肝细胞癌（HCC）是一种具有严峻临床预后的突出胃肠恶性肿瘤。在这方面，发现新的早期生物标志物对于改善HCC相关的死亡率具有重要的潜力。Efferocytosis是一种重要的免疫过程，它在清除凋亡细胞中起着核心作用。然而，关于Efferocytosis相关基因（EFRGs）在HCC中的作用的综合研究非常有限，对其对HCC免疫治疗和靶向药物干预的调控影响尚不清楚。我们从TCGA数据库获取了HCC患者的RNA测序数据和临床特征。为了确定HCC中具有预后意义的基因，我们使用limma软件包进行了单变量Cox回归分析。随后，我们采用机器学习算法识别了关键基因。为了评估不同HCC亚型的免疫景观，我们采用了CIBERSORT算法。此外，我们采用单细胞RNA测序(scRNA-seq)技术研究了免疫细胞中EFRGs的表达水平，并探索了HCC组织中的细胞间通讯。使用CCK-8实验评估了HCC细胞的迁移能力，通过创面愈合实验确定了药物敏感性预测的可靠性。我们成功地确定了一组名为EFRGs的九个基因，这些基因具有建立肝细胞癌特异性预后模型的显著潜力。此外，通过利用该模型导出的个体风险评分，我们能够将患者分为两个不同的风险组，揭示了在免疫浸润模式和免疫治疗反应方面的显著差异。值得注意的是，通过包括对HepG2和Huh7细胞系进行的创面愈合实验和CCK8实验在内的综合实验研究，验证了该模型准确预测药物反应的能力。我们构建了一个EFRGs模型，它在免疫治疗和化疗的预后评估和决策支持中具有宝贵的工具作用。版权©2023年Liu, Li, Zhang, Hu and Li。

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.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.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.We constructed an EFRGs model that serves as valuable tools for prognostic assessment and decision-making support in the context of immunotherapy and chemotherapy.Copyright © 2023 Liu, Li, Zhang, Hu and Li.