Search

Background Ovarian cancer is a gynecologic malignancy with poor prognosis. Present prognostic models for ovarian cancer focus on clinico-pathological parameters, quantifiable prognostic biomarkers at molecular level are urgently needed. Platelets contribute to ovarian cancer progression, thus we aimed to search for new predictors in platelet-treated ovarian cancer cells. Methods Microarrays analysis was done with platelet-treated SKOV3 cells and controls (4 replicates in each group). Studies on ovarian cancer cells co-incubated with platelets were searched in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by R language. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were conducted using online software Metascape. Venn diagram was generated to present common DEGs. Candidate genes were determined by protein-protein interaction (PPI) network, Cox proportional hazards model and Kaplan-Meier analysis. The functions of candidate genes were predicted using data from TCGA by R software, and validated by in vitro experiments. Results One dataset (GSE155546) met the inclusion criteria and were analyzed with our microarray data. A total of 4553 mRNAs were differentially expressed between the two groups from our own data, whereas 260 genes exhibited significantly differential expression in GSE155546. DEGs involved in extracellular matrix (ECM) organization and system development were found in both datasets. There were 88 overlapping genes between the two datasets. TGFBI was proved to be an independent adverse factor for ovarian cancer. In addition, high expression of AFT3 and CXCL1 showed worse prognosis in ovarian cancer, while IGFBP7 behaved as a protective predictor. Only increased expression of TGFBI led to significant decrease of overall survival (OS), progression-free survival (PFS) and post-progression survival (PPS), therefore TGFBI was selected as the candidate gene. Functionally, TGFBI was predicted to be significantly correlated with epithelial mesenchymal transition (EMT) markers, degradation of ECM, collagen formation and ECM-related genes. In vitro experiments demonstrated that TGFBI could affect the migration and invasiveness of ovarian cancer cells by regulation E-cadherin, Vimentin, N-cadherin and MMP2. Conclusion We found TGFBI as a novel prognostic indicator using platelet-treated ovarian cancer model. Functionally, TGFBI could promote ovarian cancer progression by EMT induction and ECM remodeling.

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.