Identification of a Novel Epithelial-to-Mesenchymal-related Gene Signature in Predicting Survival of Patients with Hepatocellular Carcinoma

(E-pub Abstract Ahead of Print)

Author(s): Simeng Xiao, Junjie Hu, Na Hu, Lei Sheng, Hui Rao, Guohua Zheng*

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

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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: We conducted 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. An EMT-related gene signature was constructed by 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 a 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 in 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 EMT-related gene signature reached higher area under 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

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Article Details

(E-pub Abstract Ahead of Print)
DOI: 10.2174/1386207324666210303093629
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