Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma - Liu, Tao; Li, Chao; Zhang, Jiantao; Hu, Han; Li, Chenyao (Frontiers in Immunology, 2023)
Background: Hepatocellular carcinoma (HCC) represents a prominent gastrointestinal malignancy with a grim clinical outlook. In this regard, the discovery of novel early biomarkers holds substantial promise for ameliorating HCC-associated mortality. Efferocytosis, a vital immunological process, assumes a central position in the elimination of apoptotic cells. However, comprehensive investigations exploring the role of efferocytosis-related genes (EFRGs) in HCC are sparse, and their regulatory influence on HCC immunotherapy and targeted drug interventions remain poorly understood. Methods: RNA sequencing data and clinical characteristics of HCC patients were acquired from the TCGA database. To identify prognostically significant genes in HCC, we performed the limma package and conducted univariate Cox regression analysis. Subsequently, machine learning algorithms were employed to identify hub genes. To assess the immunological landscape of different HCC subtypes, we employed the CIBERSORT algorithm. Furthermore, single-cell RNA sequencing (scRNA-seq) was utilized to investigate the expression levels of ERFGs in immune cells and to explore intercellular communication within HCC tissues. The migratory capacity of HCC cells was evaluated using CCK-8 assays, while drug sensitivity prediction reliability was determined through wound-healing assays. Results: We have successfully identified a set of nine genes, termed EFRGs, that hold significant potential for the establishment of a hepatocellular carcinoma-specific prognostic model. Furthermore, leveraging the individual risk scores derived from this model, we were able to stratify patients into two distinct risk groups, unveiling notable disparities in terms of immune infiltration patterns and response to immunotherapy. Notably, the model's capacity to accurately predict drug responses was substantiated through comprehensive experimental investigations, encompassing wound-healing assay, and CCK8 experiments conducted on the HepG2 and Huh7 cell lines. Conclusions: We constructed an EFRGs model that serves as valuable tools for prognostic assessment and decision-making support in the context of immunotherapy and chemotherapy.