Investigation of Hippo pathway-related prognostic lncRNAs and molecular subtypes in liver hepatocellular carcinoma

Abstract Purpose: This study aimed to investigate Hippopathway-related prognostic long noncoding RNAs (lncRNAs) and their prognostic value in liver hepatocellular carcinoma (LIHC). Methods: Expression and clinical data regarding LIHC were acquired from The Cancer Genome Atlas (TCGA) and European Bioinformatics Institute (EBI) array databases. Hippo pathway-related lncRNAs and their prognostic value were revealed, followed by molecular subtype investigations. Differences in survival, clinical characteristics, immune cell infiltration, and checkpoint expression between the subtypes were explored. LASSO regression was used to determine the most valuable prognostic lncRNAs, followed by the establishment of a prognostic model. Survival and differential expression analyses were conducted between two groups (high- and low-risk). Results: A total of 313 Hippo pathway-related lncRNAs were identified from LIHC, of which 88 were associated with prognosis, and two molecular subtypes were identified based on their expression patterns. These two subtypes showed significant differences in overall survival, pathological stage and grade, vascular invasion, infiltration abundance of seven immune cells, and expression of several checkpoints, such as CTLA-4 and PD-1/L1 (P<0.05). LASSO regression identified the six most valuable independent prognostic lncRNAs for establishing a prognosis risk model. Risk scores calculated by the risk model assigned patients into two risk groups with an AUC of 0.913 and 0.731, respectively, indicating that the high-risk group had poor survival. The risk score had an independent prognostic valuewith an HR of 2.198. In total, 3007 genes were dysregulated between the two risk groups, and the expression of most genes was elevated in the high-risk group, involving the cell cycle and pathways in cancers. Conclusion: Hippo pathway-related lncRNAs could stratify patients for personalized treatment and predict the prognosis of patients with LIHC.