A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival

• Published in: Hereditas 2019-07, Vol.156 (1), p.24-24, Article 24
• Main Authors: Chen, Zihao, Liu, Guojun, Hossain, Aslam, Danilova, Irina G, Bolkov, Mikhail A, Liu, Guoqing, Tuzankina, Irina A, Tan, Wanlong
• Format: Article
• Language: English
• Subjects: Algorithms; biomarkers; Bladder cancer; carcinogenesis; Computational Biology - methods; data collection; Female; Gene Expression Profiling; gene expression regulation; Gene Expression Regulation, Neoplastic; Gene Ontology; Gene Regulatory Networks; genes; Humans; Male; Models, Theoretical; P3H4; Prognosis; Protein Interaction Mapping; regression analysis; risk; Risk Assessment; Risk score model; ROC Curve; sequence analysis; Survival Analysis; TPST1; urinary bladder neoplasms; Urinary Bladder Neoplasms - diagnosis; Urinary Bladder Neoplasms - genetics; Urinary Bladder Neoplasms - metabolism; Urinary Bladder Neoplasms - mortality; WGCNA
• ISSN: 1601-5223; 0018-0661; 1601-5223
• DOI: 10.1186/s41065-019-0100-1

Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA. Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA. WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models. The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA.