Multi-omic biomarker detection in ovarian cancer

Area of Science:

• Gynecologic Oncology
• Molecular Biology
• Biomarker Discovery

Background:

• Ovarian cancer is a lethal malignancy often diagnosed late, lacking reliable early detection and therapeutic stratification biomarkers.
• High-throughput technologies enable multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics, epigenomics) for comprehensive molecular profiling.

Purpose of the Study:

• To review progress in applying multi-omics strategies for ovarian cancer biomarker discovery.
• To highlight how integrative analyses identify novel diagnostic, prognostic, and predictive candidates.
• To discuss challenges and future directions for clinical implementation.

Main Methods:

• Narrative review synthesizing current research on multi-omics in ovarian cancer.
• Discussion of methodological frameworks, computational pipelines, and data harmonization.
• Emphasis on systems biology and machine learning for biomarker validation.

Main Results:

• Integrative multi-omics analyses uncover biomarkers beyond single-omics limitations.
• Noncoding RNAs, protein signatures, and metabolic alterations are promising biomarker classes.
• Potential for multiparameter biomarker panels and precision medicine applications.

Conclusions:

• Multi-omics approaches are crucial for advancing biomarker identification in ovarian cancer.
• Bridging molecular complexity with translational utility can significantly improve patient outcomes.
• Future directions include clinical implementation of advanced biomarker panels.