A Novel Cholesterol Metabolism-Related lncRNA Signature Predicts the Prognosis of Patients with Hepatocellular Carcinoma and Their Response to Immunotherapy

Abstract

Background: The survival rate of hepatocellular carcinoma (HCC) is low and the prognosis is poor. Metabolic reprogramming is still an emerging hallmark of cancer, and reprogramming of cholesterol metabolism plays a crucial action in tumor pathogenesis. Increasing evidence suggests that cholesterol metabolism affects the cell proliferation, invasion, migration, and resistance to chemotherapy of HCC. To date, no long noncoding RNA (lncRNA) signature associated with cholesterol metabolism has been developed to predict the outcome of patients with HCC. Methods: The RNA-seq data as well as the prognostic and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We conducted univariate and multivariate analyses to assess cholesterol metabolism-related lncRNAs correlated with the prognosis of patients with HCC in order to construct a prognostic signature. Functional differences between low- and high-risk groups were investigated using genomic enrichment analysis (GSEA). Kaplan-Meier (KM) curves were applied to explore the overall survival (OS) of the low- and high-risk groups. Single-sample genomic enrichment analysis (ssGSEA) was applied to investigate the association between this predictive signature and immune function. We subsequently examined how this signature relates to treatment response in HCC patients. Results: A prognostic signature comprising six lncRNAs related to cholesterol metabolism was constructed (AC124798.1, AL031985.3, AC103760.1, NRAV, WAC-AS1 and AC022613.1). We found that low-risk groups showed a better prognosis than high-risk groups. In HCC patients, the cholesterol metabolism-related lncRNA signature may be served as an independent prognostic factor. Cholesterol metabolism-related lncRNA signature had higher diagnostic efficiency compared to clinicopathologic variables. After stratifying patients according to different clinicopathological variables, patients with low-risk had a longer OS compared with high-risk patients. The ssGSEA demonstrated that this signature was closely related to the immune status of HCC patients. GSEA analysis demonstrated that immune- and tumor-related pathways were predominantly enriched in the high-risk group. High-risk patients were more responsive to immune checkpoint inhibitors (ICIs) and conventional chemotherapeutic agents. Conclusions: This cholesterol metabolism-related lncRNA signature can predict the prognosis of HCC patients and guide the clinical management of HCC patients, including immunotherapy.