Construction of a tumor immune microenvironment-related risk scoring model for prognosis of hepatocellular carcinoma.

Abstract

Objective: This study aims to develop a prognostic model for HCC based on TME-related factors.

Introduction: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis, largely due to the complex and heterogeneous interactions between stromal and immune cells within the tumor microenvironment (TME).

Methods: Genome and transcriptome data, as well as clinical information of HCC patients, were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The TME score was evaluated using the "ESTIMATE" R package. Differentially expressed genes (DEGs) associated with TME phenotype were analyzed using the LIMMA R-package. Survival outcomes were compared using Kaplan-Meier curves with log-rank test and Cox proportional hazards model. Protein-Protein Interaction (PPI) networks integrated with multivariate survival and LASSO analyses were utilized to identify TME-related hub genes for a risk score model. A nomogram predicting prognosis of HCC patients was developed through four independent cohorts.

Results: The TME scores showed a negative correlation with tumor progression and survival in HCC patients. We identified 50 core genes with high connectivity in the PPI network, as along with 33 key DEGs associated with survival in HCC. Intersection analysis revealed six hub genes -CXCL8, CXCL1, CCR7, IL7R, MMP9, and CD69. The risk score based on these six TME-related hub genes was significantly associated with overall survival and clinicopathological characteristics of HCC patients. Furthermore, the nomogram demonstrated its ability to discriminate HCC patients from healthy individuals using peripheral blood mononuclear cells.

Conclusion: We have developed a TME-related risk scoring model for HCC patients and identified six hub gene panel that serve as a potential biomarker for personalized prognosis of immunotherapy and non-invasive diagnostics of HCC.