An immune‐related gene signature for the prognosis of human bladder cancer based on WGCNA. (December 2022)

• Record Type: Journal Article
• Authors: Cheng, Jiangting; Zhang, Sihong; Fan, Aoyu; Li, Yaohui; Xu, Peirong; Huang, Jiaqi; He, Minke; Wang, Hang
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: Bladder cancer (BLCA) -- Immunotherapy -- Signature -- Polo like kinase 1 (PLK1) -- Gefitinib; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106186
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The innovation of immunotherapy was a milestone in the treatment of bladder cancer (BLCA). However, the treatment benefits varied by individual thus promoting the investigation of the biomarker of the patients. Unfortunately, there were not many effective predictive models, which were desired by clinicians, for BLCA that can predict the prognosis and benefit of immunotherapy. We constructed a three genes prognosis prediction model termed RiskScore based on the result of weighted correlation network analysis (WGCNA) from The Cancer Genome Atlas (TCGA) cohort (n = 406). We then validated the prediction accuracy with three validation cohort(GSE13507 (n = 165), GSE48075(n = 73), GSE32894(n = 224)). We compared the differences in gene expression, immune relate function, and immune infiltration between two groups divided by RiskScore. We further discovered the potential drug target and suitable compounds for high-risk groups. Our results suggested that the low-risk group may be more potential for immunotherapy for they have higher B cell infiltration, higher expression of immune checkpoints(PDCD1, CTLA4), and much more active immune-related pathways(B cell and T cell receptor signaling pathway). The RiskScore showed a well predictive accuracy for the prognosis of BLCA. After Spearman analysis, we found the suitable drug target and compounds for the patients in the high-risk group. The model we constructed is able to predict the prognosis of BLCA patients with ease and accuracy. PLK1 and gefitinib may be utilized for further treatment of BLCA patients. Highlights: The WGCNA algorithm could be applied to filter the key gene hub relating to the external sample traits. The patient populations that respond better to immunotherapy can be filtered out using the RiskScore model. The PLK1 and the compound gefitinib may play an important role in the treatment of BLCA according to machine learning.