Liu Rongqiang, Ye Jing, Wang Jianguo, Ma Wangbin, Qiu Zhendong, Yu Jia, Wang Weixing
Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
Sci Rep. 2025 Feb 3;15(1):4130. doi: 10.1038/s41598-025-88377-7.
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.
肝细胞癌发病率高且预后较差。在本研究中，我们通过肝细胞癌的单细胞测序数据研究了T细胞相关基因在预后中的价值。该研究纳入了12例肝细胞癌单细胞测序病例。采用高维加权基因共表达网络分析（hdWGCNA）来识别与CD4 T细胞、CD8 T细胞和耗竭性T细胞相关的基因模块。通过AUCell算法揭示了耗竭性T细胞中信号通路活性的改变。运用xCELL、TIMER、QUANTISEQ、CIBERSORT和CIBERSORT-abs来探索免疫细胞浸润情况。使用免疫检查点抑制剂基因和TIDE方法预测免疫治疗反应。最后，采用免疫组织化学和实时定量PCR验证基因表达。hdWGCNA算法鉴定出40个与CD4 T细胞、CD8 T细胞和耗竭性T细胞密切相关的基因。最终选择7个基因进行转录组数据建模。三个独立数据集的结果表明该模型具有较强的预后价值。模型基因是影响患者CD4 T细胞和CD8 T细胞浸润的关键因素。低风险组患者的PD-1免疫治疗疗效更高。耗竭性T细胞内信号通路活性的改变是导致免疫功能下降的关键因素。CD8 T细胞、CD4 T细胞和耗竭性T细胞中7个基因的差异表达是改善肝癌免疫治疗反应的关键靶点。
