Multi-omic biomarker detection in ovarian cancer
Ovarian cancer remains one of the most lethal gynecologic malignancies, largely because of late-stage diagnosis and the absence of reliable biomarkers for early detection and therapeutic stratification. Recent advances in high-throughput technologies have enabled multi-omics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics to elucidate the comprehensive molecular landscape of ovarian cancer. This narrative review synthesizes current progress in applying multi-omics strategies to biomarker discovery, highlighting how integrative analyses uncover novel diagnostic, prognostic, and predictive candidates beyond the limitations of single-omics studies. We discuss methodological frameworks, computational pipelines and translational challenges in harmonizing heterogeneous datasets, as well as the potential of systems biology and machine learning to improve biomarker validation. Particular emphasis is placed on the identification of noncoding RNAs, protein signatures, and metabolic alterations as promising biomarker classes. Finally, we outline future directions for clinical implementation, including the development of multiparameter biomarker panels and precision medicine applications. By bridging molecular complexity with translational utility, multi-omics approaches hold transformative potential to advance biomarker identification and improve patient outcomes in ovarian cancer.