Identification of a Tumor Immunological Phenotype-Related Gene Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Hepatocellular Carcinoma.

Abstract

Hepatocellular carcinoma (HCC) is the predominant subtype of primary liver cancer and represents a highly heterogeneous disease, making it hard to predict the prognosis and therapy efficacy. Here, we established a novel tumor immunological phenotype-related gene index (TIPRGPI) consisting of 11 genes by Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict HCC prognosis and immunotherapy response. TIPRGPI was validated in multiple datasets and exhibited outstanding performance in predicting the overall survival of HCC. Multivariate analysis verified it as an independent predictor and a TIPRGPI-integrated nomogram was constructed to provide a quantitative tool for clinical practice. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in tumor microenvironment were shown between the TIPRGPI high and low-risk groups. Notably, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade (ICB) therapy of the low-risk group. Besides, six potential drugs binding to the core target of the TIPRGPI signature were predicted via molecular docking. Taken together, our study shows that the proposed TIPRGPI was a reliable signature to predict the risk classification, immunotherapy response, and drugs candidate with potential application in the clinical decision and treatment of HCC. The novel "TIP genes"-guided strategy for predicting the survival and immunotherapy efficacy, we reported here, might be also applied to more cancers other than HCC.

• Authors: Yuqin Tang, Chengbin Guo, Zhao Yang, Yumei Wang, Yongqiang Zhang, Dong Wang
• Journal: Frontiers in immunology (Front Immunol) Vol. 13 Pg. 862527 ( 2022) ISSN: 1664-3224 [Electronic] Switzerland
• PMID: 35493471 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2022 Tang, Guo, Yang, Wang, Zhang and Wang.

Topics

• Carcinoma, Hepatocellular (genetics, metabolism, therapy)
• Humans
• Immunotherapy
• Kaplan-Meier Estimate
• Liver Neoplasms (genetics, metabolism, therapy)
• Molecular Docking Simulation
• Phenotype
• Prognosis
• Tumor Microenvironment (genetics)