Abstract
Background: Epithelial-mesenchymal transformation (EMT) promotes cancer metastasis, including hepatocellular carcinoma. Therefore, EMT-related gene signature was explored.
Objective: The present study was designed to develop an EMT-related gene signature for predicting the prognosis of patients with hepatocellular carcinoma..
Methods: An integrated gene expression analysis based on tumor data of the patients with hepatocellular carcinoma from The Cancer Genome Atlas (TCGA), HCCDB18, and GSE14520 dataset was conducted. An EMT-related gene signature was constructed by the least absolute shrinkage and selection operator (LASSO) and COX regression analysis of univariate and multivariate survival.
Results: A 3-EMT gene signature was developed and validated based on gene expression profiles of hepatocellular carcinoma from three microarray platforms. Patients with a high-risk score had significantly worse overall survival (OS) than those with low-risk scores. The EMT-related gene signature showed a high performance in accurately predicting prognosis and examining the clinical characteristics and immune score analysis. Univariate and multivariate Cox regression analyses confirmed that the EMT-related gene signature was an independent prognostic factor for predicting survival in hepatocellular carcinoma patients. Compared with the existing models, our EMTrelated gene signature reached a higher area under the curve (AUC).
Conclusion: Our findings provide novel insight into understanding EMT and help identify hepatocellular carcinoma patients with poor prognosis.
Keywords: Hepatocellular carcinoma, epithelial-mesenchymal transformation, signature, prognosis, bioinformatics, liver cancer.
Graphical Abstract
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