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 × 10−29) and ECM-related genes (r = 0.52, p = 2.86 × 10−27). 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.

Background Ovarian cancer is a highly malignant gynecological tumor with high mortality worldwide. The poor prognosis mainly results from extensive metastasis at the time of presentation. Although some mechanisms of metastasis in ovarian cancer were proposed, few have been used in the clinical practice. In the study, we aimed to identify novel genes and pathways contributing to metastatic ovarian cancer from bioinformatics databases, thus providing more potential candidates for clinical applications. Methods Studies collecting matched primary tumors and metastatic lesions from ovarian cancer patients were searched in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between groups were screened by software R language. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for the DEGs were implemented by using online software 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 from the TCGA database. Genes affecting patients’ outcome significantly were served as hub genes. The mechanisms of the hub genes in promoting ovarian cancer metastasis were predicted using samples from TCGA database by R software. Results Two gene expression profiles (GSE30587 and GSE73168) met the inclusion criteria and were finally analyzed. A total of 362 genes were significantly differentially expressed in GSE30587, whereas 653 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. Both of the DEG sets were related to system development. There were 8 overlapping genes between the two datasets (DCN, SVEP1, MS4A6A, FABP4, APBB1IP, VCAM1 and ZFHX4), and ZFHX4 was proved to be an independent adverse prognostic factor for ovarian cancer patients (HR = 1.40, 95%CI: 1.11–1.75, p = 0.004). Mechanistically, ZFHX4 was positively significantly correlated with epithelial-mesenchymal transition (EMT) markers (r = 0.54, p = 2.59×10− 29) and ECM-related genes (r = 0.52, p = 2.86×10− 27). Conclusions ZFHX4 could promote metastasis in ovarian cancer by regulating EMT and reprogramming matrisome, which rendered ZFHX4 as a potential therapeutic target and prognostic biomarker for ovarian cancer metastasis.