Open AccessAn outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis
Author(s) -
Xiong Yaoyi,
Yuan Lushun,
Xiong Jing,
Xu Huimin,
Luo Yongwen,
Wang Gang,
Ju Lingao,
Xiao Yu,
Wang Xinghuan
Publication year - 2020
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.14918
Subject(s) - nomogram , proportional hazards model , univariate , lasso (programming language) , oncology , multivariate statistics , bladder cancer , receiver operating characteristic , medicine , multivariate analysis , cancer , bioinformatics , biology , computer science , machine learning , world wide web
Abstract The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.