A Bioinformatics Analysis: ZFHX4 is Associated with Metastasis and Poor Survival in Ovarian Cancer

Overview

Journal J Ovarian Res

Publisher Biomed Central

Specialties Gynecology & Obstetrics
Reproductive Medicine

Date 2022 Aug 1

PMID 35915456

Authors

Shuai Zong

Ping-Ping Xu

Yin-Hai Xu

Yi Guo

Affiliations

Soon will be listed here.

Abstract

Background: Metastasis was the major cause of the high mortality in ovarian cancer. Although some mechanisms of metastasis in ovarian cancer were proposed, few have been targeted in the clinical practice. In the study, we aimed to identify novel genes contributing to metastasis and poor clinical outcome in ovarian cancer from bioinformatics databases.

Methods: Studies collecting matched primary tumors and metastases from ovarian cancer patients were searched in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by software R language. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for the DEGs were implemented by Metascape. Venn diagram was plotted to present overlapping DEGs. The associations between the overlapping DEGs and prognosis were tested by Cox proportional hazard regression model using a cohort of ovarian cancer patients from the TCGA database. Genes affecting patients' outcomes significantly were served as hub genes. The mechanisms of the hub genes in promoting ovarian cancer metastasis were then predicted by R software.

Results: Two gene expression profiles (GSE30587 and GSE73168) met the inclusion criteria and were finally analyzed. A total of 259 genes were significantly differentially expressed in GSE30587, whereas 712 genes were in GSE73168. In GSE30587, DEGs were mainly involved in extracellular matrix (ECM) organization; For GSE73168, most of DEGs showed ion trans-membrane transport activity. There were 9 overlapping genes between the two datasets (RUNX2, FABP4, CLDN20, SVEP1, FAM169A, PGM5, ZFHX4, DCN and TAS2R50). ZFHX4 was proved to be an independent adverse prognostic factor for ovarian cancer patients (HR = 1.44, 95%CI: 1.13-1.83, p = 0.003). Mechanistically, ZFHX4 was positively significantly correlated with epithelial-mesenchymal transition (EMT) markers (r = 0.54, p = 2.59 × 10) and ECM-related genes (r = 0.52, p = 2.86 × 10).

Conclusions: ZFHX4 might promote metastasis in ovarian cancer by regulating EMT and reprogramming ECM. For clinical applications, ZFHX4 was expected to be a prognostic biomarker for ovarian cancer metastasis.

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