Predicting Survival Signature of Bladder Cancer Related to Cancer-Associated Fibroblast (CAF) Constructed by Intersecting Genes in TCGA and GEO.

Bladder cancer was one of the most common carcinomas around the world. However, the mechanism of the disease still remained to be investigated. We expected to establish a prognostic survival model with 9 prognostic genes to predict overall survival (OS) in patients of bladder cancer. The gene expression data of bladder cancer were obtained from TCGA and GEO datasets. TCGA and GEO datasets were used for screening prognostic genes along with developing and validating a 9-gene prognostic survival model by method of weighted gene co-expression network analysis (WGCNA) and LASSO with Cox regression. The relative analysis of evaluate tumor burden mutation (TBM), GO, KEGG, chemotherapy drug and functional pathway were also performed based on CAF-related mRNAs. 151 Overlapping CAF-related genes were distinguished after intersecting differentially expressed genes from 945 genes in TCGA and 491 genes in GEO dataset. 9 Prognostic genes (MSRB2, AGMAT, KLF6, DDAH2, GADD45B, SERPINE2, STMN3, TEAD2, and COMP) were used for construction of prognostic model after LASSO with Cox regression. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Functional analysis indicated chemokine, cytokine, ECM interaction, oxidative stress and apoptosis were highly appeared. Potential drugs targeted different CAF-related genes like vemurafenib, irofulven, ghiotepa, and idarubicin were found as well. We constructed a novel 9 CAF-related mRNAs prognostic model and explored the gene expression and potential functional information of related genes, which might be worthy of clinical application. In addition, potential chemotherapy drugs could provide useful insights into the potential clinical treatment of bladder cancer.