Abstract:
High-grade serous ovarian carcinoma (HGSOC) represents the most lethal gynecological malignancy, predominantly diagnosed at advanced metastatic stages and characterized by recurrent platinum chemoresistance. In this study, we developed an integrative multi-omics computational pipeline combining multi-cohort transcriptomic profiling and interactome network topology to discover and prioritize candidate diagnostic and prognostic biomarkers. Microarray datasets (GSE14407 and GSE18520) were normalized using the Robust Multi-array Average (RMA) algorithm. Differential gene expression analysis via Limma identified 501 statistically significant consensus DEGs, comprising 168 upregulated and 333 downregulated genes (|log2FC| > 1, adjusted P < 0.05). Functional enrichment analysis using Metascape demonstrated significant overrepresentation in signaling by Rho GTPases, actin cytoskeleton reorganization, and the APC-IQGAP1-Rac1 macromolecular complex. Interactome reconstruction via STRING and CytoHubba topological modeling isolated RAC1 as the central enzymatic master hub with highest Maximal Clique Centrality (MCC) and bottleneck degree. Systematic literature interrogation prioritized RAC1 as the primary oncogenic driver executing lamellipodial extension, epithelial-mesenchymal transition (EMT), and peritoneal mesothelial clearance. Conversely, SMAD4 was prioritized as the definitive cell-autonomous tumor suppressor, whose suppression in ovarian cancer stem cells unleashes unchecked self-renewal and invasive dissemination. In conclusion, our multi-omics framework establishes the RAC1-High / SMAD4-Low signaling axis as a clinically relevant candidate biomarker signature in HGSOC, establishing a validated molecular foundation for downstream TCGA survival validation and in vitro experimental verification.
KEYWORDS:
Ovarian Cancer, Multi-Omics Integration, Transcriptomics, Interactome Topology, RAC1, SMAD4, Biomarker Discovery.
