Integrating Circulating miRNA Profiles, Glycosylation, and Volatile Organic Compound Signatures Toward Multimodal Liquid Biopsy for Early Detection of Ovarian Cancer: A Molecular Framework

Ovarian cancer remains difficult to detect before dissemination, and individual circulating biomarkers are unlikely to capture its biological and clinical heterogeneity. Multimodal liquid biopsy offers a potential route forward, but most biomarker combinations are developed empirically, with limited consideration of the molecular relationships among the measured signals. In this Perspective, we propose that intracellular miRNA dysregulation may provide an organizing layer for integrating glycoprotein/glycan remodeling with blood-derived volatile organic compound (VOC) signatures, while corresponding circulating miRNA profiles constitute a distinct measurable biomarker layer that may only partially reflect the underlying intracellular regulatory state. The miRNA–glycosylation axis is supported by direct mechanistic evidence that selected miRNAs regulate glycogenes and pathways controlling fucosylation, sialylation, and other glycan features. By contrast, the proposed connection between miRNA dysregulation and VOC production is indirect and likely mediated through metabolic reprogramming, mitochondrial dysfunction, oxidative stress, ferroptosis, and lipid peroxidation. We evaluate the evidence supporting each biomarker domain, prioritize candidate bridge miRNAs, including members of the miR-200 family and miR-34a, and define the principal boundary conditions of the framework, including causal inference, disease specificity, histological heterogeneity, VOC source ambiguity, temporal variability, and cross-platform standardization. We further outline a translational strategy based on matched biospecimens, mechanism-aware computational integration, experimental perturbation, longitudinal analysis, and independent validation at clinically relevant specificity thresholds. The objective is not to assert a fixed causal chain, but to provide a testable, clinically oriented framework for moving multimodal ovarian cancer diagnostics from statistical combination to biologically informed integration.