Integrative bioinformatics approaches for early detection biomarkers in ovarian cancer

Ovarian cancer remains a major clinical challenge, largely because most patients present with advanced-stage disease. Early detection is essential for improving outcomes, yet current biomarkers such as CA-125 and HE4 have limited sensitivity in early-stage tumors. Advances in bioinformatics and multi-omics research offer new opportunities to identify reliable early detection biomarkers, but their clinical relevance is often obscured by highly technical descriptions. This review summarizes integrative bioinformatics approaches used in early detection biomarker discovery for ovarian cancer, with a specific focus on presenting these methods in clinically meaningful terms. A narrative review framework was used to examine current multi-omics datasets, analytic strategies, and validation approaches. The description of data preprocessing, quality control, and integration methods was revised to emphasize clinical implications - such as reliability, diagnostic accuracy, and translational potential - rather than technical processes. Integrative analysis of genomics, transcriptomics, proteomics, and epigenetic data reveals several promising biomarker candidates that may allow earlier recognition of ovarian cancer. Simplified and clinically oriented explanations are provided for multi-omics integration strategies, supported by a conceptual figure to enhance understanding. Across studies, combined biomarker panels consistently outperform single-marker approaches and may support earlier detection when interpreted in a clinical context. Integrative bioinformatics offers important opportunities for identifying clinically meaningful early detection biomarkers in ovarian cancer. By presenting these methods in a more accessible and clinically focused manner, this review supports improved communication between researchers and clinicians and highlights pathways through which multi-omics discoveries may be translated into practical diagnostic tools.