Multi-omics analysis of the mechanism by ATP13A2 regulates the tumor microenvironment and prognosis in hepatocellular carcinoma.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is a prevalent global cancer. Most patients with HCC are diagnosed at an advanced stage. Therefore, new biomarkers and treatments are urgently needed. METHODS: We employed eQTL and intersected the results with aging-related genes, ultimately identifying ATP13A2 as the target gene for our study. The expression and intercellular communication of ATP13A2 in HCC were analyzed using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (stRNA-seq). Subsequently, we utilized the Deep Learning Survival Neural Network (DeepSurv) model to construct a prognostic model. Additionally, we performed RNA sequencing (RNA-seq) analysis. In vitro, CCK8, cell wound healing, and flow cytometry assays were used to identify the potential functions of ATP13A2 in HCC cells. RESULTS: ATP13A2 is positively associated with HCC risk. scRNA-seq analysis demonstrated that ATP13A2 + malignant cells exhibited stronger interactions with tumor microenvironment (TME) cells. stRNA-seq analysis revealed that ATP13A2 + malignant cells were significantly spatially correlated with TME cells. The DeepSurv prognostic model indicated that HCC patients with high risk scores had a significantly lower survival rate than those with low risk scores. In vitro, knockdown of ATP13A2 affected the proliferation, apoptosis, and migration of HCC cells. CONCLUSION: The ATP13A2 gene is closely related to TME, and its high expression is indicative of poor prognosis. ATP13A2 has the potential to serve as a biomarker for prognosis and efficacy assessment of HCC and may offer a new therapeutic target for its treatment.