Identification of novel <scp>M2</scp> macrophage‐related molecule <scp>ATP6V1E1</scp> and its biological role in hepatocellular carcinoma based on machine learning algorithms

Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterized by late detection and suboptimal response to current therapies. The tumour microenvironment, especially the role of M2 macrophages, is pivotal in the progression and prognosis of HCC. We applied the machine learning algorithm-CIBERSORT, to quantify cellular compositions within the HCC TME, focusing on M2 macrophages. Gene expression profiles were analysed to identify key molecules, with ATP6V1E1 as a primary focus. We employed Gene Set Enrichment Analysis (GSEA) and Kaplan-Meier survival analysis to investigate the molecular pathways and prognostic significance of ATP6V1E1. A prognostic model was developed using multivariate Cox regression analysis based on ATP6V1E1-related molecules, and functional impacts were assessed through cell proliferation assays. M2 macrophages were the dominant cell type in the HCC TME, significantly correlating with adverse survival outcomes. ATP6V1E1 was robustly associated with advanced disease stages and poor prognostic features such as vascular invasion and elevated alpha-fetoprotein levels. GSEA linked high ATP6V1E1 expression to critical oncogenic pathways, including immunosuppression and angiogenesis, and reduced activity in metabolic processes like bile acid and fatty acid metabolism. The prognostic model stratified HCC patients into distinct risk categories, showing high predictive accuracy (1-year AUC = 0.775, 3-year AUC = 0.709 and 5-year AUC = 0.791). In vitro assays demonstrated that ATP6V1E1 knockdown markedly inhibited the proliferation of HCC cells. The study underscores the significance of M2 macrophages and ATP6V1E1 in HCC, highlighting their potential as therapeutic and prognostic targets.