An early prediction and evaluation method for predicting the efficacy of immunotherapy for non-small cell lung cancer

Abstract

The present invention provides an early prediction and evaluation method for predicting the efficacy of immunotherapy for non-small cell lung cancer, comprising the following steps: screening out biomarkers related to the evaluation of the efficacy, prognosis and adverse reactions of lung cancer immunotherapy with the best sensitivity; performing at least two screenings on the initially screened gene differentially expressed genes; screening for differentially expressed genes at the DNA, RNA and protein levels of the specimens to find common candidate genes; and selecting 2 biomarkers related to the evaluation of the efficacy, prognosis and adverse reactions of lung cancer immunotherapy with the best sensitivity. The present invention uses a new generation of gene sequencing, multi-omics and bioinformatics analysis tools and literature mining methods to screen out biomarkers related to the evaluation of the efficacy, prognosis and adverse reactions of lung cancer immunotherapy with better sensitivity.

Description

Technical Field

[0001] The present invention specifically relates to an early prediction and evaluation method for predicting the efficacy of immunotherapy for non-small cell lung cancer. Background Art

[0002] Lung cancer is the malignant tumor with the highest incidence and mortality globally, with the incidence and mortality accounting for 11.6% and 18.4% of all tumors respectively. Currently, the treatment methods for advanced lung cancer patients mainly include traditional tumor treatment means such as surgery, chemotherapy, and radiotherapy, but the treatment effect is not satisfactory. In recent years, platinum-based chemotherapy is still the first-line treatment option for advanced non-small cell lung cancer patients without target-driven gene mutations. Its objective response rate (0RR) is 25% - 35%, and the median overall survival (OS) and median progression-free survival (PFS) are 8 - 10 and 4 - 6 months respectively. Along with the successive identification of carcinogenic driver genes such as epidermal growth factor receptor (EGFR), targeted therapies such as EGFR-tyrosine kinase inhibitors (EGFR-TKIs) have brought hope to NSCLC patients. Although EGFR-TKIs have achieved great success in the field of NSCLC treatment, the subsequent drug resistance problem is still inevitable, and the treatment methods for patients with negative driver genes are very limited, and the prognosis of advanced patients is still poor. Therefore, new treatment strategies and the development of new drugs, such as immunotherapy, are urgently needed to improve the treatment status of NSCLC patients.

[0003] In 2015, the US Food and Drug Administration approved the first immune checkpoint inhibitor (ICIs), nivolumab, a programmed cell death protein-1 (PD-1) antibody, for the treatment of advanced non-small cell lung cancer (NSCLC), marking that immunotherapy has become the third major treatment for advanced NSCLC after chemotherapy and targeted therapy. Since tumor cells express ICIs signals such as programmed death-ligand 1 (PD-L1) and bind to the receptor PD-1, inhibiting the cell activity of cytotoxic lymphocytes (CTLs) and inducing apoptosis of T cells, ultimately escaping immune surveillance. Currently, the ICIs used in clinical practice mainly competitively bind to the immunosuppressive molecule PD-L1 on the surface of tumor cells, thereby blocking the signal transduction between it and the receptor PDT on the surface of immune cells and reactivating T cells to play an immune surveillance role. Four phase III clinical studies (CheckMate-017, CheckMate-057, KEYNOTE-010, and OAK) all used PD-1 inhibitors (nivolumab and pembrolizumab) and PD-L1 inhibitors (atezolizumab and durvalumab) as the second-line treatment regimens for NSCLC patients after the failure of first-line platinum-based chemotherapy. The results showed that both PD-1 and PD-L1 inhibitors had more significant clinical efficacy than the docetaxel regimen. In addition, the objective response rates (ORRs) of the nivolumab group and the docetaxel group were 20% and 9% respectively (P = 0.008), the 1-year survival rates were 42% and 24% respectively, and the adverse reaction rates were 7% and 55% respectively. Moreover, the expression status of PD-L1 did not affect the efficacy of atezolizumab in the second-line treatment of squamous cell carcinoma and adenocarcinoma. Based on the results of the above randomized clinical studies, the US FDA has approved pembrolizumab, nivolumab, atezolizumab, and durvalumab for the second-line treatment of advanced NSCLC. The results of the Keynote-042 study showed that pembrolizumab demonstrated better efficacy in the first-line treatment of NSCLC patients with a percentage of tumor cells with positive PD-L1 expression greater than or equal to 1%, and the greater the PD-L1 expression, the more significant the patient benefit. Although immunotherapy, especially immune checkpoint inhibitors, may enable the effective population of non-small cell lung cancer to benefit in the long term.However, the objective remission rate in the non-selective population with non-small cell lung cancer is only 10% - 20%. Even in some patients, there is a phenomenon of accelerated tumor growth after immunotherapy, which occurs in 33% - 44% of NSCLC patients. This phenomenon is called hyperprogressive disease (HPD). Once HPD occurs, the median OS of patients in the HPD group is significantly shorter than that of patients in the non-HPD group. The efficacy of immunotherapy significantly decreases, the condition deteriorates, and the prognosis is poor. Therefore, the screening of the advantageous population is still very important. PD-L1 is the most commonly used efficacy prediction biomarker for immunotherapy at present, but there are still certain limitations and it cannot be used as a routine biomarker in clinical practice. Other related studies have shown that tumor mutation burden, tumor-infiltrating lymphocytes, microsatellite instability, etc. are all important biomarkers for predicting the efficacy of immunotherapy. However, there are also deficiencies. Existing biomarkers still cannot effectively guide the selection of the advantageous population and the evaluation of efficacy, and the detection cost is high. It is impossible to predict rare or even extremely rare serious adverse reactions at an early stage. Therefore, it has become an urgent task to explore suitable immunotherapy biomarkers and then accurately select potential beneficiaries of immunotherapy. Therefore, a screening method for non-small cell lung cancer immunotherapy biomarkers is proposed to solve this problem. Summary of the Invention

[0004] The purpose of the present invention is to provide a screening method for non-small cell lung cancer immunotherapy biomarkers in view of the deficiencies of the prior art, and this screening method for non-small cell lung cancer immunotherapy biomarkers can well solve the above problems.

[0005] To meet the above requirements, the technical solution adopted by the present invention is: to provide a screening method for non-small cell lung cancer immunotherapy biomarkers, and this screening method for non-small cell lung cancer immunotherapy biomarkers includes the following steps:

[0006] S1: Screen out the biomarkers related to the efficacy, prognosis and adverse reactions of lung cancer immunotherapy with the best sensitivity;

[0007] S2: Through bioinformatics analysis tools, screen the initially screened gene differentially expressed genes at least twice, and further select more suitable candidate genes for multi-omics sequencing, so as to screen out the final candidate biomarkers;

[0008] S3: Apply multi-omics screening technology to screen differentially expressed genes at the DNA, RNA and protein levels of 40 non-small cell lung cancer tumor tissue specimens, and find more than 10 common candidate genes;

[0009] S4: Conduct a literature review and use bioinformatics tools for analysis, and finally select 3 biomarkers related to the efficacy, prognosis and adverse reactions of lung cancer immunotherapy with the best sensitivity;

[0010] S5: Conduct late-stage laboratory and multi-center clinical validation on the three biomarkers with potential positive value.

[0011] S6: Establish a highly specific, sensitive, safe and non-invasive early prediction and prognosis evaluation bio-marker for non-small cell lung cancer immunotherapy;

[0012] Screen out the non-small cell lung cancer immunotherapy prediction biomarker 1, and the biomarker related to the prognosis and adverse reactions of non-small cell lung cancer immunotherapy is CD266;

[0013] Screen out the non-small cell lung cancer immunotherapy prediction biomarker 2, and the biomarker related to the efficacy and prognosis of non-small cell lung cancer immunotherapy is DNMT3A.

[0014] S7: Establish a highly specific, sensitive, safe and non-invasive early prediction and prognosis evaluation system for non-small cell lung cancer immunotherapy.

[0015] The advantages of this non-small cell lung cancer immunotherapy biomarker screening method are as follows:

[0016] Detect the gene expression in the blood and tissue specimens of lung cancer patients treated with PD1 / PDL1 immune checkpoint inhibitors by next-generation sequencing. Divide the patients into an immunotherapy-sensitive group and an immunotherapy-tolerant group according to their efficacy. Observe the efficacy and adverse reactions of the two groups of patients. Use bioinformatics analysis tools and literature mining methods to screen out several genes with the most obvious differential expression involved in immune regulation. Detect the candidate genes at the DNA, RNA, and protein levels using molecular biology techniques, and finally screen out the biomarkers related to the evaluation of the efficacy and adverse reactions of non-small cell lung cancer immunotherapy with the best sensitivity. Brief Description of the Drawings

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used to represent the same or similar parts in these drawings. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0018] Figure 1 Schematically shows the technical roadmap of the non-small cell lung cancer immunotherapy biomarker screening method according to an embodiment of the present application. Detailed Description of the Embodiments

[0019] To make the objectives, technical solutions, and advantages of the present application clearer, the following further describes the present application in detail with reference to the accompanying drawings and specific embodiments.

[0020] In the following description, references to "an embodiment", "embodiments", "an example", "examples", etc. indicate that the embodiments or examples so described may include specific features, structures, characteristics, properties, elements, or limitations, but not every embodiment or example necessarily includes the specific features, structures, characteristics, properties, elements, or limitations. Additionally, repeated use of the phrase "according to an embodiment of the present application" may, although not necessarily, refer to the same embodiment.

[0021] For simplicity, certain technical features known to those skilled in the art are omitted in the following description.

[0022] According to an embodiment of the present application, a method for screening biomarkers for non - small cell lung cancer immunotherapy is provided, including the following steps:

[0023] S1: Screen out the biomarkers related to the evaluation of the efficacy, prognosis, and adverse reactions of lung cancer immunotherapy with the best sensitivity;

[0024] S2: Through bioinformatics analysis tools, perform at least two screenings on the initially screened differentially expressed genes, and further select more suitable candidate genes for multi - omics sequencing, so as to screen out the final candidate biomarkers;

[0025] S3: Apply multi - omics screening technology to screen for differentially expressed genes at the DNA, RNA, and protein levels in 40 non - small cell lung cancer tumor tissue specimens, and find more than 10 common candidate genes;

[0026] S4: Conduct a literature review and use bioinformatics tools for analysis, and finally select 3 biomarkers related to the evaluation of the efficacy, prognosis, and adverse reactions of lung cancer immunotherapy with the best sensitivity;

[0027] S5: Conduct late - stage laboratory and multi - center clinical verification on the 3 biomarkers with potential positive value.

[0028] S6: Establish a highly specific, sensitive, safe, and non - invasive early prediction and prognosis evaluation bio - marker for non - small cell lung cancer immunotherapy; Screen out non - small cell lung cancer immunotherapy prediction biomarker 1, and the biomarker related to predicting the prognosis and adverse reactions of non - small cell lung cancer immunotherapy is CD266;

[0029] Screen out non - small cell lung cancer immunotherapy prediction biomarker 2, and the biomarker related to predicting the efficacy and prognosis of non - small cell lung cancer immunotherapy is DNMT3A.

[0030] S7: Establish a highly specific, sensitive, safe, and non - invasive early prediction and prognosis evaluation system for non - small cell lung cancer immunotherapy.

[0031] According to an embodiment of the present application, in step S1 of the method for screening biomarkers for non-small cell lung cancer immunotherapy, the latest generation of gene sequencing and bioinformatics analysis tools and literature mining methods are applied for biomarker screening.

[0032] According to an embodiment of the present application, the literature mining method of the method for screening biomarkers for non-small cell lung cancer immunotherapy is specifically as follows: Six non-small cell lung cancer cohorts receiving immune checkpoint inhibitor treatment are selected, namely YeonKim et.al, Hwang et.al, Rizvi et.al, Miao et.al, Samstein et.al, and Prat et.al;

[0033] The Hwang et.al, Prat et.al, and Yeon Kim et.al cohorts contain expression data and clinical prognosis data;

[0034] The Rizvi et.al, Miao et.al, and Samstein et.al cohorts contain mutation data and clinical prognosis data;

[0035] For the expression data, according to the median value of each gene, it is divided into a high-expression group and a low-expression group respectively;

[0036] For the mutation data, according to the definition of non-synonymous mutations in the maftools R package, the non-synonymous mutation status of each gene is grouped according to mutant and wild-type;

[0037] For the copy number variation data, non-small cell lung cancer patients are divided into an amplification group and a non-amplification group or a deletion group and a non-deletion group;

[0038] A univariate cox regression model is used to analyze the impact of each gene (high expression versus low expression; mutant versus wild-type, amplification group versus non-amplification group or deletion group versus non-deletion group) on the prognosis of non-small cell lung cancer patients;

[0039] For the immune therapy adverse reaction data, adverse event reports of all non-small cell patients who received anti-PD1 / PD-L1 treatment from 2015 to 2021 were obtained from the FAERS database (FDA Adverse Event Reporting System Database), and the adverse events were classified according to the existing immune-related adverse reaction guidelines. The irAE ROR reporting oddsratio, ROR, the ratio of irAEs caused by anti-PD1 / PD-L1 reagents to irAEs caused by other drugs reported in the database was calculated as an index to measure the amount of irAEs caused by immune therapy;

[0040] Through gene enrichment analysis of these genes, single factors highly correlated with ROR were selected, and then factors were added to construct a bivariate regression model. Spearman correlation analysis was used to find the correlation between immune therapy adverse reactions and candidate genes.

[0041] According to an embodiment of the present application, the method for screening biomarkers for non-small cell lung cancer immunotherapy further includes using Kaplan-Meier analysis to further visualize the relationship between each of more than 2,300 genes and the efficacy, prognosis, and adverse reactions of immunotherapy for non-small cell lung cancer patients.

[0042] According to an embodiment of the present application, in the step of using a univariate cox regression model to analyze each gene in the method for screening biomarkers for non-small cell lung cancer immunotherapy, high expression is compared with low expression; mutant type is compared with wild type, amplified group is compared with non-amplified group, or deletion group is compared with non-deletion group.

[0043] According to an embodiment of the present application, in step S2 of the method for screening biomarkers for non-small cell lung cancer immunotherapy, 172 more suitable candidate genes are selected for multi-omics sequencing after at least two screenings.

[0044] According to an embodiment of the present application, in step S3 of the method for screening biomarkers for non-small cell lung cancer immunotherapy, whole exome sequencing, ordinary transcriptome sequencing, and protein TMT sequencing are used.

[0045] According to an embodiment of the present application, the bioinformatics tools in step S4 of the method for screening biomarkers for non-small cell lung cancer immunotherapy include GSEA functional enrichment and network analysis, random survival forest analysis, Cox proportional hazards regression model analysis, Graphical Lasso Estimation for analyzing multiple genes, principal component analysis, and survival tree analysis.

[0046] According to an embodiment of the present application, in step S5 of the method for screening biomarkers for non-small cell lung cancer immunotherapy, verification is carried out in the following manner: peripheral blood of 30 non-small cell lung cancer patients is collected, and 3 important functional biomarkers are verified by RT-PCR, Western blot, and immunohistochemistry; all 30 non-small cell lung cancer patients meet the following requirements: single-agent treatment with PD-1 / PD-L1 inhibitors, without restricting the dose and course of PD-1 / PD-L1 inhibitors.

[0047] According to an embodiment of the present application, a method for screening biomarkers for non-small cell lung cancer immunotherapy is provided, including the following steps:

[0048] Step1: Steps for performing bioinformatics analysis to mine data related to lung cancer immunotherapy;

[0049] Step2: Steps for collecting clinical data;

[0050] Step3: Steps for performing gene mining;

[0051] Among them, the steps for performing gene mining in Step3 are specifically as follows:

[0052] Step31: Apply multi-omics screening technology to screen differentially expressed genes at three levels of DNA, RNA, and protein in gene expression profile data; S32: Steps for deeply mining the screened genes; The Step31: Apply multi-omics screening technology to screen differentially expressed genes at three levels of DNA, RNA, and protein in gene expression profile data, and the specific steps are as follows: Step311: Collect blood or tissue specimens from 50 - 70 non-small cell lung cancer patients before immunotherapy; Step312: Perform sample extraction and processing; Step313: Construct a gene library; Step314: Perform probe enrichment; Step315: Perform high-throughput sequencing: The captured library is loaded onto the Illumina HiSeq 4000 high-throughput sequencing platform according to the kit instructions. DNA forms DNA clusters on the kit Flew cell, and the sequencing platform completes DNA high-throughput sequencing through cycles of single-base synthesis, pause, fluorescence detection, and synthesis recovery; Step316: Perform bioinformatics analysis; The S32: Steps for deeply mining the screened genes are specifically as follows: After 3 cycles of immunotherapy, non-small cell lung cancer patients are divided into an immunotherapy-sensitive group and a tolerant group according to the curative effect. Observe the curative effect and adverse reactions of the two groups of patients, and apply the previous bioinformatics database to screen genes with significantly different expressions to determine molecular biomarkers suitable for early clinical prediction, prognosis, and adverse reaction assessment.

[0053] According to an embodiment of the present application, the method for screening biomarkers for non-small cell lung cancer immunotherapy further includes steps for performing clinical experiment verification, specifically including:

[0054] Steps for obtaining clinical plasma specimens and steps for molecular biology detection of plasma specimens;

[0055] Among them, the steps for obtaining the clinical plasma specimens are specifically as follows: The research subjects are non-small cell lung cancer patients, regardless of age and gender. The inclusion and exclusion criteria are the same as before. After collecting 10 ml of peripheral blood specimens from 30 - 50 patients and centrifuging, the plasma is aliquoted into imported enzyme-free EP tubes and stored in a -70°C refrigerator for testing;

[0056] The steps of the molecular biology detection of the plasma specimen specifically include: extracting the genes of the plasma specimen and detecting them by fluorescence quantitative PCR method, and extracting the proteins of the plasma specimen and detecting them by Western blotting method.

[0057] According to an embodiment of the present application, the method for screening biomarkers for non-small cell lung cancer immunotherapy further includes the step of verifying the discovered important functional genes and proteins, specifically including:

[0058] Verification of important functional genes and proteins by RT-PCR, Western blot and immunohistochemistry: quantitatively verifying specific differential miRNAs by quantitative RT-PCR;

[0059] Combining clinical data and analyzing the Kaplan-Meier survival curve of non-small cell lung cancer patients according to the Logrank test, and further studying the function and therapeutic target of specific genes.

[0060] Performing Western blot semi-quantitative verification on differential protein spots, and simultaneously performing immunohistochemical determination to determine the intracellular localization.

[0061] According to an embodiment of the present application, the method for screening biomarkers for non-small cell lung cancer immunotherapy further includes the step of organizing and statistically analyzing experimental data in combination with clinical specimen data.

[0062] According to an embodiment of the present application, the steps of performing bioinformatics analysis to mine data related to non-small cell lung cancer immunotherapy in Step1 of the method for screening biomarkers for non-small cell lung cancer immunotherapy specifically include:

[0063] Step11: Using the expression data of lung cancer samples with clinical information from GEO, TCGA and CGGA;

[0064] Step12: Performing the step of deciphering the mutation characteristics acting in the genome;

[0065] Step13: Performing the steps of feature extraction and immune response research;

[0066] Step14: Performing the step of gene annotation: annotating, visualizing and integrating genes of interest using GOstat2.5, and performing online annotation using databases;

[0067] Step15: Performing the step of cluster analysis: processing gene expression values by the average value from patients, and then performing hierarchical cluster analysis and plotting a heat map for visualization;

[0068] Step16: Performing the steps of GSEA functional enrichment and network analysis;

[0069] Step 17: Steps for performing a random survival forest analysis: Use the method of random survival forest to distinguish the relationship between gene expression and survival;

[0070] Step 18: Steps for performing a Cox proportional hazards regression model analysis;

[0071] Step 19: Steps for analyzing multiple genes using Graphical Lasso Estimation;

[0072] Step 110: Steps for performing principal component analysis and survival tree analysis;

[0073] Step 111: Conduct late-stage laboratory and large-sample, multi-center clinical validation on markers with potential positive value, and establish a high-specificity, high-sensitivity, safe and non-invasive early prediction and prognostic evaluation system for non-small cell lung cancer immunotherapy.

[0074] According to an embodiment of the present application, the steps for collecting clinical data in Step 2 of the method for screening biomarkers for non-small cell lung cancer immunotherapy specifically include:

[0075] The selected research subject criteria are as follows: patients with stage IIIb or IV non-small cell lung cancer who are over 18 years old and diagnosed by pathology or cytology, and who have no autoimmune-related diseases and have not used immune checkpoint inhibitors according to the Eastern Cooperative Oncology Group;

[0076] Intervention measures: Monotherapy with PD-1 / PD-L1 inhibitors, without restricting the dose and course of PD-1 / PD-L1 inhibitors;

[0077] Exclusion criteria include: non-clinical trial studies, incomplete data, studies from which relevant data required for this analysis cannot be extracted, and lung cancer patients who received combined first-line or second-line treatment with PD-1 / PD-L1 inhibitors during the trial;

[0078] The specific efficacy evaluation criteria are as follows:

[0079] Complete remission: All lesions are confirmed to have disappeared at two consecutive observation points with an interval of at least 4 weeks;

[0080] Partial remission: The total tumor burden is confirmed to have decreased by 50% or more compared to the baseline tumor burden at two consecutive observation points with an interval of at least 4 weeks;

[0081] Stable: The total tumor burden is confirmed to have decreased by less than 50% or increased by less than 25% compared to the baseline tumor burden at two consecutive observation points with an interval of at least 4 weeks;

[0082] Progress: The total tumor burden is detected to have increased by at least 25% compared to the baseline tumor burden at any time at two consecutive observation points with an interval of at least 4 weeks.

[0083] According to an embodiment of the present application, the preliminary bioinformatics databases of the non-small cell lung cancer immunotherapy biomarker screening method include: GEO, TCGA, and CGGA.

[0084] According to an embodiment of the present application, when determining molecular biomarkers suitable for early clinical prediction, prognosis, and adverse reaction assessment in step Step32 of the non-small cell lung cancer immunotherapy biomarker screening method, the biological processes, subcellular distributions, molecular function clustering of genes, and literature analysis on the correlation between lung cancer immunotherapy mined from bioinformatics and literature are combined.

[0085] This application explores biomarkers for efficacy prediction and prognosis assessment specific to non-small cell lung cancer immunotherapy from the international forefront of this field. Specific biomarkers related to non-small cell lung cancer immunotherapy are screened through high-throughput next-generation sequencing. And it is a precedent to further verify specific biomarkers through large-scale clinical specimens. At the same time, by combining the gene and protein levels and using these advanced technologies and methods to screen for biomarkers, this project has stronger scientificity and persuasiveness. And in-depth biological characteristic analysis is carried out on the discovered non-small cell lung cancer immunotherapy-related biomarkers with potential value, and large-sample multi-center verification work is carried out on the biomarkers with potential positive value in the later stage, to establish a highly specific, sensitive, safe and non-invasive early prediction and prognosis assessment bio-marker for non-small cell lung cancer immunotherapy. Currently, no relevant public technical solutions have been seen at home and abroad.

[0086] This application detects gene expression in blood and tissue specimens of lung cancer patients treated with PD1 / PDL1 immune checkpoint inhibitors by next-generation sequencing. According to the efficacy of the patients, they are divided into an immunotherapy-sensitive group and an immunotherapy-tolerant group. The efficacy and adverse reactions of the two groups of patients are observed. Several genes with the most obvious differential expression involved in immune regulation are screened out by using bioinformatics analysis tools and literature mining methods. Molecular biology techniques are used to detect candidate genes at the DNA, RNA, and protein levels respectively. Finally, biomarkers related to the efficacy and adverse reaction assessment of non-small cell lung cancer immunotherapy with the best sensitivity are screened out. And large-sample multi-center clinical verification is carried out on the biomarkers with potential positive value in the later stage, to establish a highly specific, sensitive, safe and non-invasive early prediction and prognosis assessment bio-marker for non-small cell lung cancer immunotherapy.

[0087] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the claims described above.

Claims

1. A screening method for immunotherapy biomarkers of non-small cell lung cancer, characterized in that, It includes the following steps: S1: Screen out the biomarkers related to the efficacy, prognosis and adverse reactions of lung cancer immunotherapy with the best sensitivity; Select 6 non-small cell lung cancer cohorts receiving immune checkpoint inhibitor therapy, namely Yeon Kim et.al, Hwang et.al, Rizvi et.al, Miao et.al, Samstein et.al and Prat et.al; The Hwang et.al, Prat et.al and Yeon Kim et.al cohorts contain expression data and clinical prognosis data; The Rizvi et.al, Miao et.al and Samstein et.al cohorts contain mutation data and clinical prognosis data; For the expression data, divide it into high-expression group and low-expression group according to the median value of each gene; For the mutation data, according to the definition of non-synonymous mutations in the maftools R package, group the non-synonymous mutation status of each gene into mutant type and wild type; For the copy number variation data, divide non-small cell lung cancer patients into amplification group and non-amplification group or deletion group and non-deletion group; Use a univariate cox regression model to analyze each gene's impact on the prognosis of non-small cell lung cancer patients; For the immune therapy adverse reaction data, obtain the adverse event reports of all non-small cell patients who received anti-PD1 / PD-L1 therapy from 2015 to 2021 from the FAERS database, classify the adverse events according to the existing immune-related adverse reaction guidelines, and calculate the irAE ROR as an indicator to measure the amount of irAEs caused by immune therapy; Through gene enrichment analysis of these genes, select the univariate factors highly correlated with ROR and then add factors to construct a bivariate regression model, and use Spearman correlation analysis to find the correlation between immune therapy adverse reactions and candidate genes; S2: Through bioinformatics analysis tools, screen the initially screened differentially expressed genes at least twice, further select more suitable candidate genes for multi-omics sequencing, so as to screen out the final candidate biomarkers; S3: Apply multi-omics screening technology to screen differentially expressed genes at the DNA, RNA and protein levels of 40 non-small cell lung cancer tumor tissue specimens, and find more than 10 common candidate genes; S4: Conduct a literature review and use bioinformatics tools for analysis, and finally select 3 biomarkers related to the efficacy, prognosis and adverse reactions of lung cancer immunotherapy with the best sensitivity; S5: Conduct late-stage laboratory and clinical multi-center clinical verification on the 3 biomarkers with potential positive value; S6: Establish a highly specific, sensitive, safe and non-invasive early prediction and prognosis assessment bio-marker for non-small cell lung cancer immunotherapy; Screen out the non-small cell lung cancer immunotherapy prediction biomarker 1, and the biomarker related to the prognosis and adverse reactions of non-small cell lung cancer immunotherapy is CD266; Screen out the immunotherapy prediction marker 2 for non-small cell lung cancer, and the biomarker related to predicting the efficacy and prognosis of non-small cell lung cancer immunotherapy is DNMT3A; S7: Establish a highly specific, sensitive, safe and non-invasive early prediction and prognosis evaluation system for non-small cell lung cancer immunotherapy.

2. The method for screening biomarkers for immunotherapy of non-small cell lung cancer according to claim 1, wherein In step S1, the latest generation of gene sequencing, bioinformatics analysis tools, literature mining methods are applied to screen for biomarkers.

3. The method for screening biomarkers for immunotherapy of non-small cell lung cancer according to claim 2, wherein: It also includes using Kaplan-Meier analysis to further visualize the relationship between each of more than 2,300 genes and the immunotherapy efficacy, prognosis and adverse reactions of non-small cell lung cancer patients.

4. The method for screening biomarkers for immunotherapy of non-small cell lung cancer according to claim 1, wherein: In the step of using a univariate cox regression model to analyze each gene, high expression is compared with low expression; mutant type is compared with wild type, amplified group is compared with non-amplified group or deletion group is compared with non-deletion group.

5. The method for screening biomarkers for immunotherapy of non-small cell lung cancer according to claim 1, wherein In step S2, 172 more suitable candidate genes are selected for multi-omics sequencing after at least two screenings.

6. The method for screening biomarkers for immunotherapy of non-small cell lung cancer according to claim 1, wherein In step S3, whole exome sequencing, ordinary transcriptome sequencing, and protein TMT sequencing are used.

7. The method for screening biomarkers for immunotherapy of non-small cell lung cancer according to claim 1, characterized in that The bioinformatics tools in step S4 include GSEA functional enrichment and network analysis, random survival forest analysis, Cox proportional hazards regression model analysis, Graphical Lasso Estimation to analyze multiple genes, principal component analysis, and survival tree analysis.

8. The method for screening biomarkers for immunotherapy of non-small cell lung cancer according to claim 1, wherein In step S5, the following method is used for verification: Collect peripheral blood from 30 non-small cell lung cancer patients and verify 3 important functional biomarkers by RT-PCR, Western blot and immunohistochemistry; The 30 non-small cell lung cancer patients all meet the following requirements: Monotherapy with PD-1 / PD-L1 inhibitors, without restricting the dose and course of PD-1 / PD-L1 inhibitors, and 10 of them have adverse reactions after immunotherapy.

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