Research Paper Volume 13, Issue 1 pp 675—693
A novel immune-related genes prognosis biomarker for hepatocellular carcinoma
- 1 Department of General Surgery, Taizhou Central Hospital (Taizhou University Hospital), Taizhou 318000, China
- 2 Department of Anesthesia Surgery, Taizhou Central Hospital (Taizhou University Hospital), Taizhou 318000, China
- 3 Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
- 4 Department of Anesthesia, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510655, China
Received: July 17, 2020 Accepted: October 5, 2020 Published: November 26, 2020https://doi.org/10.18632/aging.202173
How to Cite
Copyright: © 2020 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Hepatocellular carcinoma (HCC) is closely associated with the immune microenvironment. To identify the effective population before administering treatment, the establishment of prognostic immune biomarkers is crucial for early HCC diagnosis and treatment.
Results: A total of 335 IRGs identified from 788 overlapping IRGs were associated with the survival of HCC. A prognostic immunoscore model was identified. The Kaplan-Meier survival curves and time-dependent ROC analysis revealed a powerful prognostic performance of immunoscore signature via multi validation. Besides, the immunoscore signature exhibited a better predictive power compared to other prognostic signatures. Gene set enrichment analysis showed multiple signaling differences between the high and low immunoscore group. Furthermore, immunoscore was significantly associated with multiple immune cells and immune infiltration in the tumor microenvironment.
Conclusions: We identified the immunoscore as a robust marker for predicting HCC patient survival.
Methods: Three sets of immune-related genes (IRGs) were integrated to identify the overlapping IRGs. Weighted gene co-expression network analysis was performed to obtain the survival-related IRGs. Further, the prognostic immunoscore model was constructed via LASSO-penalized Cox regression analysis. Then the prognostic performance of immunoscore was evaluated. In addition, ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between immunoscore and tumor immune microenvironment.