Research Paper Volume 12, Issue 3 pp 2302—2332
Identification of an immune-related risk signature for predicting prognosis in clear cell renal cell carcinoma
- 1 Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- 2 Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei, China
- 3 The Institute of Urology, Anhui Medical University, Hefei, China
- 4 The Key Laboratory of Aquatic Biodiversity and Conservation of Chinese Academy of Sciences, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
- 5 University of Chinese Academy of Sciences, Beijing, China
Received: July 27, 2019 Accepted: January 7, 2020 Published: February 6, 2020
https://doi.org/10.18632/aging.102746How to Cite
Abstract
Immune status affects the initiation and progression of clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma. In this study, we identified an immune-related, five-gene signature that improves survival prediction in ccRCC. Patients were classified as high- and low-risk based on the signature risk score. Survival analysis showed differential prognosis, while principal component analysis revealed distinctly different immune phenotypes between the two risk groups. High-risk patients tended to have advanced stage, higher grade disease, and poorer prognoses. Functional enrichment analysis showed that the signature genes were mainly involved in the cytokine-cytokine receptor interaction pathway. Moreover, we found that tumors from high-risk patients had higher relative abundance of T follicular helper cells, regulatory T cells, and M0 macrophages, and higher expression of PD-1, CTLA-4, LAG3, and CD47 than low-risk patients. This suggests our gene signature may not only serve as an indicator of tumor immune status, but may be a promising tool to select high-risk patients who may benefit from immune checkpoint inhibitor therapy. Multivariate Cox regression analysis showed that the signature remained an independent prognostic factor after adjusting for clinicopathological variables, while prognostic accuracy was further improved after integrating clinical parameters into the analysis.