Comprehensive analysis of tumor immune-related gene signature for predicting prognosis, immunotherapy, and drug sensitivity in bladder urothelial carcinoma.

Abstract

Background: Bladder urothelial carcinoma (BLCA) is globally recognized as a prevalent malignancy. Its treatment remains challenging due to the extensive morbidity, high mortality rates, and compromised quality of life from postoperative complications and the lack of specific molecular targets. Our aim was to establish a prognostic model to evaluate the prognostic significance, assess immunotherapy responses, and determine drug susceptibility in patients with BLCA.

Methods: From The Cancer Genome Atlas (TCGA) datasets, we obtained BLCA clinical details and expression data of immune-related genes. These data were analyzed using R and related packages. Differential expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, weighted gene co-expression network analysis (WGCNA), univariate and multivariate Cox regression analysis, prognostic analysis, model establishment and evaluation, gene set variation analysis (GSVA), immune function and checkpoint analysis, immunotherapy response prediction, and prediction of drug sensitivity were conducted.

Results: A total of 11 differentially expressed immune genes (DEIGs) were selected to establish the bladder carcinoma immune-related gene signature for BLCA prognosis prediction. In both the training and testing groups, the high-risk cohort showed a lower overall survival (OS) than the low-risk cohort. The area under the receiver operating characteristic curve (AUC) was 0.712 in the training group and 0.631 in the testing group, highlighting its predictive capacity. In the external validation datasets GSE39281 and IMvigor210, the OS of the high-risk group was significantly lower than that of the low-risk group, with AUC values of 0.609 and 0.563, respectively. Patients in the training group were categorized into low- and high-risk groups based on the bladder carcinoma immune gene signature (BCIGS) median risk score. GSVA showed 21 KEGG pathways positively correlated with model risk scores. The high-risk group presented with elevated stromal score, immune score, ESTIMATE score, and T cell exclusion score level. Conversely, the low-risk group displayed heightened cytotoxic T-lymphocyte antigen 4 (CTLA4) expression, indicative of a better response to immune checkpoint inhibitors (ICIs). Notably, significant disparities were found in immune subtypes, immune-related function, and immune-related survival between the two risk groups. The AUC values of our model are 0.765 and 0.660, respectively, surpassing those of other models, such as the tumor inflammation signature (TIS), tumor immune dysfunction and exclusion (TIDE), and various clinical factors. We also presented a nomogram, with the AUCs for predicting 1-, 2-, and 3-year OS at 0.727, 0.772, and 0.765 respectively, suggesting the signature's robust predictive power. Finally, 20 small molecular compounds were identified, with the TW.37 drug's half maximum inhibitory concentration (IC₅₀) value difference being the most pronounced between the high- and low-risk patient groups, indicating its potential as a treatment option.

Conclusions: Our constructed immune-related gene signature model forecasts BLCA patient prognosis and potentially guides individualized immunotherapy and chemotherapeutic drug choices.