A Novel Ferroptosis-Related Long Non-Coding RNA Prognostic Signature Correlates With Genomic Heterogeneity, Immunosuppressive Phenotype, and Drug Sensitivity in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) with high heterogeneity is a common malignancy worldwide, but effective treatments are limited. Ferroptosis plays a critical role in tumors as a novel iron-dependent and reactive oxygen species-reliant type of cell death. Several studies have shown that long non-coding RNAs (lncRNAs) can drive HCC initiation and progression. However, the prognostic value of ferroptosis-related lncRNAs in patients with HCC has not been explored comprehensively. Gene set variation analysis (GSVA) based on gene set and RNA-seq profiles obtained from public databases indicated that Ferroptosis is suppressed in HCC patients. Ferroptosis-related differentially expressed lncRNAs were screened by Pearson's test. Univariate COX regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate COX regression were performed to establish a novel five ferroptosis-related lncRNA signature in the training cohort with 60% patients, which was further verified in the testing cohort with 40% patients. Dimensionality reduction analysis, Kaplan-Meier curve, receiver operating characteristic (ROC) curve, independent prognostic analysis, and stratification analysis confirmed that our signature had a high clinical application value in predicting the overall survival of HCC patients. Compared to the clinicopathological factors and the other four published HCC prognostic signatures, the current risk model had a better predictive value. The comparison results of functional enrichment, tumor immune microenvironment, genomic heterogeneity, and drug sensitivity between the high- and low-risk groups showed that the risk score is associated with extensive genomic alterations, immunosuppressive tumor microenvironment, and clinical treatment response. Finally, cell experiments showed that silencing LNCSRLR expression inhibited the growth, proliferation, migration, and invasion of the HCC cell line. Thus, the model can function as an efficient indicator for predicting clinical prognosis and treatment of Anticancer drugs in HCC patients.