Construction Of A Prognosis-related Protein Risk Score Model For Bladder Cancer

Objective: Based on The Cancer Genome Atlas(TCGA)and the G ProteinCoupled Receptors(GPCR)gene set,we screened bladder cancer(BLCA)prognosisrelated proteins by bioinformatic methods,established a risk score model for bladder cancer prognosis-related proteins,and investigated their relationship with immune infiltration,tumor microenvironment and tumor immunotherapy.Methods: Clinical data and gene expression data of BLCA patients were downloaded from TCGA,and GPCR gene set data were obtained from MSig DB website.Single-factor COX regression analysis and LASSO regression analysis were used to screen out the differentially expressed GPCR genes associated with prognosis,and the obtained GPCR genes were analyzed by correlation analysis and single-factor COX regression analysis to obtain proteins significantly associated with prognosis,and all BLCA patient samples were randomly divided 1:1 into a training group(Train group)and a test group(Test group)for machine LASSO regression analysis and multi-factor COX regression analysis were used for model feature screening and prognostic risk model building,risk scores were calculated for each sample,patients were divided into low risk and high risk groups according to median risk scores,risk scores were compared between groups,and Kaplan-Meier survival curves,risk curves,subject work characteristic(ROC)curves were combined and calibration curves to validate the accuracy of the model.Then,a clinical multifactorial COX regression model was developed to obtain a Nomogram for individualized survival prognosis of BLCA patients by combining clinical data.Based on this,the relationship between related genes and immune infiltration and the difference in expression of target genes between tumor tissues and adjacent normal tissues were obtained by TIMER 2.0 database,CIBERSORT calculated the percentage of immune cells between groups,GSEA analyzed the enrichment of genes in ion channels,and correlated the differential expression of immune checkpoint-related genes in high-and low-risk groups.The expression levels of target proteins were compared between tumor tissues and adjacent normal tissues using the Human Protein Atlas database.Results: The clinical data and gene expression data of 430 BLCA patients were downloaded from TGCA database,869 GPCR gene set data were obtained from MSig DB website,118 differential genes were obtained by TCGA bladder cancer data combined with GPCR gene set,and 15 differentially expressed GPCR genes related to BLCA prognosis were obtained by analysis,and the 15 GPCR genes were The corresponding proteins were obtained by correlation analysis,among which 10 proteins were significantly associated with BLCA prognosis,namely:GATA3,P38 MAPK,SRC,STAT3-PY705,ARID1 A,SF2,TAZ,ANNEXIN1,SMAC,CABL,and GATA3 and ANNEXIN1 were the best-matched proteins,which were used to establish Regression analysis confirmed that risk score was an independent risk factor for BLCA,and the Kaplan-Meier survival curve,calibration curve and risk curve all proved that the risk score model had high accuracy.Combined with the results of TIMER 2.0 database study,the expression of GATA3 was positively correlated with CD8+ T cells,while the expression of ANNEXIN1 was negatively correlated with CD8+ T cells,and the high expression of CD8+ T was negatively correlated with the survival rate of BLCA patients.In the tumor microenvironment,high expression of Tregs had a positive impact on the prognosis of BLCA patients(P<0.001),whereas high expression of tumor-associated macrophages was associated with poor prognosis in BLCA patients(P<0.001).Finally,the results of correlation analysis for differential expression of immune checkpoint-related genes in high-and low-risk groups showed that the expression levels of PD-L1,PD-1,CTLA4 and LAG3 were significantly higher in the high-risk group than in the low-risk group(P<0.001).Conclusions: A bladder cancer protein risk score model was successfully constructed from the TCGA database and GPCR gene set using bioinformatics,and GATA3 and ANNEXIN1 were screened as prognostically significantly relevant proteins for bladder cancer patients and were associated with the level of immune infiltration,tumor microenvironment and efficacy of immunotherapy,which are expected to be biomarkers for assessing the prognosis of bladder cancer.