Depressive illness is a significant risk factor for ovarian cancer development (OC). The underlying mechanism is unclear (perhaps involving altered neuroendocrine factors), but identifying associated alterations in gene expression of OC in depression sufferers may uncover novel factors that affect OC progression. We thus analyzed microarray gene expression data from OC tissue taken from patients diagnosed with and without depression. We identified 34 differentially expressed genes (DEGs) of depression from OC patients. Gene ontology (GO) and KEGG pathway analyses indicated several molecular pathways including complement and coagulation cascades, the hippo signaling pathway, ether lipid metabolism, the MAPK signaling pathway, and antigen processing and presentation were overrepresented among DEGs. Subsequent, protein-protein interaction (PPI) analysis revealed pathway hub proteins (FOS, EGR1, JUNB, HSPA1B, FGFR3, TRIB1, CTSB, SERPINE1), regulatory transcription factors (TFs; FOS, EGR1, JUNB) and one miRNA, miR-101-5p from TFs-miRNAs coregulatory networks. The prognostic survival analysis of the DEGs revealed CXCL12, ARL4C, NQO2 associated with worse OC survival outcomes. Protein-metabolite interaction network analysis showed that upregulated protein CHPT1 (a choline phosphotransferase) interacts with four important phospholipid synthesis and signaling metabolites. Protein-drug interaction analysis revealed SERPINE1 and PLAT proteins interact with compounds that include thrombolytic drugs, plasmin inhibitors, thiazolidinediones, NSAIDs, and hypertension treatments. Thus, we identified evidence for factors and pathways altered in OC tissue when patient depression is evident, and characterized important aspects of these OC-depression links that may be instrumental in developing new treatments for OC patients. Keywords: Ovarian cancer, Depression, Drug targets, Biomarker signatures, Differentially expressed genes, Protein-protein interaction, Protein-metabolite interaction, Protein-drug interactions, Survival analysis