Biomarkers In Gynecological Malignancies: From Multi-Omics Discovery to Machine Learning-Driven Precision Care

Gynecological malignancies, including ovarian, endometrial (uterine), and cervical cancers, represent a substantial global health burden. Cervical cancer remains one of the most common cancers among women worldwide, with over 600,000 new cases and more than 300,000 deaths annually, disproportionately affecting low- and middle-income countries. Ovarian cancer, although less common, accounts for the highest mortality among gynecological cancers due to late-stage diagnosis and a five-year survival rate below 50% in advanced disease. Endometrial cancer incidence is rising globally, particularly in high-income settings, driven by ageing populations and increasing obesity rates. Despite therapeutic advances, outcomes remain highly stage-dependent. Early detection, accurate risk stratification, and individualized treatment selection are critical unmet needs. Biomarkers, including molecular, cellular, and imaging-based, have emerged as pivotal tools in transforming cancer care, offering opportunities for screening, early diagnosis, prognosis, treatment prediction, and monitoring disease recurrence.This Special Issue aims to address the urgent need for robust, validated biomarkers that can meaningfully improve outcomes in gynecological cancers. Advances in genomics, transcriptomics, proteomics, metabolomics, and radiomics have accelerated biomarker discovery, while machine learning and artificial intelligence now enable integration of complex multi-omics and clinical datasets. However, translation into clinical practice remains limited by challenges related to validation, reproducibility, equitable access, and effective implementation. We seek to bridge discovery science with clinical application by highlighting innovative research that leverages omic technologies and computational approaches to enable precision screening, early detection, risk prediction, prognostication, and therapy selection in ovarian, uterine, and cervical cancers.This Special Issue will provide a comprehensive and evidence-based overview of biomarker discovery, validation, and clinical integration across ovarian, endometrial, and cervical malignancies. We welcome contributions addressing novel diagnostic and prognostic biomarkers, liquid biopsy approaches, multi-omics integration, machine learning applications, risk prediction models, translational studies, and biomarker-driven clinical trials. Submissions may include original research articles, clinical trials, systematic and narrative reviews, brief reports, perspectives, editorials, and letters. We particularly encourage interdisciplinary work bridging oncology, gynecology, molecular sciences, bioinformatics, and public health, with an emphasis on clinical impact, equity, and global relevance.***This Research Topic is currently not considering Case Reports.Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Kelechi Njoku, Claire Henry Source: https://www.frontiersin.org/research-topics/78653/biomarkers-in-gynecological-malignancies-from-multi-omics-discovery-to-machine-learning-driven-precision-care