Network-Based Bioinformatics Analysis Reveals Adipocytokine Signaling Pathway-Related RNA-Binding Proteins as Potential Candidate Biomarkers in Ovarian Cancer

Abstract

Background: Ovarian cancer remains one of the most lethal gynecological malignancies, and identifying novel molecular biomarkers is essential for improving diagnosis and treatment. Adipocytokine signaling has been implicated in cancer progression; however, its regulatory mechanisms in ovarian cancer remain poorly understood. Objectives: This study aimed to identify genes related to the adipocytokine signaling pathway and their associated RNA-binding proteins (RBPs) in ovarian cancer through an integrated bioinformatics approach. Methods: Differentially expressed genes (DEGs) were identified using the GEPIA3 database and intersected with adipocytokine signaling pathway genes retrieved from MSigDB to obtain adipocytokine signaling-related DEGs (AD-DEGs). Regulatory networks between AD-DEGs and RBPs were constructed using starBase, STRING, and Cytoscape. Genomic alterations, immune infiltration, and drug sensitivity were analyzed using the Gene Set Cancer Analysis (GSCA) platform. Results: Five AD-DEGs (AKT3, NPY, G6PC2, TNF, and IRS4) and 10 hub AD-RBPs were identified. Most hub AD-RBPs showed strong correlations between copy number variation and gene expression, whereas somatic mutations were infrequent. Immune infiltration analysis demonstrated a significant negative association between AD-DEGs and B-cell infiltration. Drug sensitivity analysis showed that both AD-DEGs and hub AD-RBPs were significantly associated with responses to multiple anticancer agents, with ELAVL1, HNRNPC, HNRNPA1, PTBP1, and TARDBP showing the strongest associations. Conclusions: These findings suggest that adipocytokine signaling-related genes and their regulatory RBPs may contribute to ovarian cancer progression via metabolic and post-transcriptional mechanisms. The identified candidates may serve as biomarkers and potential therapeutic targets, providing a foundation for future experimental validation and precision medicine strategies in ovarian cancer.