Integrated Multi-Omics and Pathomics Analysis for Prognostic Modeling of Hepatocellular Carcinoma Reveals Oxidative Stress-Driven Immunometabolic Regulatory Mechanisms

Hepatocellular carcinoma (HCC), a major cause of cancer mortality, exhibits strong ties to oxidative stress (OS), though integrated multi-omics studies linking OS mechanisms to clinically predictive models remain scarce. To address this, we integrated European-descent GWAS data (2,852 HCC cases vs. 447,587 controls) with 1,065 OS-related genes, identifying 176 potential HCC-associated genes (P < 0.05) via TWAS (UTMOST/GBJ tests), including 12 key OS drivers. Pathomic features extracted from 379 TCGA HCC histopathological images (ResNet-50/CellProfiler) informed prognostic modeling, with histopathology-gene correlations mapped via Spearman analysis. Single-cell transcriptomics (GSE125449) uncovered CXCL1⁺ malignant cell interactions with NCF4⁺ macrophages through the CCL20-CCR6 axis. An elastic net-selected 70-feature gradient boosting machine (GBM) model demonstrated robust prognostic performance (training: 1/3/5-year AUC = 0.834/0.888/0.918; validation: AUC = 0.747/0.814/0.826), with the risk score serving as an independent prognostic factor (HR = 25.402, P < 0.001). TCGA analyses further linked risk scores to altered immune microenvironments, somatic mutations (e.g., TP53), and activated energy/metabolic pathways. This study elucidates OS-driven immunometabolic regulatory mechanisms in HCC and delivers an integrated histology-genomic prognostic model with implications for immunotherapy strategies.