Unraveling the significance of cuproptosis in hepatocellular carcinoma heterogeneity and tumor microenvironment through integrated single-cell sequencing and machine learning approaches.

Background: Hepatocellular carcinoma (HCC) exhibits pronounced heterogeneity, which significantly limits the effectiveness of precision therapies. A comprehensive understanding of the biological characteristics and molecular mechanisms underlying HCC cell subpopulations is crucial for improving prognostic predictions and refining treatment strategies.

Methods: Single-cell RNA sequencing data were obtained from the GEO database and processed using the Seurat R package for quality control, including data filtering, batch effect correction, and dimensionality reduction via PCA and UMAP to visualize cell distribution and identify distinct subpopulations. Cell types were annotated using established marker genes and literature references. The GSVA method was applied to evaluate the activity of 18 programmed cell death pathways. Cell developmental trajectories were reconstructed using Monocle 2 and validated with cytoTRACE to assess differentiation potential. Metabolic pathway activity was analyzed using the scMetabolism package. Bulk RNA sequencing data from the TCGA cohort were integrated to identify prognosis-associated genes through univariate Cox regression. The malignant potential of tumor subpopulations was quantified using GSVA scoring. Weighted gene co-expression network analysis (WGCNA) was employed to identify cuproptosis-related genes. A risk scoring model was constructed using LASSO regression and multivariate Cox regression based on cuproptosis-related genes and marker genes of cuproptosis-characterized tumor cells. The model's performance was validated across TCGA, GEO, and ICGC datasets. Additionally, the relationships between risk scores, clinical characteristics, key signaling pathways, and immunotherapy responses were explored. Finally, a prognostic nomogram was developed to support clinical decision-making.

Results: 12 programmed cell death pathways were enriched in tumors, with cuproptosis defining HCC, particularly in the C2 subpopulation. GSVA highlighted high-risk patient enrichment in proliferation, DNA repair, and metabolism, reflecting aggressive malignancy. Developmental trajectory and metabolic analyses confirmed greater stemness and metabolic activity in C2. TCGA linked cuproptosis-related subpopulations to poor prognosis. The risk model stratified patients (validated in TCGA/GEO/ICGC), correlating with clinical grade, T-stage, survival (HR = 2.597, 95%CI 2.051-3.289, P < 0.05). The nomogram showed strong predictive power (C-index = 0.716), aiding clinical decisions.

Conclusions: The C2 subpopulation represents the most malignant subset of HCC cells, with cuproptosis serving as a defining characteristic of this subgroup. The risk scoring and nomogram models based on cuproptosis-related genes offer novel insights and a robust scientific foundation for prognostic prediction and personalized treatment in HCC patients. These findings highlight the potential of targeting cuproptosis and tumor microenvironment interactions to improve therapeutic outcomes in HCC.