CN114686591B - Lung squamous cell carcinoma immunotherapy curative effect prediction model based on gene expression condition, construction method and application thereof - Google Patents
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Abstract
The invention discloses a lung squamous cancer immunotherapy efficacy prediction model based on gene expression conditions, and a construction method and application thereof, and belongs to the technical field of biology. The construction method of the model comprises the following steps: the method comprises the steps of obtaining immune treatment queue data of lung squamous carcinoma patients containing clinical information and gene expression profile matrixes, dividing the immune treatment queue data into a training set and a verification set, calculating respective 28 immune cell enrichment scores of lung squamous carcinoma samples in the training set through a ssGSEA method, carrying out normalization treatment and NMF typing on an upper scoring matrix, carrying out survival analysis, screening genes with differential expression and genes affecting OS and DFS, merging intersections, constructing an IPTS model through the screened genes, and obtaining the immune treatment queue data, wherein the immune treatment queue data is obtained by predicting the immune treatment curative effect of the lung squamous carcinoma patients, and can improve the accuracy of screening lung squamous carcinoma patients benefiting from immune treatment such as immune checkpoint inhibitors and the like, and provide important reference value for survival prognosis of the patients.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a lung squamous cancer immunotherapy effect prediction model based on gene expression conditions, and a construction method and application thereof.
Background
At present, despite the great progress in cancer diagnosis and treatment, progress is slow in improving survival rate of lung cancer patients, and the average 5-year survival rate of lung cancer patients in most countries is only 10-20%. In recent years, immunotherapy has achieved favorable results in clinical practice, and recent studies suggest that the 5-year survival rate of patients with advanced non-small cell lung cancer who first use PD-1 antibodies reaches 23.2%, and the 5-year survival rate of patients who second use anti-PD-1 antibodies reaches 16% as compared with the traditional treatment, which is improved by two times. However, only about 20% of patients in the non-small cell lung cancer population benefit from immune checkpoint inhibitor therapy, and how to select the patients most likely to benefit from immune therapy is the major challenge currently facing this field. Thus, the high mortality rate of lung cancer is attributable in part to the lack of a good personalized regimen, which makes lung cancer treatment undesirable, especially for squamous cell lung cancer, which has large tumor heterogeneity and a lack of effective targets. Therefore, it is highly desirable to construct an immune therapy efficacy prediction model for lung squamous carcinoma in order to improve the accuracy of screening lung squamous carcinoma patients who benefit from immune therapy such as immune checkpoint inhibitors.
Disclosure of Invention
Aiming at the defects in the prior art, the main purpose of the invention is to provide a method for constructing a lung squamous cancer immunotherapy curative effect prediction model based on gene expression.
The invention also aims to provide a lung squamous cancer immunotherapy effect prediction model based on the gene expression situation, which is obtained by the construction method, and solves the problem that the prediction model which is not established by national data verification and depends on R language and public database data in the prior art is poor in prediction accuracy.
The invention also aims to provide application of the lung squamous cancer immunotherapy efficacy prediction model based on the gene expression condition in obtaining the lung squamous cancer immunotherapy efficacy prediction intermediate result.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides a gene marker for detecting the curative effect of lung squamous cancer immunotherapy, comprising one or more genes selected from the following: AKAP2, NANOG, TMEM236, NTSR1, LRRC38, GCGR, MARCO, PF, RP1, ALOX5-24, FCGR2A, KCNQ, NLRP12, SCARF1, SIGLEC12, TGM2 and VSTM1.
Preferably, the gene markers for detecting the efficacy of lung squamous cancer immunotherapy consist of AKAP2, NANOG, TMEM236, NTSR1, LRRC38, GCGR, MARCO, PF, RP1, ALOX5-24, FCGR2A, KCNQ3, NLRP12, SCARF1, SIGLEC12, TGM2 and VSTM1.
The invention also provides application of the gene marker for detecting the curative effect of lung squamous carcinoma immunotherapy in preparing a detection reagent for the curative effect of lung squamous carcinoma immunotherapy, wherein the gene marker for detecting the curative effect of lung squamous carcinoma immunotherapy consists of AKAP2, NANOG, TMEM236, NTSR1, LRRC38, GCGR, MARCO, PF4, RP1, ALOX5-24, FCGR2A, KCNQ3, NLRP12, SCARF1, SIGLEC12, TGM2 and VSTM1.
The invention also provides a kit for detecting the curative effect of lung squamous cancer immunotherapy, which comprises a reagent for determining the expression condition of the gene marker for detecting the curative effect of lung squamous cancer immunotherapy and a using instruction.
The invention also provides a construction method of the lung squamous cancer immunotherapy curative effect prediction model based on the gene expression condition, which comprises the following steps:
step 1: obtaining immune treatment queue data of a lung squamous carcinoma patient from a database, dividing the data into a training set and a verification set according to samples, wherein the data comprises clinical information and gene expression profile matrix of the lung squamous carcinoma patient;
step 2: based on the known 28 immune cell gene markers, calculating the respective 28 immune cell enrichment scores of the lung squamous carcinoma samples in the training set by a single sample gene set enrichment analysis (ssGSEA) method, and evaluating the infiltration degree of the lung squamous carcinoma samples;
step 3: normalizing the immune cell enrichment scoring matrix, and then carrying out non-Negative Matrix Factorization (NMF) typing on lung squamous carcinoma samples;
step 4: carrying out survival analysis and differential expression gene screening on the NMF typing sample, and simultaneously screening genes affecting the total survival time (OS) and the disease-free survival time (DFS) of lung squamous carcinoma patients;
step 5: firstly, combining the screened differential expression genes, genes affecting the OS of a lung squamous carcinoma patient, genes affecting the DFS of the lung squamous carcinoma patient and the gene expression spectrum matrix in the verification set to obtain intersection sets, and screening genes for constructing a lung squamous carcinoma immunotherapy efficacy prediction model, wherein the genes comprise AKAP2, NANOG, TMEM236, NTSR1, LRRC38, GCGR, MARCO, PF4, RP1, ALOX5-24, FCGR2A, KCNQ, NLRP12, SCARF1, SIGLEC12, TGM2 and VSTM1; the construction of IPTS model based on the 17 genes and their regression coefficients is as follows:
ipts= 0.4869250211- (0.1428834537 ×akap 2) - (0.12060842 ×nanog) - (0.0951070744 ×tmem 236) - (0.0436119966 ×ntsr 1) - (0.0258542814 ×lrrc 38) - (0.0170225681 ×gcgr) - (0.0011330363 ×marco) - (0.0008511336 ×pf4) - (0.0004418332 ×rp1) + (0.0023249088 ×alox 5-24) + (0.0021763779 ×fcgr 2A) + (0.0006362408 ×kcnq 3) + (0.0247048306 ×nlrp 12) + (0.0314720069 ×scarf 1) + (0.0013954206 ×siglec 12) + (0.0004957628 ×tgm2) + (0.0617891897 ×vstm 1), which are lung squamous cell carcinoma immunotherapy efficacy prediction models based on the above 17 gene expression cases.
Preferably, in step 1, the verification set includes two verification sets of GSE126044 and GSE 135222.
Preferably, in step 3, the 28 immune cells are divided into anti-tumor immunocompetent cells, tumor-promoting immunosuppressive cells and other types of cells, wherein the anti-tumor immunocompetent cells comprise activated CD4 + T cell, activated CD8 + T cells, activated dendritic cells, CD 56-expressing natural killer cells, and central memory CD4 + T cell, central memory CD8 + T cell, effector memory CD4 + T cell, effector memory CD8 + T cells, natural killer T cells, type 1 helper T cells, and type 17 helper T cells; the tumor-promoting immunosuppressive cells comprise CD56 low-expression natural killer cells, immature dendritic cells, macrophages, marrow-derived suppressor cells, neutrophils, plasmacytoid dendritic cells, regulatory T cells and type 2 helper T cells; such other cell types include activated B cells, eosinophils, γδ T cells, immature B cells, mast cells, memory B cells, monocytes and follicular helper T cells.
Preferably, in step 3, the method further comprises the steps of performing correlation analysis and difference analysis on the anti-tumor immune enrichment scores and the pro-tumor immune enrichment scores between different immune types, and performing difference detection on 28 immune cell enrichment scores respectively, so as to evaluate the reliability of the immune types.
Preferably, in step 4, the method further comprises the step of rejecting samples with survival time or occurrence of recurrent metastatic event less than 30 days.
Preferably, the method further comprises the step of validating the IPTS model.
The invention also provides application of the lung squamous cancer immunotherapy effect prediction model based on the gene expression condition in predicting the immune infiltration degree and the immunotherapy effect of a lung squamous cancer patient.
Compared with the prior art, the invention has the beneficial effects that: the gene marker combination which can be used for detecting the curative effect of the lung squamous carcinoma immunotherapy is selected through screening, a lung squamous carcinoma immunotherapy curative effect prediction model based on the gene expression condition is constructed based on the gene marker combination, and the gene marker combination is used for predicting the curative effect of the lung squamous carcinoma patient immunotherapy, so that the accuracy of screening lung squamous carcinoma patients benefiting from immunotherapy such as immune checkpoint inhibitors can be improved, and an important reference value is provided for survival prognosis of the patients.
Drawings
FIG. 1 is a flow chart of a construction method of a lung squamous cancer immunotherapy effect prediction model based on gene expression.
Fig. 2 is a graph of ROC for predicting NMF typing using an IPTS model tested in the examples.
FIG. 3 is a graph showing the results of the IPTS model constructed in the examples for predicting the efficacy of immunotherapy of lung squamous carcinoma; wherein: a: the difference box plot between immune and non-immune responsive patient IPTS in the validation set GSE126044 shows that the overall levels of IPTS in responsive patients are higher than in non-responsive patients; b: ROC curves plotted according to patient immune response conditions in IPTS and validation set GSE 126044; c: the difference box diagram between the immunotherapy benefit and the patient IPTS which does not benefit in the GSE135222 is verified, and the overall level of the IPTS of the patient who does not benefit in the immunotherapy is higher than that of the patient who does not benefit; d: ROC curves plotted according to patient immune response conditions in IPTS and validation set GSE 135222; e: survival curves drawn from IPTS scoring and validating the progression free survival time and survival status of GSE 135222.
FIG. 4 is a high and low immunoinfiltrate molecular typing gene marker in the examples; wherein, the regression coefficient is less than 9 genes, which can be used as the marker genes of C1 type (low immune cell infiltration type); the total number of genes with regression coefficient >0 is 8, and the gene can be used as a marker gene of C2 type (hyperimmune cell infiltration type).
FIG. 5 is a graph showing the effect of the IPTS model on predicting OS in a lung squamous carcinoma patient in the examples.
Figure 6 is an example of the effect of IPTS model in predicting DFS in a lung squamous carcinoma patient.
Fig. 7 shows a specific clinical application manner of the IPTS model in the embodiment, after IPTS is obtained by calculation, it can be predicted whether a patient with lung squamous carcinoma is in type C1 (low immune cell infiltration type) or type C2 (high immune cell infiltration type) according to the value, for example, when ipts= 0.6369, the probability of the patient being in type C1 and type C2 is 50%, the probability of the patient being in type C2 is higher than the value, and the part of lung squamous carcinoma patient should be actively treated by immunotherapy.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
Example 1
In this embodiment, a model for predicting the therapeutic effect of lung squamous cancer immunotherapy (IPTS model) based on gene expression is constructed according to the method shown in fig. 1, and the steps are as follows:
(1) Data collection and processing
Clinical information and gene expression profile matrix of lung squamous carcinoma patients are downloaded from The Cancer Genome Atlas (TCGA; https:// cancetrgenome.nih.gov /) database at 10 and 15 days of 2021, and 501 samples with complete clinical information and expression profile matrix are screened out and used as training set for constructing immune curative effect prediction model. Then, gff file of v37 version (release time: 2021, 10 th month, 14 th day) was downloaded from GENCODE (https:// www.gencodegenes.org/human /), and information of GeneSymbol and ENSG_ID was extracted therefrom using R language version 4.1.2 and matched, so that ENSG_ID was converted into GeneSymbol, and when there were a plurality of matches, the median was taken. The count data was then converted to TPM (Transcripts Per Kilobase) data based on the length of the gene for subsequent analysis.
In addition, clinical information and gene expression profile matrices of two non-small cell lung cancer datasets with immunotherapeutic efficacy, GSE126044 and GSE135222, were downloaded from Gene Expression Omnibus (GEO) database (https:// www.ncbi.nlm.nih.gov/GEO /) at month 11 of 2022 as a validation set of the model. Wherein GSE126044 was sequenced using Illumina HiSeq 2500 (GPL 16791) platform for a total of 16 non-small cell lung cancer samples; GSE135222 was also sequenced using the GPL16791 platform, totaling 27 samples, which published prognostic information for patient PFS (Progression free survival).
(2) Single sample Gene set enrichment analysis (ssGSEA) to assess immune cell infiltration
According to the gene markers of 28 immune cells published by researchers such as Jia Q, the enrichment fraction of 28 immune cells in each of 501 lung squamous carcinoma samples in the TCGA database is calculated by the ssGSEA method by using GSVA package (version number: 1.42.0) in R language, and the 28 immune cells are divided into anti-tumor immunocompetent cells, tumor-promoting immunosuppressive cells and other types of cells, wherein the anti-tumor immunocompetent cells comprise activated CD4 + T cell, activated CD8 + T cells, activated dendritic cells, CD 56-expressing natural killer cells, and central memory CD4 + T cell, central memory CD8 + T cell, effector memory CD4 + T cell, effector memory CD8 + T cells, natural killer T cells, type 1 helper T cells, and type 17 helper T cells; tumor-promoting immunosuppressive cells include CD 56-underexpressing natural killer cells, immature dendritic cells, macrophages, bone marrow derived suppressor cells, neutrophils, plasmacytoid dendritic cells, regulatory T cells, and type 2 helper T cells; other cell types include activated B cells, eosinophils, γδ T cells, immature B cells, mast cells, memory B cells, monocytes and follicular helper T cells.
(3) Non-negative matrix factorization for immunophenotyping samples
After normalizing the immune cell enrichment scoring matrix, the NMF package (version number: 0.23.0) in R language was used for typing, wherein rank was set to 2:10, and the method was brunet, nrun=100. After the typing result of each sample was obtained, the feasibility of typing was verified by performing dimension reduction analysis using Rtsne function of R software package Rtsne (version number: 0.15) and prcomp function of stats (version number: 3.6.0), respectively. In addition, correlation analysis and difference analysis are carried out on the anti-tumor immune enrichment scores and the pro-tumor immune enrichment scores among different types, and difference detection is carried out on 28 immune cell enrichment scores respectively, so that the reliability of the types is further demonstrated.
(4) Survival analysis between NMF genotyping and Gene screening affecting Total survival (OS), disease Free Survival (DFS)
Patients were grouped according to typing and then survival analysis was performed using the R software package survivinal (version number: 3.3-1), and the best cut-off was calculated using the survivin_cut point function. Then, plotting was performed using a surviviner package (version number: 0.4.9). In addition, samples with survival time or occurrence of recurrence and metastasis time less than 30 days are removed, survival analysis is carried out on all genes in the gene expression profile, and genes affecting prognosis survival of lung squamous cell carcinoma in a TCGA database are screened, so that genes affecting OS and DFS of a patient are respectively derived.
(5) Constructing an immune efficacy prediction model
Because the sequencing platform and sequencing depth are different, many genes detected in the training set are not detected in the verification set, the genes affecting the OS and DFS of the patient and the genes detected in the two verification sets GSE126044 and GSE135222 are collected, and after intersection of the 5 gene sets, the screened genes are used for constructing a subsequent immune curative effect prediction model, including AKAP2, NANOG, TMEM236, NTSR1, LRRC38, GCGR, MARCO, PF4, RP1, ALOX5-24, FCGR2A, KCNQ3, NLRP12, SCARF1, SIGLEC12, TGM2 and VSTM1, and the result is that a Wen diagram is drawn by using an on-line tool of biological & Evolutionary Genomics (http:// bioinformation.
Extracting the gene expression profile matrix and NMF typing information which are screened and used for modeling, setting nfold=10 by using a glmnet package (version number: 4.1-3), constructing a LASSO regression model by lambda=lambda.min, and constructing an IPTS model according to the regression coefficient of each gene to obtain the regression coefficient, wherein the equation is as follows:
ipts= 0.4869250211- (0.1428834537 ×akap 2) - (0.12060842 ×nanog) - (0.0951070744 ×tmem 236) - (0.0436119966 ×ntsr 1) - (0.0258542814 ×lrrc 38) - (0.0170225681 ×gcgr) - (0.0011330363 ×marco) - (0.0008511336 ×pf4) - (0.0004418332 ×rp1) + (0.0023249088 ×alox 5-24) + (0.0021763779 ×fcgr 2A) + (0.0006362408 ×kcnq 3) + (0.0247048306 ×nlrp 12) + (0.0314720069 ×scarf 1) + (0.0013954206 ×siglec 12) + (0.0004957628 ×tgm2) + (0.0617891897 ×vstm 1). Then, alignment and calibration curves were drawn through an rms package (version number: 6.2-0) to visualize the regression analysis results. In addition, sang Jitu was drawn through a networkD3 package (version number: 0.4) to visualize the typing results and their marker genes.
(6) Reliability and predictive value verification of immune efficacy prediction model
In order to verify the distinction degree of the prediction model on NMF typing and whether the sample typing result can be basically replaced, firstly, respectively calculating IPTS values of each sample in a training set and each verification set according to the gene expression condition, then integrating the IPTS and NMF typing results of the training set, and constructing an ROC curve through pROC package (version number: 1.18.0) to judge the NMF typing capacity of the prediction model. And then dividing the training set into a high scoring group and a low scoring group according to the cut-off value of the ROC curve, respectively detecting the difference between IPTS in different NMF typing, the difference and the correlation between the tumor immunity enrichment promoting score and the tumor immunity enrichment resisting score between the high scoring group and the low scoring group, and the difference condition between the 28 immune cell enrichment scores so as to determine the coincidence degree of the molecular typing result and the NMF typing result, namely whether the substitution of the molecular typing result to the NMF typing result is reasonable or not. In addition, since genes in the model have influence on the training set OS and DFS, survival analysis and survival curve drawing are performed in the same way to evaluate the prognostic prediction value of the model.
Clinical information from TCGA database for squamous cell lung carcinoma patients suggests that almost all patients were not treated with immunotherapy. In order to judge whether the constructed immune parting model has an immune curative effect prediction value, verification is carried out through two verification sets with non-small cell lung cancer immune curative effect evaluation. First, it was compared whether there was a significant difference in IPTS scores between different immune-responsive lung cancer patient populations, and because of the small sample size and uneven variance in the GSE126044 dataset, IPTS was in a non-normal distribution, the differences between the datasets were statistically significant using the Mann-Whitney rank sum test, P < 0.05. And then, constructing an ROC curve according to the IPTS score and the immune response result, and judging the immune curative effect prediction value of the model. Because the GSE135222 data set has patient PFS information, survival analysis is further carried out, and the immune curative effect prediction value and the prognosis prediction capability of the evaluation model are verified.
As shown in FIG. 2, the ROC curve is used to test the ability of the IPTS model score to predict NFM typing, and the AUC value is 0.82, which indicates that the test model can better distinguish the two types of high immune cell infiltration type and low immune cell infiltration type. Compared with the method that single sample enrichment analysis is needed to be carried out based on high-throughput sequencing so as to carry out NMF typing, 17 genes are used for predicting the immune infiltration degree and the immune treatment effect, IPTS molecular typing is carried out, so that the cost is saved, and the method has potential value of subsequent clinical popularization and use;
as shown in fig. 3, in the non-small cell lung cancer anti-PD-1 antibody immunotherapy cohort (GSE 126044), patients with immunotherapy response had significantly higher IPTS scores than patients with no immunotherapy response (p=0.0032) (fig. 3A), ROC curves showed AUC areas of 0.95 (95% ci=1.00-0.84), suggesting that IPTS scores had good efficacy prediction in this dataset (fig. 3B); in the non-small cell lung cancer anti-PD-1/PD-L1 antibody immunotherapy cohort (GSE 135222), patients who benefited by immunotherapy had higher IPTS scores than patients who did not benefited by immunotherapy (p= 0.0451) (fig. 3C), ROC curves showed AUC areas of 0.77 (95% ci=0.58-0.96) (fig. 3D), suggesting that IPTS scores have better predictive value for the efficacy of immunotherapy in this cohort. In addition, the data set discloses prognosis information of patient PFS, and by taking the optimal cut-off value, PFS after immunotherapy of patients with high IPTS score is better than PFS after immunotherapy of patients with low IPTS score (hr=0.72, 95% ci=0.5-1.04, p=0.0059) when ipts= -2.13 is taken as a boundary value (fig. 3E), which also shows good therapeutic effect prediction value of immunotherapy and certain survival prognosis prediction value.
As shown in FIG. 4, the IPTS model can show that 9 genes with regression coefficients <0 can be used as marker genes of C1 type (low immune cell infiltration type); the total number of genes with regression coefficient of >0 is 8, and the gene can be used as a marker gene of C2 type (hyperimmune cell infiltration type), so that molecular typing is realized to a certain extent.
In addition, the model also has a certain function of predicting prognosis of patients, as shown in fig. 5 and 6: when the optimal cut-off value is taken instead of the ROC curve cut-off value, the total survival OS (hr=1.06, 95% ci=0.98-1.16, p=0.03, fig. 5) and the disease-free survival DFS (hr=1.12, 95% ci=1.03-1.20, p=0.0027, fig. 6) of the high scoring group are all significantly lower than the low scoring group, and it is seen that the predictive model has a function of predicting the prognosis of the patient under certain conditions (i.e. predicting OS, IPTS optimal cut-off value is 0.75526; IPTS optimal cut-off value is 0.96915) when predicting DFS.
As shown in fig. 7, constructing an alignment chart to visualize the predictive effect of the model on C2 typing (hyperimmune cell infiltration), it can be seen that IPTS values were calculated according to the formula of the model, and when ipts= 0.6369, the probability of the patient being in both C1 and C2 typing was 50%, and beyond this value the probability of the patient being in C2 typing was higher, and this part of lung squamous carcinoma patients should be actively treated with immunotherapy.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (7)
1. Use of a gene marker for detecting the efficacy of lung squamous carcinoma immunotherapy in the preparation of a detection reagent for the efficacy of lung squamous carcinoma immunotherapy, said gene marker for detecting the efficacy of lung squamous carcinoma immunotherapy consisting of AKAP2, NANOG, TMEM236, NTSR1, LRRC38, GCGR, MARCO, PF4, RP1, ALOX5-24, FCGR2A, KCNQ3, NLRP12, SCARF1, SIGLEC12, TGM2 and VSTM1;
the construction of the IPTS model based on the gene markers and their regression coefficients is as follows:
ipts= 0.4869250211- (0.1428834537 ×akap 2) - (0.12060842 ×nanog) - (0.0951070744 ×tmem 236) - (0.0436119966 ×ntsr 1) - (0.0258542814 ×lrrc 38) - (0.0170225681 ×gcgr) - (0.0011330363 ×marco) - (0.0008511336 ×pf4) - (0.0004418332 ×rp1) + (0.0023249088 ×alox 5-24) + (0.0021763779 ×fcgr 2A) + (0.0006362408 ×kcnq 3) + (0.0247048306 ×nlrp 12) + (0.0314720069 ×scarf 1) + (0.0013954206 ×siglec 12) + (0.0004957628 ×tgm2) + (0.0617891897 ×vstm 1), i.e., lung squamous cell carcinoma immunotherapy efficacy prediction model based on gene expression.
2. A kit for detecting the efficacy of an immunotherapy for lung squamous carcinoma, comprising reagents for determining the expression of the gene markers for detecting the efficacy of an immunotherapy for lung squamous carcinoma according to claim 1 and instructions for use.
3. A method for constructing a model for predicting the therapeutic effect of lung squamous cancer immunotherapy based on gene expression as claimed in claim 1, comprising the steps of:
step 1: obtaining immune treatment queue data of a lung squamous carcinoma patient from a database, dividing the data into a training set and a verification set according to samples, wherein the data comprises clinical information and gene expression profile matrix of the lung squamous carcinoma patient;
step 2: based on the known 28 immune cell gene markers, calculating the respective 28 immune cell enrichment scores of the lung squamous carcinoma samples in the training set by a ssGSEA method, and evaluating the infiltration degree of the lung squamous carcinoma samples;
the 28 immune cells are divided into anti-tumor immunocompetent cells, tumor-promoting immunosuppressive cells and other cell types, wherein the anti-tumor immunocompetent cells comprise activated CD4 + T cell, activated CD8 + T cells, activated dendritic cellsCD56 high expressing natural killer cells, central memory CD4 + T cell, central memory CD8 + T cell, effector memory CD4 + T cell, effector memory CD8 + T cells, natural killer T cells, type 1 helper T cells, and type 17 helper T cells; the tumor-promoting immunosuppressive cells comprise CD56 low-expression natural killer cells, immature dendritic cells, macrophages, marrow-derived suppressor cells, neutrophils, plasmacytoid dendritic cells, regulatory T cells and type 2 helper T cells; such other cell types include activated B cells, eosinophils, γδ T cells, immature B cells, mast cells, memory B cells, monocytes and follicular helper T cells;
step 3: after normalizing the immune cell enrichment scoring matrix, carrying out NMF typing on lung squamous carcinoma samples;
step 4: carrying out survival analysis and differential expression gene screening on the NMF typing sample, and simultaneously screening genes affecting OS and DFS of lung squamous carcinoma patients;
step 5: firstly, combining the screened differential expression genes, genes affecting the OS of a lung squamous carcinoma patient, genes affecting the DFS of the lung squamous carcinoma patient and the gene expression spectrum matrix in the verification set to obtain intersection sets, and screening genes constructing a lung squamous carcinoma immune therapy efficacy prediction model to be AKAP2, NANOG, TMEM236, NTSR1, LRRC38, GCGR, MARCO, PF4, RP1, ALOX5-24, FCGR2A, KCNQ3, NLRP12, SCARF1, SIGLEC12, TGM2 and VSTM1; the construction of IPTS model based on the 17 genes and their regression coefficients is as follows:
ipts= 0.4869250211- (0.1428834537 ×akap 2) - (0.12060842 ×nanog) - (0.0951070744 ×tmem 236) - (0.0436119966 ×ntsr 1) - (0.0258542814 ×lrrc 38) - (0.0170225681 ×gcgr) - (0.0011330363 ×marco) - (0.0008511336 ×pf4) - (0.0004418332 ×rp1) + (0.0023249088 ×alox 5-24) + (0.0021763779 ×fcgr 2A) + (0.0006362408 ×kcnq 3) + (0.0247048306 ×nlrp 12) + (0.0314720069 ×scarf 1) + (0.0013954206 ×siglec 12) + (0.0004957628 ×tgm2) + (0.0617891897 ×vstm 1), i.e., lung squamous cell carcinoma immunotherapy efficacy prediction model based on gene expression.
4. The method for constructing a model for predicting the therapeutic effect of lung squamous cancer immunotherapy based on gene expression according to claim 3, wherein in the step 1, the validation set comprises two validation sets of GSE126044 and GSE 135222.
5. The method for constructing a model for predicting the efficacy of lung squamous cancer immunotherapy based on gene expression as claimed in claim 3, wherein in step 3, the method further comprises the steps of performing correlation analysis and difference analysis on the antitumor immune enrichment score and the pro-tumor immune enrichment score between the different immune types, and performing difference examination on the 28 immune cell enrichment scores respectively.
6. The method for constructing a model for predicting the efficacy of an immunotherapy for lung squamous cancer based on gene expression according to claim 3, wherein the step 4 further comprises the step of removing samples having a survival time or a time for occurrence of recurrent metastasis of less than 30 days.
7. The method for constructing a model for predicting the efficacy of a lung squamous cell carcinoma immunotherapy based on gene expression as set forth in claim 3, further comprising the step of verifying the model for predicting the efficacy of a lung squamous cell carcinoma immunotherapy based on gene expression.
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