Research Paper Volume 16, Issue 7 pp 6537—6549

A prognostic signature established based on genes related to tumor microenvironment for patients with hepatocellular carcinoma

Zhongfeng Cui1, , Ge Li1, , Yanbin Shi2, , Xiaoli Zhao3, , Juan Wang3, , Shanlei Hu3, , Chunguang Chen1, , Guangming Li3, ,

  • 1 Department of Clinical Laboratory, Henan Provincial Infectious Disease Hospital, Zhengzhou 450000, China
  • 2 Department of Radiology, Henan Provincial Infectious Disease Hospital, Zhengzhou 450000, China
  • 3 Department of Infectious Diseases and Hepatology, Henan Provincial Infectious Disease Hospital, Zhengzhou 450000, China

Received: November 10, 2023       Accepted: March 13, 2024       Published: April 4, 2024
How to Cite

Copyright: © 2024 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Complex cellular signaling network in the tumor microenvironment (TME) could serve as an indicator for the prognostic classification of hepatocellular carcinoma (HCC) patients.

Methods: Univariate Cox regression analysis was performed to screen prognosis-related TME-related genes (TRGs), based on which HCC samples were clustered by running non-negative matrix factorization (NMF) algorithm. Furthermore, the correlation between different molecular HCC subtypes and immune cell infiltration level was analyzed. Finally, a risk score (RS) model was established by LASSO and Cox regression analyses (CRA) using these TRGs. Functional enrichment analysis was performed using gene set enrichment analysis (GSEA).

Results: HCC patients were divided into three molecular subtypes (C1, C2, and C3) based on 704 prognosis-related TRGs. HCC subtype C1 had significantly better OS than C2 and C3. We selected 13 TRGs to construct the RS model. Univariate and multivariate CRA showed that the RS could independently predict patients’ prognosis. A nomogram integrating the RS and clinicopathologic features of the patients was further created. We also validated the reliability of the model according to the area under the receiver operating characteristic (ROC) curve value, concordance index (C-index), and decision curve analysis. The current findings demonstrated that the RS was significantly correlated with CD8+ T cells, monocytic lineage, and myeloid dendritic cells.

Conclusion: This study provided TRGs to help classify patients with HCC and predict their prognoses, contributing to personalized treatments for patients with HCC.


TME: Tumor microenvironment; HCC: Hepatocellular carcinoma; NMF: Non-negative matrix factorization; ROC: Receiver Operating Characteristic; AUC: Area under curve; C-index: Concordance index; DCs: Dendritic cells; NK cell: Natural killer cell; MDSC: Myeloid-derived suppressor cell; TAM: Tumor-associated macrophage; TAN: Tumor-associated neutrophil; Treg: Regulatory T cell; TIL: Tumor-infiltrating lymphocyte; CTL: CD8+ cytotoxic T lymphocyte; IHC: Immunohistochemistry; CyTOF: Cytometry; GDC: Genomic Data Commons; TCGA: The Cancer Genome Atlas Consortium; GEO: Gene Expression Ominibus; MCP-counter: Microenvironment Cell Populations-counter; DCA: Decision curve analysis; GSEA: Gene set enrichment analysis; RMS: Restricted mean survival; LRG: Low-risk group; HRG: High-risk group.