Prediction of prognostic biomarkers for hepatocellular carcinoma and immune microenvironment infiltration based on single-cell sequencing and RNA-Seq integration.

objectiveEarly diagnosis and prognostic evaluation of hepatocellular carcinoma (HCC) remain significant challenges in clinical management. This study aims to identify prognostic biomarkers in HCC and to explore their implications in immune microenvironment infiltration.

methodsIn this study, we constructed a single-cell transcriptomic atlas of HCC, focusing on the expression profiles of T cell-related genes. Analytical approaches included cell-cell communication analysis and pseudotime trajectory analysis. To further predict and validate T cell-associated prognostic genes, we integrated transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Patients were stratified into high- and low-risk groups based on a prognostic model derived from these biomarkers. Immune infiltration levels in the tumor microenvironment were then evaluated across risk groups.

resultsA total of eight primary tumor samples and seven normal tissue samples were included as raw data in this study. Following stringent quality control and filtering, 53,477 cells were retained for downstream analysis. From these, we isolated 12,333 T cells, which were subjected to further clustering and annotation. The T cell subpopulations identified included 6314 natural killer T cells (NK T cells), 5199 effector memory CD4

conclusionThis study identified and validated prognostic biomarkers in HCC that effectively predict patient survival rates and immune infiltration characteristics. These findings provide a potential foundation for precision medicine strategies in HCC, offering novel insights into the tumor-immune microenvironment and its clinical implications.