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.