TNF Family–Based Signature Predicts Prognosis, Tumor Microenvironment, and Molecular Subtypes in Bladder Carcinoma - Li, Huihuang; Liu, Siyuan; Li, Chenxuan; Xiao, Zicheng; Hu, Jiao; Zhao, Cheng (Frontiers in Cell and Developmental Biology, 2022)
Background: Tumor necrosis factor (TNF) family members play vital roles in cancer development and anti-tumor immune responses. However, the expression patterns, prognostic values and immunological characteristics of TNF members in bladder carcinoma (BLCA) remain unclear. Methods: The training cohort, TCGA-BLCA, was downloaded from The Cancer Genome Atlas (TCGA); another two Gene Expression Omnibus (GEO) datasets (GSE13507 and GSE32894) and the Xiangya cohort (RNA-seq cohort collected from our hospital) were used as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) algorithm and cross-validation were used to screen variables. Cox regression model and random survival forest (RSF) were used to develop the risk score respectively. Then, we systematically correlated the TNF risk score with the tumor microenvironment (TME) cell infiltration, molecular subtypes of BLCA and the potential value for predicting the efficacy of immunotherapy. Results: We developed two TNF-based patterns, named TNF cluster 1 and TNF cluster 2. TNF cluster 1 exhibited poorer survival outcome and an inflamed TME characteristic compared to TNF cluster 2. We then filtered out 196 differential expressed genes (DEGs) between the two TNF clusters and applied the LASSO algorithm and cross-validation to screen out 22 genes to build the risk score. For risk score, we found that RSF exhibited higher efficacy than the cox regression model, and we chose the risk score developed by RSF for the following analysis. BLCA patients in the higher risk score group showed significantly poorer survival outcomes. Moreover, these results could be validated in the external validation cohorts, including the GSE13507, GSE32894 and Xiangya cohorts. Then, we systematically correlated the risk score with TME cell infiltration and found that it was positively correlated with the infiltration of a majority of immune cells. Also, a higher risk score indicated a basal subtype of BLCA. Notably, the relationship between risk score, TME cell infiltration and molecular subtypes could be validated in the Xiangya cohort. Conclusions: We developed and validated a robust TNF-based risk score, which could predict prognostic outcomes, TME and molecular subtypes of BLCA. However, the value of risk score predicting the efficacy of immunotherapy needs further research.