CN116287273A - Biomarker for evaluating prognosis and adverse reaction of non-small cell lung cancer immunotherapy - Google Patents
Biomarker for evaluating prognosis and adverse reaction of non-small cell lung cancer immunotherapyDownload PDFInfo
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Abstract
Description
Technical Field
The invention particularly relates to a biomarker for evaluating prognosis of non-small cell lung cancer immunotherapy and adverse reaction.
Background
Lung cancer is the malignant tumor with highest global morbidity and mortality, and the morbidity and mortality respectively account for 11.6% and 18.4% of all tumors. At present, the treatment method for patients with advanced lung cancer mainly adopts traditional tumor treatment means such as surgery, chemotherapy, radiotherapy and the like, but has poor treatment effect. In recent years, platinum-based chemotherapy has remained the first-line treatment option for patients with advanced non-small cell lung cancer without targeted driver gene mutations. The objective remission rate (objective response rate,0 RR) is 25-35%, and the median total survival (OS) and median progression-free survival (PFS) are 8T0 and 4-6 months respectively. With the sequential identification of oncogenes such as epidermal growth factor receptor (epidermal growth factor receptor, EGFR), targeted therapies such as EGFR-tyrosine kinase inhibitors (EGFR-tyrosine kinase inhibitors, EGFR-TKIs) hold promise for NSCLC patients. Despite the tremendous success of EGFR-TKIs in the field of NSCLC treatment, the concomitant problem of drug resistance remains unavoidable, and the treatment regimen driving gene negative patients is very limited and the prognosis for advanced patients remains poor. Thus, there is a need for new therapeutic strategies and the development of new drugs, such as immunotherapy, that improve the current state of treatment of NSCLC patients.
In 2015, the U.S. food and drug administration approved the first immune checkpoint inhibitor (immune checkpoint inhibitors, ICIs) programmed death protein 1 (programmed cell death protein-1, pd-1) antibody Nivolumab (Nivolumab) for the treatment of advanced non-small cell lung cancer, marking that immunotherapy became the third largest treatment for advanced NSCLC following chemotherapy and targeted therapy. Since tumor cells inhibit the cellular activity of cytotoxic T lymphocytes (cytotoxic lymphocyte, CTLs) by expressing ICIs signals such as programmed death protein ligand 1 (PD-L1) and binding to receptor PD-1, induction of T cell apoptosis ultimately evades immune surveillance. The ICIs applied in clinic at present mainly block the signal transduction between the ICIs and the receptor PDT on the surface of immune cells by competitively combining with the immunosuppressive molecule PD-L1 on the surface of tumor cells, and reactivate the T cells to play an immune monitoring role. The PDT inhibitors (Nivolumab and Pembrolizumab) and PD-L1 inhibitors (Atezolizumab and Durvalumab) were used as secondary treatment regimens for NSCLC patients following primary chemotherapy first-line failure in 4 phase III clinical studies (CheckMate-017, checkMate-057, KEYN0TE-010 and OAK). The result shows that more docetaxel schemes of the PD-1 and the PD-L1 inhibitor have more remarkable clinical effects. In addition, ORR of Nivolumab group and docetaxel group were 20% and 9% (p=0.008), survival rate of 42% and 24% in 1 year, adverse reaction rate was 7% and 55%, respectively, and expression state of PD-L1 did not affect therapeutic effect of atezolizumab in two-line treatment of squamous cell carcinoma and adenocarcinoma. Based on the results of the above randomized clinical study, the us FDA has approved Pembrolizumab > Nivolumab, atezolizumab and Durvalumab for the second line treatment of advanced NSCLC. Research results of Keynote-042 show that Pembrolizumab has better curative effect in first-line treatment of patients with NSCLC, wherein the percentage of the PD-L1 positive expression tumor cells is greater than or equal to 1%, and the patients benefit more remarkably along with the increase of PD-L1 expression. Although immunotherapy, and in particular immune checkpoint inhibitors, can benefit the effective population of non-small cell lung cancer for a long period of time. However, the objective remission rate of non-selective populations in non-small cell lung cancer is only 10% -20%, and even some patients have a phenomenon of accelerated tumor growth after receiving immunotherapy, which occurs as high as 33% -44% in NSCLC patients, and this phenomenon is called rapid progression (HPD). Once HPD occurs, the median 0S of the HPD group is obviously shortened compared with that of the patients in the non-HPD group, the curative effect of immunotherapy is obviously reduced, the disease condition is worsened, and the prognosis is poor. Therefore, the screening of dominant population is still important. PD-L1 is the most commonly used therapeutic effect prediction marker for immunotherapy at present, but has certain limitations and cannot be applied clinically as a conventional marker. Other related studies have shown that: tumor mutation load, tumor infiltrating lymphocytes, microsatellite instability and the like are all important biomarkers for predicting the curative effect of immunotherapy. However, the existing biomarkers have the defects that the selection and the curative effect evaluation of the dominant population cannot be effectively guided, the detection cost is high, and the rare even rare serious adverse reactions and the like cannot be predicted in an early stage. Therefore, it is urgent to search for suitable immunotherapeutic biomarkers, and thus precisely select the population potentially benefiting from immunotherapy.
Disclosure of Invention
The invention aims to provide a screening method of a non-small cell lung cancer immunotherapy biomarker, which can well solve the problems.
In order to meet the requirements, the invention adopts the following technical scheme: a biomarker for assessing prognosis and adverse reaction of non-small cell lung cancer immunotherapy, the biomarker comprising:
the biomarker related to the prognosis of the immunotherapy and the adverse reaction of the non-small cell lung cancer is CD266 protein;
the biomarker related to the curative effect and prognosis of the non-small cell lung cancer immunotherapy is DNMT3A gene.
The invention has the following advantages:
the gene expression in the blood and tissue specimens of lung cancer patients treated by PD1/PDL1 immunodetection point inhibitors is detected by a new generation sequencing method, the lung cancer patients are divided into an immune treatment sensitive group and an immune treatment tolerant group according to the curative effects of the patients, the curative effects and adverse reactions of the two groups of patients are observed, and a bioinformatics analysis tool and a literature mining method are used for screening out a plurality of genes which are most obviously expressed and participate in the regulation of immunity. The candidate genes are respectively detected at three levels of DNA, RNA and protein by using a molecular biology technology, and finally, biomarkers with the best sensibility and relevant curative effects of non-small cell lung cancer immunotherapy and adverse reaction evaluation are screened out.
Drawings
The accompanying drawings, where like reference numerals refer to identical or similar parts throughout the several views and which are included to provide a further understanding of the present application, are included to illustrate and explain illustrative examples of the present application and do not constitute a limitation on the present application. In the drawings:
fig. 1 schematically illustrates a technical scheme of a non-small cell lung cancer immunotherapy biomarker screening method according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and specific embodiments.
In the following description, references to "one embodiment," "an embodiment," "one example," "an example," etc., indicate that the embodiment or example so described may include a particular feature, structure, characteristic, property, element, or limitation, but every embodiment or example does not necessarily include the particular feature, structure, characteristic, property, element, or limitation. In addition, repeated use of the phrase "according to an embodiment of the present application" does not necessarily refer to the same embodiment, although it may.
Certain features have been left out of the following description for simplicity, which are well known to those skilled in the art.
Provided is a biomarker for evaluating prognosis and adverse reaction of non-small cell lung cancer immunotherapy, which is specifically as follows: the biomarker related to the prognosis of the immunotherapy and the adverse reaction of the non-small cell lung cancer is CD266 protein; the biomarker related to the curative effect and prognosis of the non-small cell lung cancer immunotherapy is DNMT3A gene.
According to one embodiment of the present application, there is provided a method for screening a biomarker for immunotherapy of non-small cell lung cancer, comprising the steps of:
s1: screening biomarkers related to lung cancer immunotherapy curative effect, prognosis and adverse reaction evaluation with the best sensitivity;
s2: screening the gene differential expression genes obtained by preliminary screening at least twice through a bioinformatics analysis tool, and further selecting more suitable candidate genes for multi-group chemical sequencing so as to screen out final candidate markers;
s3: screening differentially expressed genes at three levels of DNA, RNA and protein of 40 non-small cell lung cancer tumor tissue samples by using a multi-group screening technology to find more than 10 common candidate genes;
s4: performing literature review, analyzing by using a bioinformatics tool, and finally selecting 3 biomarkers with the best sensitivity, which are relevant to lung cancer immunotherapy curative effect, prognosis and adverse reaction evaluation;
s5: later laboratory and clinical multi-center clinical validation was performed on the 3 biomarkers with potential positive value.
S6: establishing a non-small cell lung cancer immunotherapy high-specificity and sensitivity safe noninvasive early prediction and prognosis evaluation bio-marker; screening out a non-small cell lung cancer immunotherapy prediction marker 1, and predicting the curative effect of the non-small cell lung cancer immunotherapy and a biomarker (bio-marker) related to prognosis as DNMT3A; screening out a non-small cell lung cancer immunotherapy prediction marker 2, and predicting the prognosis of the non-small cell lung cancer immunotherapy and a biomarker (bio-marker) related to adverse reaction to be CD266.
According to one embodiment of the present application, the latest generation of genetic sequencing and bioinformatics analysis tools and literature mining methods and biomarker screening are applied in step S1 of the non-small cell lung cancer immunotherapy biomarker screening method.
According to one embodiment of the present application, the literature-mining method of the non-small cell lung cancer immunotherapy biomarker screening method is specifically as follows: selecting 6 non-small cell lung cancer cohorts treated with immune checkpoint inhibitors, yeon Kim et al, hwang et al, rizvi et al, miao et al, samstein et al, and Prat et al, respectively;
the Hwang et al, prat et al and Yeon Kim et al queues contain expression data and clinical prognosis data;
the Rizvi et al, miao et al and Samstein et al queues contain mutation data and clinical prognosis data;
for expression data, the high expression group and the low expression group are respectively carried out according to the median value of each gene;
for mutation data, the non-synonymous mutation status of each gene was grouped according to mutant and wild type according to the definition of non-synonymous mutation in the maftools R package;
for copy number variation data, non-small cell lung cancer patients are divided into amplified and non-amplified or deleted and non-deleted groups;
a one-way cox regression model was used to analyze the effect of each gene (high expression versus low expression; mutant versus wild type, amplified vs. non-amplified vs. deleted vs. non-deleted) on prognosis of non-small cell lung cancer patients.
For the immunotherapy adverse reaction data, acquiring adverse event reports of all non-small cell patients receiving anti-PD 1/PD-L1 treatment from 2015 to 2021 from an FAERS database (FDA adverse event reporting system database), classifying the adverse events according to the existing immune related adverse reaction guidelines, and calculating irAE ROR reporting odds ratio, ROR, and the ratio of the irAEs caused by the anti-PD 1/PD-L1 reagent to the irAEs caused by other medicines reported in the database as indexes for measuring the amount of the irAEs caused by the immunotherapy;
through the gene enrichment analysis of the genes, single factors highly related to ROR are selected, then factors are added to construct a bivariate regression model, and Spearman correlation analysis is adopted to find out the correlation between the adverse immune response and candidate genes.
According to one embodiment of the present application, the non-small cell lung cancer immunotherapy biomarker screening method further comprises using Kaplan-Meier analysis to further visualize the correlation between the immunotherapy efficacy, prognosis and adverse reaction of each of 2300 the plurality of genes to the non-small cell lung cancer patient.
According to one embodiment of the present application, the step of analyzing each gene using a single factor cox regression model in the non-small cell lung cancer immunotherapy biomarker screening method employs high expression versus low expression; mutant versus wild type, amplified versus non-amplified versus deleted versus non-deleted.
According to one embodiment of the present application, 172 more suitable candidate genes are selected for multiplex chemical sequencing after at least two screenings in step S2 of the non-small cell lung cancer immunotherapeutic biomarker screening method.
According to one embodiment of the present application, the step S3 of the non-small cell lung cancer immunotherapy biomarker screening method is performed using whole exon sequencing, normal transcriptome sequencing, protein TMT sequencing.
According to one embodiment of the present application, the bioinformatics tools described in step S4 of the non-small cell lung cancer immunotherapy biomarker screening method include GSEA function enrichment and network analysis, random survival forest analysis, cox risk ratio regression model analysis, graphical Lasso Estimation to analyze multiple genes, principal component analysis, and survival tree analysis.
According to one embodiment of the present application, the non-small cell lung cancer immunotherapy biomarker screening method is validated in step S5 as follows: collecting peripheral blood of 30 cases of non-small cell lung cancer patients, and verifying 3 important functional biomarkers by adopting RT-PCR, western blot and immunohistochemistry; 30 patients with non-small cell lung cancer all meet the following requirements: the PD-1/PD-L1 inhibitor single drug treatment is adopted, the dosage and the treatment course of the PD-1/PD-L1 inhibitor are not limited, and 10 patients have adverse reactions after immunotherapy.
According to one embodiment of the present application, there is provided a method for screening a biomarker for immunotherapy of non-small cell lung cancer, comprising the steps of:
step1: a step of analyzing and mining data related to lung cancer immunotherapy by bioinformatics;
step2: collecting clinical data;
step3: a step of gene excavation;
the Step3 gene excavation comprises the following steps:
step31: screening differentially expressed genes at three levels of DNA, RNA and protein in gene expression profile data using a multiple-set of chemical screening techniques; step32: deep digging the screened genes; the Step31: the method for screening the differentially expressed genes in three levels of DNA, RNA and protein in gene expression profile data by using a multi-group screening technology comprises the following specific steps: step311: collecting blood or tissue samples of 50-70 patients with non-small cell lung cancer before immunotherapy; step312: sample extraction and treatment are carried out; step313: constructing a gene library; step314: carrying out probe enrichment; step315: high throughput sequencing was performed: the captured library is loaded on a Illumina HiSeq 4000 high-throughput sequencing platform according to a kit description, DNA forms a DNA cluster on a kit Flew cell, and the sequencing platform completes DNA high-throughput sequencing through single base synthesis after suspension, fluorescence detection and synthesis recovery; step316: performing a bioinformatic analysis; step32: the step of deep excavation of the screened genes is specifically as follows: after 3-period immunotherapy, dividing the patients with non-small cell lung cancer into an immunotherapy sensitive group and a tolerance group according to the curative effect, observing the curative effect and adverse reaction of the two groups of patients, screening differential expression obvious genes by using a pre-bioinformatics database, and determining molecular biomarkers suitable for clinical early prediction, prognosis and adverse reaction evaluation.
According to one embodiment of the present application, the non-small cell lung cancer immunotherapy biomarker screening method further comprises the step of performing clinical experimental verification, and specifically comprises the following steps:
a step of acquiring a clinical plasma specimen and a step of molecular biological detection of the plasma specimen;
the method comprises the following steps of: the study object selects non-small cell lung cancer patients, the age and sex are unlimited, before inclusion and exclusion criteria are met, 10ml whole blood of peripheral blood samples of 30-50 patients are collected and centrifuged, and then plasma is taken and split-packed into an imported enzyme-removed EP tube for preservation in a refrigerator at-70 ℃ for detection;
the steps of the molecular biological detection of the plasma specimen specifically comprise: the extracted plasma sample genes are detected by adopting a fluorescent quantitative PCR method, and the extracted plasma sample proteins are detected by adopting a Western blotting method.
According to one embodiment of the present application, the non-small cell lung cancer immunotherapy biomarker screening method further comprises the step of verifying the discovered important functional genes and proteins, and specifically comprises the following steps:
verification of important functional genes and proteins by RT-PCR, western blot and immunohistochemistry: quantitative RT-PCR is used for quantitatively verifying the specific differential miRNA;
analyzing Kaplan-Meier survival curves of non-small cell lung cancer patients according to the Logank test by combining clinical data, and further researching functions of the non-small cell lung cancer patients and serving as targets of treatment aiming at specific genes;
western blot semi-quantitative verification is carried out on the differential protein spots, and immunohistochemical measurement is carried out at the same time so as to determine the positioning condition in cells.
According to one embodiment of the present application, the non-small cell lung cancer immunotherapy biomarker screening method further comprises the step of combining clinical specimen data arrangement and statistical experimental data.
According to one embodiment of the present application, step1 of the non-small cell lung cancer immunotherapy biomarker screening method specifically comprises the steps of performing bioinformatics analysis to mine lung cancer immunotherapy-related data:
step11: using expression data from lung cancer samples with clinical information from GEO, TCGA and CGGA;
step12: a step of disrupting a functional mutant feature in the genome;
step13: performing feature extraction and immune response research;
step14: the procedure of gene annotation: annotating, visualizing and integrating genes of interest using gostat2.5, online annotating using a database;
step15: and (3) performing cluster analysis: the gene expression value is processed through an average value from a patient, hierarchical clustering analysis is then carried out, and a heat map is drawn so as to realize visualization;
step16: performing GSEA function enrichment and network analysis;
step17: and (3) carrying out random survival forest analysis: distinguishing the relation between gene expression and survival by using a random survival forest method;
step18: performing Cox risk proportion regression model analysis;
step19: a step of analyzing a plurality of genes by Graphical Lasso Estimation;
step110: performing principal component analysis and survival tree analysis;
step111: and (3) carrying out clinical verification of a later laboratory and multiple centers of clinical large samples on the markers with potential positive values, and establishing a safe and noninvasive early prediction and prognosis evaluation system with high specificity and sensitivity for non-small cell lung cancer immunotherapy.
According to one embodiment of the present application, the Step2 of the non-small cell lung cancer immunotherapy biomarker screening method specifically comprises the steps of:
the selected study criteria were as follows: patients with stage IIIb or IV non-small cell lung cancer older than 18 years of age and diagnosed pathologically or cytologically, while Eastern Cooperative Oncology Group were free of autoimmune-related disease and not using immune checkpoint inhibitors;
intervention measures: the PD-1/PD-L1 inhibitor single drug treatment is adopted, so that the dosage and the treatment course of the PD-1/PD-L1 inhibitor are not limited;
the exclusion index includes: non-clinical trial study, incomplete data, study incapable of extracting relevant data required in the analysis, lung cancer patients who received PD-1/PD-L1 inhibitor in combination with other first-line or second-line treatments in the trial;
the efficacy evaluation criteria were as follows:
complete alleviation: confirm the disappearance of all lesions at two consecutive observation points separated by not less than 4 weeks;
partial relief: at least two consecutive observation points separated by 4 weeks each confirm a 50% or more decrease in total tumor burden from baseline tumor burden;
stabilization: two consecutive observations at least 4 weeks apart confirm that the total tumor burden drops by less than 50% or increases by less than 25% from the baseline tumor burden;
progress: an increase of at least 25% in total tumor burden over baseline tumor burden was detected at either of two consecutive observation points spaced at least 4 weeks apart.
According to one embodiment of the present application, the pre-bioinformatics database of the non-small cell lung cancer immunotherapy biomarker screening method comprises: GEO, TCGA, CGGA.
According to one embodiment of the present application, the biological processes, subcellular distribution, molecular functional clustering of binding genes at the time of determining molecular biomarkers suitable for clinical early prediction and prognosis and adverse reaction assessment, and literature analysis related to bioinformatics and literature-mined lung cancer immunotherapy are determined in Step32 of the non-small cell lung cancer immunotherapy biomarker screening method.
The application explores the biomarker for predicting the curative effect and evaluating the prognosis of the specificity of the immunotherapy of the non-small cell lung cancer from the front edge of the international field. Specific biomarkers associated with non-small cell lung cancer immunotherapy were screened by high throughput second generation sequencing. And further verifying specific biomarkers through large-scale clinical specimens is a precedent. And meanwhile, the gene and protein layers are combined, and the advanced technology and method are adopted to screen the biomarker, so that the subject has stronger scientificity and convincing ability. And the biological characteristic analysis is carried out deeply on the discovered non-small cell lung cancer immunotherapy related biomarker with potential value, and the later-period large sample multicenter verification work is carried out on the marker with potential positive value, so that a safe noninvasive early prediction and prognosis evaluation bio-marker with high specificity and sensitivity for the non-small cell lung cancer immunotherapy is established, and the related technical scheme is not yet disclosed at home and abroad.
The gene expression in blood and tissue specimens of lung cancer patients treated by PD1/PDL1 immune detection point inhibitors is detected by a new generation sequencing method, the lung cancer patients are divided into an immune treatment sensitive group and an immune treatment tolerant group according to the curative effects of the patients, the curative effects and adverse reactions of the two groups of patients are observed, and a plurality of genes which are most obvious in differential expression and participate in the regulation of immunity are screened by using a bioinformatics analysis tool and a literature mining method. The candidate genes are respectively detected at three levels of DNA, RNA and protein by using a molecular biology technology, and finally, biomarkers with the best sensibility and relevant curative effects of non-small cell lung cancer immunotherapy and adverse reaction evaluation are screened out. And the marker with potential positive value is subjected to later-stage large-sample multi-center clinical verification, and a safe noninvasive early prediction and prognosis evaluation bio-marker with high specificity and sensitivity for non-small cell lung cancer immunotherapy is established.
The foregoing examples are merely representative of several embodiments of the present invention, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit of the invention, which are within the scope of the invention. The scope of the invention should therefore be pointed out with reference to the appended claims.
Claims (10)
1. A biomarker for assessing prognosis and adverse effects of immunotherapy of non-small cell lung cancer, characterized in that:
the biomarker related to the prognosis of the immunotherapy and the adverse reaction of the non-small cell lung cancer is CD266 protein;
the biomarker related to the curative effect and prognosis of the non-small cell lung cancer immunotherapy is DNMT3A gene.
2. A method for screening a biomarker for immunotherapy of non-small cell lung cancer, comprising the steps of:
s1: screening biomarkers related to lung cancer immunotherapy curative effect, prognosis and adverse reaction evaluation with the best sensitivity;
s2: screening the gene differential expression genes obtained by preliminary screening at least twice through a bioinformatics analysis tool, and further selecting more suitable candidate genes for multi-group chemical sequencing so as to screen out final candidate markers;
s3: screening differentially expressed genes at three levels of DNA, RNA and protein of 40 non-small cell lung cancer tumor tissue samples by using a multi-group screening technology to find more than 10 common candidate genes;
s4: performing literature review, analyzing by using a bioinformatics tool, and finally selecting 3 biomarkers with the best sensitivity, which are relevant to lung cancer immunotherapy curative effect, prognosis and adverse reaction evaluation;
s5: later laboratory and clinical multi-center clinical validation was performed on the 3 biomarkers with potential positive value.
S6: establishing a non-small cell lung cancer immunotherapy high-specificity and sensitivity safe noninvasive early prediction and prognosis evaluation bio-marker;
screening out a non-small cell lung cancer immunotherapy prediction marker 1, and predicting the prognosis of the non-small cell lung cancer immunotherapy and a biomarker related to adverse reaction as CD266;
screening out a non-small cell lung cancer immunotherapy prediction marker 2, and predicting the curative effect of the non-small cell lung cancer immunotherapy and a biomarker related to prognosis as DNMT3A.
3. The method for screening biomarkers for non-small cell lung cancer immunotherapy according to claim 2, wherein the latest generation of genetic sequencing and bioinformatics analysis tools and literature mining methods and the screening of biomarkers are applied in step S1.
4. The method for screening a biomarker for immunotherapy of non-small cell lung cancer according to claim 3, wherein the literature-mining method is specifically as follows: selecting 6 non-small cell lung cancer cohorts treated with immune checkpoint inhibitors, yeon Kim et al, hwang et al, rizvi et al, miao et al, samstein et al, and Prat et al, respectively;
the Hwang et al, prat et al and Yeon Kim et al queues contain expression data and clinical prognosis data;
the Rizvi et al, miao et al and Samstein et al queues contain mutation data and clinical prognosis data;
for expression data, the high expression group and the low expression group are respectively carried out according to the median value of each gene;
for mutation data, the non-synonymous mutation status of each gene was grouped according to mutant and wild type according to the definition of non-synonymous mutation in the maftools R package;
for copy number variation data, non-small cell lung cancer patients are divided into amplified and non-amplified or deleted and non-deleted groups;
analyzing each gene by using a single factor cox regression model, wherein the high expression versus is low in expression; the mutant type versus wild type, amplified group versus non-amplified group or deleted group versus non-deleted group, and the effect on the curative effect of non-small cell lung cancer patients;
for the immunotherapy adverse reaction data, acquiring adverse event reports of all non-small cell patients receiving anti-PD 1/PD-L1 treatment from 2015 to 2021 from an FAERS database (FDA adverse event reporting system database), classifying the adverse events according to the existing immune related adverse reaction guidelines, and calculating irAE ROR reporting odds ratio, ROR, and the ratio of the irAEs caused by the anti-PD 1/PD-L1 reagent to the irAEs caused by other medicines reported in the database as indexes for measuring the amount of the irAEs caused by the immunotherapy;
through the gene enrichment analysis of the genes, single factors highly related to ROR are selected, then factors are added to construct a bivariate regression model, and Spearman correlation analysis is adopted to find out the correlation between the adverse immune response and candidate genes.
5. The method for screening a biomarker for immunotherapy of non-small cell lung cancer according to claim 3, wherein: further included is the use of Kaplan-Meier analysis to further visualize the link between the therapeutic efficacy of immunotherapy, prognosis and adverse reaction of each of 2300 multiple genes in patients with non-small cell lung cancer.
6. The method for screening non-small cell lung cancer immunotherapeutic biomarkers according to claim 4, wherein: the step of analyzing each gene using a one-factor cox regression model employs high expression versus low expression; mutant versus wild type, amplified versus non-amplified versus deleted versus non-deleted; correlation of the adverse immunotherapy response to the candidate gene was found using a Spearman correlation analysis.
7. The method of claim 2, wherein the step S2 is performed by selecting 172 more suitable candidate genes for multiplex sequencing after at least two screenings.
8. The method for screening non-small cell lung cancer immunotherapeutic biomarkers according to claim 2, wherein the step S3 is performed using whole exon sequencing, normal transcriptome sequencing, protein TMT sequencing.
9. The method according to claim 2, wherein the bioinformatics tools in step S4 include GSEA function enrichment and network analysis, random forest survival analysis, cox risk ratio regression model analysis, graphical Lasso Estimation for analysis of multiple genes, principal component analysis, and survival tree analysis.
10. The method for screening biomarkers for non-small cell lung cancer immunotherapy according to claim 2, wherein in step S5, the verification is performed by: collecting peripheral blood of 30 cases of non-small cell lung cancer patients, and verifying 2 important functional biomarkers by adopting RT-PCR, western blot and immunohistochemistry;
the 30 cases of non-small cell lung cancer patients all meet the following requirements: the PD-1/PD-L1 inhibitor single drug treatment is adopted, and the dosage and the treatment course of the PD-1/PD-L1 inhibitor are not limited.
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