation at 25°C for 5 minutes, rinsed twice with PBS, rinsed once with PBS containing glycine (50mM), incubated with PBS containing 0.5% BSA for 10 minutes, and finally negatively stained the copper grid with 2% uranyl acetate for 10 minutes, and observed and photographed using a transmission electron microscope (H-7650, Hitachi, Japan).

[0054] NTA detection: First, the plasma small extracellular vesicles were diluted to 1*10^ 7 -1*10^ 9 / mL, pipetting and mixing. Subsequently, the NTA instrument (NanoSight NS300, Malvern, UK) was turned on and the sample was injected into the sample chamber. The 488nm excitation module was used, the lens parameters of the camera were set, the shutter value was selected as 890, the gain value was selected as 146, and the detection threshold was set to 7. At least 200 complete tracks were analyzed and obtained for each video. Finally, the nanoparticle tracking data of plasma small extracellular vesicles were analyzed using NTA version 2.3 analysis software.

[0055] TEM was used to detect the morphology of small extracellular vesicles, and the results showed that small extracellular vesicles showed a typical "horseshoe-shaped" morphology ( Figure 1 ). NTA was used to detect the size distribution of small extracellular vesicles. The results showed that the average size of small extracellular vesicles was 58.5nm, which was consistent with the size distribution of small extracellular vesicles ( Figure 2 ). This indicates that small extracellular vesicles were successfully extracted from the plasma of patients with lung squamous cell carcinoma and healthy subjects.

[0056] Example 3

[0057] In this example, plasma small extracellular vesicle miRNA extraction and sequencing analysis were performed.

[0058] According to the product manual, miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai) was used to extract total RNA from small extracellular vesicles of lung squamous cell carcinoma patients and healthy subjects, and RNA was eluted with 15 μL RNase-free water. The concentration and fragment distribution of miRNA were detected using Agilent 2100 Bioanalyzer and the matching small RNA analysis kit (5067-1548, Agilent).

[0059] Small RNA sequencing was used to detect the expression levels of miRNAs in plasma small extracellular vesicles from patients with lung squamous cell carcinoma and healthy subjects. The miRNA library was prepared using the NEBNext Multiplex Small RNA Library Prep Set for Illumina kit (E7300L, NEB, USA) according to the product instructions. 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 Gel and PCR Clean-up kit (740609.250, MN, Germany), and the library DNA was eluted with 30 μL of nuclease-free water. The DNA concentration was quantified using the Invitrogen Qubit 4.0 fluorometer and the matching reagent Qubit dsDNA HS Assay Kit (Q32854, Thermofisher, USA). The Agilent 2100 bioanalyzer and its matching chips and reagents Agilent High Sensitivity DNA Kit & Reagents (5067-4626, Agilent, USA) were used to detect the distribution of DNA fragments in the library. The Illumina NovaSeq platform was used for sequencing, and the sequencing strategy was PE150. The sequencing data volume of each library was 6G.

[0060] Based on small RNA sequencing detection technology, the expression levels of miRNA in plasma small extracellular vesicles of lung squamous cell carcinoma patients and healthy subjects were obtained. The analysis process of sequencing data is as follows.

[0061] 1) Sequencing data alignment. After removing the sequencing adapters of the small RNA sequencing data, the sequencing data were aligned to the human reference genome hg19 (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ) using BWA software (version: 0.7.12-r1039), and the number of reads aligned to miRNA was counted.

[0062] 2) miRNA annotation. The miRNAs were annotated using the Gencode v25 and miRBase v21 databases, and those annotated as known mature miRNAs were retained for subsequent analysis.

[0063] 3) miRNA filtering: Mature miRNAs with a length of less than or equal to 30 nt and covered by at least 2 reads per sample were retained for subsequent analysis.

[0064] 4) Standardization of miRNA expression. The samples were normalized for miRNA expression using Python language Numpy v1.24.4 analysis package), Scipy v1.10.1 analysis package, and Scikit-learn v1.3.2 analysis package.

[0065] Example 4

[0066] This example explores and verifies biomarkers for combined immunotherapy and chemotherapy in squamous cell lung cancer.

[0067] Based on the expression of miRNA in plasma small extracellular vesicles of patients with squamous cell lung cancer analyzed in Example 3, the samples were divided according to the median PFS of 7.6 months after 4 cycles of immunotherapy combined with chemotherapy. The 48 samples were divided into a training cohort and a validation cohort at a ratio of 7:3. Statistical methods were used to discover plasma small extracellular vesicle miRNAs as biomarkers in the training cohort that can be used to predict the efficacy of immunotherapy combined with chemotherapy for squamous cell lung cancer and screen the benefit population.

[0068] 1) Sample grouping. According to the median BOR and PFS of 7.6 months within 4 cycles of immunotherapy combined with chemotherapy, the patients were divided into two groups: a good efficacy group (PR group) and a poor efficacy group (PD group). The 48 samples were divided into a training cohort (33 cases) and a validation cohort (15 cases) in a ratio of 7:3, and the samples in the good efficacy group and the poor efficacy group were randomly divided into the training group and the validation group in proportion.

[0069] 2) Candidate molecular markers. The Mann-Whitney U statistical test method in the Scipy analysis package of the Python language was used to analyze the miRNAs with significant differences in expression between the PR group and the PD group. Six miRNAs with expression levels CPM (Counts Per Million, reads per million bases) that changed more than 1.5 times between the two groups and test results P≤0.01 were selected as candidate molecular markers. Statistical methods were used to discover miRNAs that can be used for the evaluation of the efficacy of immunotherapy combined with chemotherapy in lung squamous cell carcinoma and the screening of benefit populations as biomarkers: hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p and hsa-miR-143-3p, a total of 6 types.

[0070] 3) Construction of the prediction model for immunotherapy combined with chemotherapy. In the training cohort, the CPM values ​​of the six candidate miRNAs were used as training features. Then, the model parameters and reference values ​​were obtained using 5-fold cross validation. According to the predicted values ​​of the 5-fold cross validation of each sample in the training cohort and the response grouping of the samples, the receiver operating characteristic curve (ROC curve) of the training cohort was drawn. The model prediction efficiency evaluation method included the area under the ROC curve (AUC, Area Under Curve, value range 0-1) and the positive predictive value (PPV, Positive Predictive Value, value range 0-1) calculated according to the reference value, specificity (value range 0-1), accuracy (value range 0-1) and sensitivity (value range 0-1). The higher the value, the better the effect. Validation of the prediction model for immunotherapy combined with chemotherapy. In the validation cohort, the model prediction classification efficiency was verified according to the classification model and prediction value determined in the training cohort. In the validation cohort, the CPM values ​​of the six candidate miRNAs were used as variables and input into the training model for response prediction. According to the model prediction value and the actual response result of each sample in the validation cohort, the ROC curve of the validation cohort was drawn. Based on the reference value, the validation cohort was divided into a good efficacy group (same as the training cohort) and a poor efficacy group, and the model prediction performance was evaluated, including specificity, sensitivity and accuracy. The higher the value, the better the effect.

[0071] Based on the candidate miRNA biomarkers for lung squamous cell carcinoma patients discovered above, multiple parameter adjustments were finally confirmed to confirm that the analysis model constructed with a combination of 6 miRNAs, including hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p and hsa-miR-143-3p, had the best performance, with the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1) as high as 0.92 ( Figure 3 In order to verify the effect of the immunotherapy combined with chemotherapy prediction model in screening the population responding to immunotherapy combined with chemotherapy in advanced squamous cell lung cancer, another batch of independent data was used as the validation cohort. In the validation cohort, the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1) of the prediction model was as high as 0.93 ( Figure 4 ), the model has better prediction performance.

[0072] Example 5

[0073] This example analyzes the expression of small extracellular vesicle miRNA predictive markers for lung squamous cell carcinoma combined with immunotherapy and chemotherapy.

[0074] In order to further evaluate the expression level of the small extracellular vesicle miRNA combination in the prediction model in the plasma small extracellular vesicles of patients with lung squamous cell carcinoma in the real world, we used healthy people as the control group to detect the expression of 6 miRNAs in the group with good and poor efficacy of the combined immunotherapy regimen by next-generation sequencing. Patients with advanced lung squamous cell carcinoma whose plasma small extracellular vesicle hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p expression levels were increased or present, or whose hsa-miR-6513-5p, hsa-miR-143-3p expression levels were reduced or absent could benefit from the combined immunotherapy and chemotherapy regimen, and there was a significant difference in the expression of the 6 miRNAs between patients with lung squamous cell carcinoma and healthy people ( Figure 5 ).

[0075] Example 6

[0076] This example conducts survival analysis of the prediction model of immunotherapy combined with chemotherapy for squamous cell lung cancer.

[0077] In order to further verify the clinical application value of the prediction model of immunotherapy combined with chemotherapy for lung squamous cell carcinoma, the small extracellular vesicle miRNA prediction marker model was used to regroup the efficacy of immunotherapy combined with chemotherapy in lung squamous cell carcinoma patients. That is, the CPM values ​​of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p and hsa-miR-143-3p obtained by small RNA sequencing were used as input to the established small extracellular vesicle miRNA prediction marker model. According to the calculation results of the model, it was determined that the patient might belong to the good prognosis group or the poor prognosis group. The Kaplan-Meier curve was used for overall survival (OS) and PFS survival analysis.

[0078] The results are as follows Figure 6 and Figure 7 As shown in the data, regardless of the training cohort or the validation cohort, the PFS and OS of the patients in the good prognosis group (responder) predicted by the small extracellular vesicle miRNA prediction marker combination model were significantly better than those in the poor prognosis group (non-responder).

[0079] In summary, the present invention uses small RNA sequencing to analyze the expression differences of plasma exosome miRNA in patients with good prognosis and poor prognosis of advanced squamous cell lung cancer in response to PD-1 inhibitor combined chemotherapy and models, screens biomarkers that can be used to predict the efficacy of combined immunotherapy and chemotherapy for squamous cell lung cancer and screen the benefit population, and verifies the predictive efficacy of the combined model. Provides a non-invasive detection solution for how to more conveniently and accurately distinguish the benefit population from the insufficient benefit population in the combined immunotherapy regimen for advanced squamous cell lung cancer.

[0080] The applicant declares that the present invention illustrates the detailed method of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned detailed method, that is, it does not mean that the present invention must rely on the above-mentioned detailed method to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of various raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung cancer, characterized in that: The biomarkers include any one of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p or hsa-miR-143-3p, or a combination of at least two thereof.

2. Use of the biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma and / or its detection product as described in claim 1 in the preparation of a product for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma.

3. The use according to claim 2, characterized in that: The detection product comprises a reagent and / or an instrument for detecting the expression level of the biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma according to claim 1; Preferably, the reagents and / or instruments include reagents and / or instruments for small RNA sequencing.

4. A product for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung cancer, characterized in that: The product includes a reagent and / or an instrument for detecting the expression level of the biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma as described in claim 1.

5. A method for constructing a prediction model for the efficacy of combined immunotherapy and chemotherapy in patients with squamous cell carcinoma of the lung, characterized in that: The method comprises: obtaining the expression level data of the target marker in the samples that benefited and did not benefit from immunotherapy combined with chemotherapy as a training set, wherein the target marker is the biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma according to claim 1; A prediction model is constructed using a machine learning algorithm based on the classification information of the sample and the level data of the target marker.

6. The method for constructing a prediction model for the therapeutic effect of combined immunotherapy and chemotherapy on patients with lung squamous cell carcinoma according to claim 5, characterized in that: The machine learning algorithm includes a linear support vector machine.

7. A prediction model for the efficacy of combined immunotherapy and chemotherapy in patients with squamous cell lung cancer, characterized in that: The prediction model is obtained by the method for constructing a prediction model for patients with lung squamous cell carcinoma after immunotherapy combined with chemotherapy as described in claim 5 or 6.

8. A device for predicting the efficacy of immunotherapy combined with chemotherapy for lung squamous cell carcinoma, characterized in that: The device comprises: A data module, used to provide expression level data of a target marker of a sample to be tested, wherein the target marker is a biomarker for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma as described in claim 1; A scoring module, used to obtain a probability value through a prediction model based on the expression level data of the target marker of the sample to be tested; wherein the prediction model is the prediction model for the efficacy of combined immunotherapy and chemotherapy for patients with squamous cell carcinoma of the lung as described in claim 7; A judgment module is used to predict the efficacy of immunotherapy combined with chemotherapy based on the probability value.

9. An electronic device comprising one or more processors and a memory for storing executable instructions, characterized in that: The one or more processors are configured to call the executable instructions stored in the memory to implement the functions of the device for predicting the efficacy of immunotherapy combined with chemotherapy for squamous cell lung carcinoma as described in claim 8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by the processor, the functions of the device for predicting the efficacy of immunotherapy combined with chemotherapy for lung squamous cell carcinoma as claimed in claim 8 are realized.

11. Use of a reagent for detecting the biomarker of claim 1 in the preparation of a composition or kit for evaluating and / or predicting the efficacy of lung squamous cell carcinoma cells or patients to immunotherapy combined with chemotherapy.

12. The use according to claim 11, characterized in that: Compared with the control, increased or present expression levels of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, and hsa-miR-5196-3p, or decreased or absent expression levels of hsa-miR-6513-5p and hsa-miR-143-3p indicate that lung squamous cell carcinoma cells or patients may benefit from combined immunotherapy and chemotherapy.

13. The use according to claim 11, characterized in that: The immunotherapy combined with chemotherapy is PD-1 inhibitor combined with chemotherapy, and the PD-1 inhibitor is tilelizumab.

14. The use according to claim 11, characterized in that: The hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p or hsa-miR-143-3p comes from small extracellular vesicles.

15. The use according to claim 14, characterized in that: The small extracellular vesicles are derived from plasma.

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