ReviewAnnals of medicine and surgery (2012)2026
Integrative bioinformatics approaches for early detection biomarkers in ovarian cancer.
Review in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Predictive and Prognostic Biomarkers in Pediatric Intussusception-A Systematic Review.Journal of clinical medicine · 2026Review
- New insight into RNA biomarkers in neuropathic pain: a clinician-neuroscientist roadmap to translational testing and treatment monitoring a clinical review.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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