1Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China.
2Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg, 620000 Russia.
Hereditas. 2019 Jul 9;156:24. doi: 10.1186/s41065-019-0100-1. eCollection 2019.
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.
膀胱癌（BLCA）是全球最常见的内部恶性肿瘤之一，预后较差。本研究旨在探索有效的预后生物标志物，并构建 BLCA 患者的预后风险评分模型。
基于从癌症基因组图谱数据库中检索到的 RNA-Seq 数据，使用加权基因共表达网络分析（WGCNA）来识别与 BLCA 病理分期相关的共表达模块。使用 Cox 比例风险回归模型和随机森林筛选预后生物标志物，以构建可预测 BLCA 患者预后的风险评分模型。使用 GSE13507 数据集作为独立测试数据集，以测试风险评分模型预测 BLCA 患者预后的性能。
WGCNA 鉴定出七个共表达模块，其中与 BLCA 病理分期最显著相关的是棕色模块，包含 77 个基因。Cox 比例风险回归模型和随机森林鉴定出 TPST1 和 P3H4 为预后生物标志物。升高的 TPST1 和 P3H4 表达与高病理分期和较差的生存相关。基于 TPST1 和 P3H4 表达水平的风险评分模型优于病理分期指标和先前提出的预后模型。
基于基因共表达网络的研究可以为 BLCA 的肿瘤发生和进展提供更多的见解，我们提出的风险评分模型可能有助于医生评估 BLCA 患者的预后。
