Identification of the CD8+T-cell exhaustion signature of Hepatocellular Carcinoma for the Prediction of prognosis and Immune microenvironment by Integrated Analysis of Bulk- and Single-Cell RNA Sequencing Data

Background: Hepatocellular carcinoma (HCC) is a prevalent type of cancer with high incidence and mortality rates. It is the third most common cause of cancer-related deaths. CD8+ T cell exhaustion (TEX) is a progressive decline in T cell function due to sustained T cell receptor stimulation from continuous antigen exposure. Studies have shown that CD8+ TEX plays an important role in the anti-tumor immune process and is significantly correlated with patient prognosis. The aim of the research is to establish a reliable CD8+ TEX-based signature using single-cell RNA sequencing (scRNAseq) and high-throughput RNA sequencing, providing a new approach to evaluate HCC patient prognosis and immune microenvironment. Method: The RNA-seq data of hepatocellular carcinoma (HCC) patients is download from three different databases: The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC). HCC's 10x scRNA data is acquired from GSE149614. Based on single-cell sequencing data, CD8+ TEX-related genes were identified using UMAP algorithm, singleR, and marker gene methods. Afterwards, we proceeded to construct CD8+ TEX signature using differential gene analysis, univariate COX regression analysis, LASSO regression, and multivariate COX regression analysis. We also validated CD8+ TEX signature in GEO and ICGC external cohorts and investigated clinical characteristics, chemotherapy sensitivity, mutation landscape, functional analysis, and immune cell infiltration in different risk groups. Finally, we identified genes that regulate CD8+ T cells through correlation analysis. Result: The CD8+ TEX signature, consisting of 13 genes (HSPD1, UBB, DNAJB4, CALM1, LGALS3, BATF, COMMD3, IL7R, FDPS, DRAP1, RPS27L, PAPOLA, GPR171), has been found to have a strong predictive effect on the prognosis of HCC. The KM analysis shows that the overall survival rate of patients in the low-risk group is higher than that of patients in the high-risk group across different datasets and specific populations. The research findings suggest that the risk score is an independent predictor of HCC prognosis. The model based on clinical features and risk score has a strong predictive effect. We have observed significant differences among various risk groups in terms of clinical characteristics, functional analysis, mutation landscape, chemotherapy sensitivity, and immune cell infiltration. Finally, it was discovered that BATF is a critical gene involved in the regulation of CD8+ T cells. Conclusion: We constructed a CD8+ T cell exhaustion signature to predict the survival probability of patients with HCC. We also found that the model could predict the sensitivity of targeted drugs and immune cell infiltration, and the risk score was negatively correlated with CD8+ T cell infiltration. In summary, this is the first CD8+ T cell exhaustion signature of HCC for the prediction of prognosis and immune microenvironment by integrated analysis of bulk and single-cell RNA sequencing data.