Ovarian cancer (OC) remains the most fatal disease of gynaecologic malignant tumours. The neovasculature in the tumour microenvironment principally comprises endothelial cells. Haematogenous cancer metastases are significantly impacted by tumour neovascularisation, which predominantly depends on the tumour-derived endothelial vasculogenesis. There is an urgent need for biomarkers for the diagnosis, prognosis and prediction of drug response. Endothelial cells play a key role in angiogenesis and other forms of tumour vascularisation. Subtypes of circulating endothelial cells may provide interesting non-invasive biomarkers of advanced OC that might have the potential to be included in clinical analysis for patients’ stratification and therapeutic management. In this review, we summarise the reported studies on circulating endothelial subtypes in OC, detailing their isolation methods as well as their potential diagnostic, prognostic, predictive and therapeutic utility for clinical application. We highlight key biomarkers for the identification of circulating endothelial cell subtypes and their targets for therapies and critically point out future challenges.

Background: Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to delayed diagnosis and limited effectiveness of current biomarkers. Identifying candidate biomarkers through integrated gene expression analysis may enhance understanding of OC biology and support future diagnostic and prognostic investigations. Methods: Four microarray datasets were retrieved from the Gene Expression Omnibus and analyzed using the limma package in R to identify differentially expressed genes (DEGs) through a bidirectional filter (|log ₂ FC| ≥ 1.2). Overlapping DEGs across all four datasets were subjected to Gene Ontology and KEGG pathway enrichment analyses. Protein-protein interaction networks were constructed using the STRING database, and hub genes were identified by consensus across three centrality algorithms in Cytoscape. Prognostic significance was evaluated using Kaplan-Meier survival analysis via GEPIA2; diagnostic performance was assessed using Youden Index-based ROC analysis; and external validation was performed using an independent dataset. Results: A total of 209 overlapping DEGs were identified, yielding 35 hub genes. Functional enrichment analysis implicated cell cycle dysregulation, metabolic reprogramming, and extracellular matrix remodeling as central biological processes. TRIP13, FGF13, and LYVE1 were significantly associated with overall survival, while ALDH1A1, GATA6, and WNT5A were associated with disease-free survival. KDR demonstrated the highest diagnostic accuracy, and external validation confirmed the generalizability of the diagnostic findings. Conclusion: This study identifies a panel of candidate diagnostic and prognostic biomarkers in ovarian cancer, providing a molecular framework for future experimental validation and translational investigation.