A Four-Cell-Senescence-Regulator-Gene Prognostic Index Verified by Genome-Wide CRISPR Can Depict the Tumor Microenvironment and Guide Clinical Treatment of Bladder Cancer

Abstract

Bladder cancer (BCa) is the 10th most commonly diagnosed cancer worldwide, and cellular senescence is defined as a state of permanent cell cycle arrest and considered to play important roles in the development and progression of tumor. However, the comprehensive effect of senescence in BCa has not ever been systematically evaluated. Using the genome-wide CRISPR screening data acquired from DepMap (Cancer Dependency Map), senescence genes from the CellAge database, and gene expression data from The Cancer Genome Atlas (TCGA), we screened out 12 senescence genes which might play critical roles in BCa. A four-cell-senescence-regulator-gene prognostic index was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression model. The transcriptomic data and clinical information of BCa patients were downloaded from TCGA and Gene Expression Omnibus (GEO). We randomly divided the patients in TCGA cohort into training and testing cohorts and calculated the risk score according to the expression of the four senescence genes. The validity of this risk score was validated in the testing cohort (TCGA) and validation cohort (GSE13507). The Kaplan–Meier curves revealed a significant difference in the survival outcome between the high- and low-risk score groups. A nomogram including the risk score and other clinical factors (age, gender, stage, and grade) was established with better predictive capacity of OS in 1, 3, and 5 years. Besides, we found that patients in the high-risk group had higher tumor mutation burden (TMB); lower immune, stroma, and ESTIMATE scores; higher tumor purity; aberrant immune functions; and lower expression of immune checkpoints. We also performed gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to investigate the interaction between risk score and hallmark pathways and found that a high risk score was connected with activation of senescence-related pathways. Furthermore, we found that a high risk score was related to better response to immunotherapy and chemotherapy. In conclusion, we identified a four-cell-senescence-regulator-gene prognostic index in BCa and investigated its relationship with TMB, the immune landscape of tumor microenvironment (TME), and response to immunotherapy and chemotherapy, and we also established a nomogram to predict the prognosis of patients with BCa.

A novel prognostic model based on cellular senescence-related gene signature for bladder cancer

“…Sun et al. identified a four-cell-senescence-regulator-gene prognostic index to predict the prognosis of patients with BCa ( 73 ). These four genes were PSMD14, PSMB5, PRPF19 and TPR.…”

A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence

“…It is necessary to expand the sample size to further verify this result. Senescence is a steady state that removes sick cells and stabilizes the collective internal environment ( 11 ). It is also thought to prevent tumor development ( 28 ).…”

“…Senescent tumor cells can modulate the tumor microenvironment (TME), transform surrounding unsenescent cells into senescent cells, and recruit and activate immune cells to produce anti-tumor and pro-tumor effects ( 8 , 9 ). Cellular senescence is capable of limiting tumor growth progression and is considered a potential therapeutic target ( 11 ). Adriamycin and bleomycin can induce senescence and thus exert anti-tumor effects.…”

A prognostic signature associated with cell senescence predicts survival outcomes and strongly associates with immunotherapy and chemotherapy response in breast cancer

“…[7] Several prediction models derived from cell senescence-related genes (CSRGs) have been developed to predict prognosis and treatment efficacy for multiple types of cancer, highlighting the critical role of these genes in cancer biology. [8,9] In this study, we firstly generated molecular clusters derived from CSRGs, and then established a risk signature for the overall survival (OS) prediction of BC patients. Differences in clinical characteristics, immune infiltration, chemotherapy and radiotherapy response between different risk groups were further analyzed.…”