A Gleason score-related outcome model for human prostate cancer: a comprehensive study based on weighted gene co-expression network analysis.

ABSTRACT:

Background

Prostate cancer (PCa) is the second leading cause of cancer death in men in 2018. Thus, the evaluation of prognosis is crucial for clinical treatment decision of human PCa patients. We aim to establishing an effective and reliable model to predict the outcome of PCa patients.

Methods

We first identified differentially expressed genes between prostate cancer and normal prostate in TCGA-PRAD and then performed WGCNA to initially identify the candidate Gleason score related genes. Then, the candidate genes were applied to construct a LASSO Cox regression analysis model. Numerous independent validation cohorts, time-dependent receiver operating characteristic (ROC), univariate cox regression analysis, nomogram were used to test the effectiveness, accuracy and clinical

SUBMITTER: Wang Y 

PROVIDER: S-EPMC7216484 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature