Identification of bladder cancer subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes

• Published in: Scientific reports Vol. 14; no. 1; pp. 14431 - 19
• Main Authors: Wu, Jiyue, Zhang, Feilong, Zheng, Xiang, Chen, Dongshan, Li, Zhen, Bi, Qing, Qiu, Xuemeng, Sun, Zejia, Wang, Wei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67; 631/67/589; 631/67/589/1336; Algorithms; Biomarkers, Tumor - genetics; Bladder cancer; Chemotherapy; Female; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humanities and Social Sciences; Humans; Immune checkpoint; Immune Checkpoint Proteins - genetics; Immune Checkpoint Proteins - metabolism; Immune checkpoints; Immunotherapy; Immunotherapy - methods; Individualized treatment; Male; multidisciplinary; Nomograms; Patients; Prognosis; Science; Science (multidisciplinary); Urinary Bladder Neoplasms - drug therapy; Urinary Bladder Neoplasms - genetics; Urinary Bladder Neoplasms - immunology; Urinary Bladder Neoplasms - therapy; Prognosis; Immune checkpoints; Bladder cancer; Immunotherapy; Individualized treatment
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-65198-8

Immunotherapy based on immune checkpoint genes (ICGs) has recently made significant progress in the treatment of bladder cancer patients, but many patients still cannot benefit from it. In the present study, we aimed to perform a comprehensive analysis of ICGs in bladder cancer tissues with the aim of evaluating patient responsiveness to immunotherapy and prognosis. We scored ICGs in each BLCA patient from TCGA and GEO databases by using ssGSEA and selected genes that were significantly associated with ICGs scores by using the WCGNA algorithm. NMF clustering analysis was performed to identify different bladder cancer molecular subtypes based on the expression of ICGs-related genes. Based on the immune related genes differentially expressed among subgroups, we further constructed a novel stratified model containing nine genes by uni-COX regression, LASSO regression, SVM algorithm and multi-COX regression. The model and the nomogram constructed based on the model can accurately predict the prognosis of bladder cancer patients. Besides, the patients classified based on this model have large differences in sensitivity to immunotherapy and chemotherapy, which can provide a reference for individualized treatment of bladder cancer.