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Nucleotide Metabolism and Immune Genes Can Predict the Prognostic Risk of Hepatocellular Carcinoma and the Immune Microenvironment
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Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
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Department of Pathogenic Biology and Immunology, Department of Basic Medicine, School of Medicine, Shihezi University, Shihezi 832000, China
3
School of Sports Medicine, Wuhan Institute of Physical Education, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Biology 2025, 14(8), 1079; https://doi.org/10.3390/biology14081079
Submission received: 13 May 2025
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Revised: 14 July 2025
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Accepted: 15 August 2025
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Published: 18 August 2025
(This article belongs to the Special Issue Bioinformatics in RNA Modifications and Non-Coding RNAs)
Simple Summary
Hepatocellular carcinoma (HCC) has a poor prognosis, necessitating better risk prediction tools. While nucleotide metabolism fuels tumor growth and immune evasion, and the immune microenvironment dictates therapy response, existing prognostic models typically focus on only one aspect. This study developed an integrated prognostic signature combining nucleotide metabolism and immune-related genes (NMIRGs) using TCGA-LIHC data. We identified two HCC subtypes (C1: poor prognosis, high immune infiltration; C2: better prognosis) based on NMIRG profiles. A nine-gene NMIRG signature (high-risk: HSP90AA1, HDAC1, RAC3, STC1, MAPT, BTC, CHGA, and GAL; low-risk: GHR) was constructed and validated in independent GEO datasets. The risk score was an independent prognostic factor, correlating with advanced stage, specific immune checkpoint expression, altered immune cell infiltration (e.g., increased T cells, decreased neutrophils in high-risk), higher tumor mutation burden (TMB), and microsatellite instability (MSI). The model showed potential for predicting immunotherapy response differences. Crucially, it outperformed existing single-feature models in predicting survival (higher C-index). Validated across multiple datasets and supplemented with experimental evidence, this NMIRG signature provides a superior tool for HCC risk stratification and immune microenvironment assessment, offering insights for personalized management and biomarker discovery.
Abstract
The overall survival of hepatocellular carcinoma (HCC) remains poor, highlighting the need for better prognostic tools. Nucleotide metabolism fuels tumor progression, while the immune microenvironment dictates therapy response, but integrated models combining both features are lacking. Using TCGA-LIHC transcriptomic/clinical data, we identified nucleotide metabolism and immune-related differentially expressed genes (NMIRGs), which stratified HCC patients into two subtypes via non-negative matrix factorization. A nine-gene prognostic risk signature was constructed through LASSO/Cox regression and validated using independent GEO datasets, and the NMIRG signature was further validated experimentally via RT-qPCR in HCC cell lines and independently using the HPA database for protein-level evidence. As evaluated by our risk signature, high-risk patients exhibited altered immune profiles (T cells increasing, neutrophils decreasing), elevated tumor mutation burden and microsatellite instability, and worse predicted immunotherapy response. Gene set enrichment analysis linked high-risk genes to immune pathways and low-risk genes to metabolic processes. Our risk signature predicted HCC prognosis independent of demographic features and outperformed existing signatures with superior C-index accuracy, effectively predicting immune microenvironment status and therapy benefits. Together, this integrated NMIRG signature offers enhanced prognostication and identifies promising biomarkers for personalized HCC management.
