A genomic instability-derived risk index predicts clinical outcome and immunotherapy response for clear cell renal cell carcinoma. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Wu, Sha; Li, Xiaoning
• Is Part Of: Bioengineered. Volume 12:Issue 1(2021)
• Page Start: 1642
• Page End: 1662
• Publication Date: 2021-01-01
• Subjects: Clear cell renal cell carcinoma -- genome instability -- immune microenvironment -- tumor mutation burden -- immunotherapy -- prognosis; Biomedical engineering -- Periodicals; Biotechnology -- Periodicals; Microbiology -- Periodicals; 660.6
• DOI: 10.1080/21655979.2021.1922330
• Languages: English
• ISSNs: 2165-5987

Abstract

ABSTRACT: This study aims to originate agenomic instability-derived risk index (GIRI) for prognostic analysis of clear cell renal cell carcinoma (ccRCC) and explore the mutation characteristics, immune characteristics, and immunotherapy response defined by GIRI. Differentially expressed genome instability-associated genes were obtained from the genomic unstable (GU) group and the genomic stable (GS) group. Rigorous screening conditions were assigned to the screening of hub genes, which were then used to generate the GIRI through multivariate Cox regression analysis. The selected samples were assigned to the high-risk group or the low-risk group based on the median GIRI. Possible reasons for the prognostic differences in risk subgroups were explored from the aspects of mutation profiles, immune profiles, immunomodulators, and biological pathway activities. The possibility of immunotherapy response was predicted by Tumor Immune Dysfunction and Exclusion analysis results. The prediction of drugs that might reverse the expression profiles of the risk subgroups was discovered through theonnectivity Map (CMap). High-risk populations manifested poor overall survival than low-risk populations and were characterized by elevated cumulative mutation counts and tumor mutation burden. Also, high-risk populations had higher immune scores, immunomodulator (PD-1, CTLA4, LAG3, and TIGIT) expression, and genomic instability-related pathway activities, and were more likely to reap benefits from immunotherapy. Besides, we predicted several drugs (PI3K inhibitor, ATPase inhibitor, and phenylalanyl tRNA synthetase inhibitor) targeting risk subgroups. The well established GIRI was an effective cancer biomarker for predicting ccRCC prognosis and provided apotential reference value for identifying immunotherapy response. Graphical abstract: