Abstract B013: Integrative gene expression and network analysis reveals key regulatory modules and pathways in ovarian cancer

Abstract Ovarian cancer is a global health concern and remains one of the most lethal gynecological malignancies. Due to late diagnosis, molecular complexity, and limited therapeutic options, it leads to large numbers of deaths annually among women. Also, with lifestyle changes, sleep patterns are changing tremendously, affecting hormone regulations in the body, which are also contributing factors for ovarian cancer. We performed a comprehensive transcriptomic analysis using RNA-sequencing data to uncover critical gene expression changes and regulatory mechanisms responsible for ovarian tumorigenesis and to understand its association with circadian disruption. Using the DESeq2 package, Differential gene expression analysis was conducted, identifying a set of significantly upregulated (NINJ2, FPR3, MOM2, FGR, and STAB1) and downregulated genes (NDRG4, SLC22A5, WSB1, RHOBTB2, PEX5L, PLD5, and PDK4) in tumor samples compared to controls. To further explore gene co-expression patterns, we applied Weighted Gene Co-expression Network Analysis (WGCNA), which uncovered significant gene modules highly associated with traits of interest. Further, Ingenuity Pathway Analysis (IPA) of differentially expressed genes revealed several canonical pathways, disease and function networks, including those related to female hormones(Ephrin A Signaling (z = 2.502), IL-4 / IL-17A / Interleukin-13 Signaling (z = 2.2–2.7), circadian rhythms (PPARα/RXRα Activation (z = 3.051), Regulation of Mitotic Cell Cycle / S Phase / DNA Replication (z ≈ 3–5)) and ovarian cancer(PD-1, PD-L1 Cancer Immunotherapy Pathway (z = -2.711), Molecular Mechanisms of Cancer (z = 4.141)) Further, through Upstream regulator analysis key transcription factors and signaling molecules potentially driving tumor progression, female hormone signaling and also circadian rhythm disruption were identified. The application of the integrative approach provides new insights into the molecular understanding of ovarian cancer, identifies candidate biomarkers, and suggests potential therapeutic targets. Our findings aim at improving diagnosis and personalized treatment of ovarian cancer. Citation Format: Ritika Patial, RC. Sobti, Kashmir Singh. Integrative gene expression and network analysis reveals key regulatory modules and pathways in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B013.