This study was conducted to identify genes that are differentially expressed in paracancerous tissue and to determine the potential predictive value of selected gene panel. Gene transcriptome data of bladder tissue was downloaded from UCSC Xena browser and NCBI GEO repository, including GTEx (the Genotype-Tissue Expression project) data, TCGA (The Cancer Genome Atlas) data, and GEO (Gene Expression Omnibus) data. Differentially Expressed Genes (DEGs) analysis was performed to identify tumor-DEGs candidate genes, using the intersection of tumor-paracancerous DEGs genes and paracancerous-normal DEGs genes. The survival-related genes were screened by Kaplan-Meier (KM) survival analysis and univariable Cox regression with the cutoff criteria of KM < 0.05 and cox p-value < 0.05. The risk model was developed using Lasso regression. The clinical data were analyzed by univariate and multivariate Cox regression analysis. Gene Ontology (GO) and KEGG enrichment analysis were performed in the DEGs genes between the high-risk and low-risk subgroups. We identified six survival-related genes, EMP1, TPM1, NRP2, FGFR1, CAVIN1, and LATS2, found in the DEG analyses of both, tumor-paracancerous and paracancerous-normal differentially expressed data sets. Then, the patients were classified into two clusters, which can be distinguished by specific clinical characteristics. A three-gene risk prediction model (EMP1, FGFR1, and CAVIN1) was constructed in patients within cluster 1. The model was applied to categorize cluster 1 patients into high-risk and low-risk subgroups. The prognostic risk score was considered as an independent prognostic factor. The six identified survival-related genes can be used in molecular characterization of a specific subtype of bladder cancer. This subtype had distinct clinical features of T (topography), N (lymph node), stage, grade, and survival status, compared to the other subtype of bladder cancer. Among the six identified survival-related genes, three-genes, EMP1, FGFR1, and CAVIN1, were identified as potential independent prognostic markers for the specific bladder cancer subtype with clinical features described.
Cell death discovery. 2020 Jul 13*** epublish ***
Shuting Cheng, Zhou Jiang, Jing Xiao, Huiling Guo, Zhengrong Wang, Yuhui Wang
Health Ministry Key Laboratory of Chronobiology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, P.R. China.