Jiachen Ge,1,* Ming Tao,2,* Gaolei Zhang,1 Jianping Cai,1 Deyu Li,1 Lianyuan Tao1 

1Department of Hepatobiliary Surgery, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Department of General Surgery, Peking University Third Hospital, Beijing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Deyu Li; Lianyuan Tao, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, No. 7 Weiwu Road, Zhengzhou City, Henan Province, People’s Republic of China, Email lideyu19@hotmail.com; tly2007tly@hotmail.com

Purpose: Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC.
Materials and Methods: We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells.
Results: We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis.
Conclusion: CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.

Keywords: hepatocellular carcinoma, lncRNA, T cell exhaustion, single-cell RNA-seq, machine learning, prognostic signature