Single-cell landscape of dynamic changes in CD8 T cells, CD4 T cells and exhausted T cells in hepatocellular carcinoma.

Hepatocellular carcinoma has a high incidence and poor prognosis. In this study, we investigated the value of T-cell-related genes in prognosis by single-cell sequencing data in hepatocellular carcinoma. Twelve cases of hepatocellular carcinoma single-cell sequencing were included in the study. The high dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify gene modules associated with CD4 T cells, CD8 T cells and exhausted T cells. Altered signaling pathway activity in exhausted T cells was uncovered by the AUCell algorithm. xCELL, TIMER, QUANTISEQ, CIBERSORT and CIBERSORT-abs were performed to explore immune cell infiltration. Immune checkpoint inhibitor genes and TIDE methods were used to predict immunotherapy response. Finally, immunohistochemistry and real-time PCR were used to verify gene expression. The hdWGCNA algorithm identified 40 genes strongly associated with CD4 T cells, CD8 T cells and exhausted T cells. Seven genes were finally selected for transcriptome data modeling. The results of the three independent datasets suggested that the model had strong prognostic value. Model genes were critical factors influencing CD4 T cell and CD8 T cell infiltration in patients. The efficacy of PD-1 immunotherapy was higher in patients belonging to the low-risk group. Alterations in signaling pathways' activity within exhausted T cells were crucial factors contributing to the decline in immune function. Differential expression of seven genes in CD8 T cells, CD4 T cells and exhausted T cells were key targets for improving immunotherapy response in HCC.