Integrating spatial and single-cell transcriptomics via machine learning to characterize efferocytosis in hepatocellular carcinoma prognosis and immunotherapy

<h4>Background</h4>The intratumoral heterogeneity and immunosuppressive microenvironment of hepatocellular carcinoma (HCC) significantly limit therapeutic efficacy. Although efferocytosis is fundamental for tissue homeostasis, its impact on the spatial architecture of the tumor microenvironment (TME) and patient prognosis in HCC remains uncharacterized.<h4>Methods</h4>We integrated single-cell RNA sequencing, spatial transcriptomics, and large-scale bulk datasets to construct an Efferocytosis-Related Scoring System (ERG score). We identified molecular subtypes and traced key cell subsets driving high-efferocytosis features. A robust prognostic model was established via large-scale machine learning screening (101 algorithms) and validated by in vitro experiments (qRT-PCR/Western Blot).<h4>Results</h4>The ERG score indicates a specific immunosuppressive state driven by the High_ERGs_SPP1_Mac subpopulation. Spatially, this subpopulation physically co-localizes with malignant cells, potentially mediated by the MDK-SDC2 axis. The machine learning-derived risk score serves as a reliable quantitative indicator of High_ERGs_SPP1_Mac abundance, effectively predicting "cold tumor" phenotypes and immune checkpoint upregulation (e.g., PD-1, CTLA4). Additionally, experimental validation confirmed the upregulation of the core gene TPI1 (triosephosphate isomerase 1), aligning with the high glycolytic features of the high-risk subtype.<h4>Conclusion</h4>The ERG scoring system offers a novel perspective for deconvoluting HCC heterogeneity. Our findings suggest that SPP1+ macrophages spatially reshape the TME, providing a theoretical basis for optimizing prognostic stratification and personalized immunotherapy.