| Objective:Studies have shown that ferroptosis and immunotherapy are new avenues for the treatment of various malignancies,including kidney cancer.This study was based on a bioinformatics approach to find biomarkers of ferroptosis and immune combination,while constructing a prognostic model of ferroptosis and immune-related genes(FIGs)in clear cell renal cell carcinoma(cc RCC)and explored to analyze the correlation between FIGs and tumor microenvironment in cc RCC.Methods:In this study,the transcriptome data and corresponding clinical information of cc RCC patients were downloaded from the cancer genome atlas(TCGA)database,including 539 kidney cancer tissue specimens and 72 paracancerous tissue samples,differentially expressed genes(DEGs)were screened by DESeq2 package analysis of R 4.1.2 software;ferroptosis-related genes(FRGs)and immune-related genes(IRGs)were obtained from Ferr Db database and Imm Port database,respectively,and 24 differentially expressed ferroptosis and immune related genes(DE-FIGs)were screened by taking the intersection and visualizing them by Venn diagram.We randomly divided the cc RCC samples(n=530)with complete clinical information into training set(n=318)and test set(n=212)in the ratio of 6:4,and firstly screened 8148prognosis-related genes(PRGs)in the training set by univariate Cox analysis using the"survival"package in R software.To prevent overfitting,LASSO regression analysis and stepwise multi-factor Cox analysis were performed on 12 OC-FIGs,and finally 5prognostically significantly correlated OC-FIGs were screened for the construction of the prognostic stratification model.The test set was used for the internal validation of the model,and the predictive performance and applicability of the model were investigated in the ICGC dataset of cc RCC patients in the International Cancer Genome Consortium(ICGC)database.Then,the results of the independent prognostic analysis were integrated with the"RMS"package of R software to construct the nomogram model,and the ROC curves were plotted with the"time ROC"package to evaluate the nomogram model.Then,based on model risk score,cc RCC patients were stratified into high and low risk groups,and the tumor mutation burden(TMB),tumor microenvironment(TME),immune infiltration,and sensitivity to immune checkpoint inhibitors(ICIs)/conventional antitumor drug therapy were analyzed in both groups.Finally,24 DE-FIGs were analyzed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)and other functional enrichment analyses to explore potential biological pathways.Results:We obtained 5761 DEGs from differential expression analysis of transcriptomic data from kidney cancer tissue samples and cancerous tissue samples in the TCGA database,of which 1988 were up-regulated and 3773 were down-regulated in expression;388 FRGs and 1793 IRGs were obtained from Ferr Db database and Imm Port database,respectively.The five best FIGs(HAMP,CXCL2,CDH1,NOX4,NR5A2)were finally selected for the construction of a prognostic model based on the comprehensive index of ferroptosis and immune status(CIFI)through taking the intersection of the above gene sets and LASSO regression,and the cc RCC patients into two groups of high and low risk.In the training set,the overall survival(OS)of patients in the high-risk group was significantly lower than that of patients in the low-risk group,and receiver operating characteristic curve(ROC)analysis showed that the area under the ROC curve for 1-year,3-year,and 5-year OS Area Under Curve(AUC)was 0.779,0.688,and 0.728 for 1-year,3-year,and 5-year OS,respectively.Also,validation on the test set,the entire dataset and the ICGC dataset yielded the same results,i.e.,patients in the high-risk group had significantly worse OS,indicating that the predictive performance and applicability of the model were satisfactory.In addition,TMB analysis showed significantly higher TMB scores for patients in the high-risk group.Tumor microenvironment analysis showed significantly higher ESTIMATE scores and immune scores and significantly lower tumor purity in patients in the high-risk group(P<0.05).In terms of immune infiltration,the infiltration of memory B cells,plasma cells,activated CD4 memory T cells,follicular helper T cells,regulatory T cells,and M0-type macrophages were significantly higher in patients in the high-risk group,while we found that the higher the level of infiltration of M0-type macrophages,regulatory T cells,plasma cells,activated CD4 memory T cells,CD8~+T cells,and follicular helper T cells,the higher the cc RCC patients’shorter the survival period(P<0.05).The expression of immune checkpoints CD27,CD28,CCD40,CTLA-4,LAG3,PD-1(PDCD1),and TIGIT was significantly higher in patients in the high-risk group than in patients in the low-risk group,and drugs targeting the above immune checkpoints were more effective in patients in the high-risk group.In terms of drug sensitivity,the IC50 of sunitinib and everolimus was significantly lower in patients in the high-risk group,and patients in the high-risk group had better sensitivity to sunitinib and everolimus,while the opposite was true for pegaptanib.Finally,functional enrichment analysis suggested that the development and prognosis of cc RCC are related to various biological functions and pathways such as glandular development,cellular iron ion homeostasis,cytokine activity,and HIF-1 signaling pathway.Conclusions:This study established a new stratification model for cc RCC based on 5 prognosis-related FIGs(HAMP,CXCL2,CDH1,NOX4,NR5A2),which not only accurately predicted the prognosis of cc RCC patients but also showed significant differences in TMB,immune infiltration and drug sensitivity,and these five FIGs may be biomarkers and therapeutic targets for cc RCC patients.These five FIGs may be biomarkers and therapeutic targets for cc RCC patients.In conclusion,our study provides a new basis for individualized immunotherapy and targeted therapy for clinical patients. |
PDF Full Text Request