Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature for Hepatocellular Carcinoma

Abstract

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial–mesenchymal transition (EMT) is crucial for cancer progression and associates with a worse prognosis. Thus, we aimed to construct an EMT-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We built an EMT-related lncRNA risk signature in the training set by using Cox regression and LASSO regression based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. Cox regression was performed to explore whether the signature could be used as an independent factor. A nomogram was built involving the risk score and clinicopathological features. Furthermore, we explored the biological functions and immune states in two groups.Results: 12 EMT-related lncRNAs were obtained for constructing the prognosis model in HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse survival than low-risk group. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the validation group. The nomogram was built and could accurately predict survival of HCC patients. GSEA results showed that in high-risk group cancer-related pathways were enriched, and exhibited more cell division activity suggested by Gene Ontology (GO) analysis.Conclusions: We established a novel EMT-related prognostic risk signature including 12 lncRNAs and constructed a nomogram to predict the prognosis in HCC patients, which may improve prognostic predictive accuracy for HCC patients and guide the individualized treatment methods for the patients with HCC.