Identification And Validation Of Ephx2 As A Prognostic Biomarker In Hepatocellular Carcinoma

MOLECULAR MEDICINE REPORTS(2021)

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摘要
Hepatocellular carcinoma (HCC) is one of the most common types of cancer, which is associated with a poor prognosis. It is necessary to identify novel prognostic biomarkers and therapeutic targets to improve the survival of patients with HCC. In the present study, a seven-gene signature associated with HCC progression was identified using weighted gene co-expression network analysis and least absolute shrinkage and selection operator, and its prognostic prediction value was confirmed in The Cancer Genome Atlas-liver HCC and International Cancer Genome Consortium liver cancer-RIKEN, Japan cohorts. Subsequently, a rarely reported gene, epoxide hydrolase 2 (EPHX2), was selected for further validation. Downregulation of EPHX2 in HCC was revealed using multiple expression datasets. Furthermore, reduced expression of EPHX2 was confirmed in HCC tissue samples and cell lines using reverse transcription-quantitative polymerase chain reaction and western blotting. Additionally, Kaplan-Meier survival curves indicated that patients with higher EPHX2 expression exhibited better prognosis, and clinicopathological analysis also revealed elevated EPHX2 levels in patients with early-stage HCC. Notably, EPHX2 was identified as an independent prognostic biomarker for overall survival of patients with HCC. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis and gene set enrichment analysis were performed to elucidate the functions of EPHX2. The results suggested that EPHX2 expression was closely associated with metabolic reprogramming. Finally, the prognostic value of EPHX2 was evaluated using HCC tissue microarrays. In conclusion, downregulation of EPHX2 was significantly associated with the development of HCC; therefore, EPHX2 may be considered a putative therapeutic candidate for the targeted treatment of HCC.
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关键词
epoxide hydrolase 2, hepatocellular carcinoma, weighted gene co-expression network analysis, tissue microarrays, prognosis
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