Abstract 3382: Comparative genomic and pathway analysis of ovarian cancer, benign tumors, and normal tissues detect alterations in several metabolic programs in ovarian cancer

Abstract

Abstract Introduction: The high mortality rate of ovarian cancer is mainly due to asymptomatic early stages of the disease. Currently used detection markers lack high sensitivity and specificity and generate large number of false positives from benign and normal subjects. Therefore experimental approaches capable of distinguishing the signatures of malignancy are required to improve the current states of biomarkers. This study aims to identify genetic abnormalities in ovarian cancer that contribute to cancer progression and developments. Methodology: This study was conducted on 67 whole tissue microarrays samples processed using Affymetrix HG-Focus chip, including 18 non tumorous samples (N), 14 benign tumor samples (B), 30 malignant ovarian samples (M), 3 borderline malignant samples, and 2 ovarian cancer cell lines. Differential expression (DE) analysis was conducted through Analysis of Variance methods (ANOVA) between malignant samples and non-malignant samples (M vs. B and N). DE results were modified to False Discovery Rate (FDR < 0.05). Further pathways analysis was conducted on DE genes to identify pathways with dysregulated elements. Pathway identification was performed by enrichment analysis method based on KEGG annotated pathways. P-values were calculated using Chi-Square test (FDR<0.05). Further enrichment analyses were performed by MSigDB GSEA portal. Results: 826 DE genes were found with fold change > 2. PDGFRA, TACSTD2, ABCA8, STAR, and C7 exhibited the highest rates of fold change (more than 14 fold). Enrichment Analysis of Hallmarks reveals several hallmarks of cancer related terms including EMT, G2M, E2F targets, P53 pathways, and Apoptosis. Pathway analysis identifies 26 pathways with dysregulated components many of which are categorized as metabolic pathways including Ascorbate Metabolism, Metabolism of xenobiotics by cytochrome, Drug metabolism cytochrome P450, and Glutathione Metabolism. Gene Ontology Enrichments identify several significant enrichments including: Response to Endogenous Stimulus, Regulation of Cell Proliferation, Tissue Development, Regulation of Cell Death, and Cell Cycle Process. Conclusions: DE analysis and Pathway can reveal dysregulated metabolic programs in ovarian cancer and the results of GO, Pathway, and hallmarks enrichment analysis are consistent with mechanistic differences between malignant tumors and other types of pathology. The inclusion of benign tumors and non-malignant ovarian tissues contribute to more accurate identification of disease profile. As expected, our data support mechanisms of P53 mutations in malignancy and tumor progression. These results can be used to identify such mechanisms in more detail as the detected DE genes can be used to generate hypothesis set for detection of ovarian malignancy signatures. Citation Format: Pourya Naderi Yeganeh, Zahra Bahrani-Mostafavi, Christine Richardson, David L. Tait, M Taghi Mostafavi. Comparative genomic and pathway analysis of ovarian cancer, benign tumors, and normal tissues detect alterations in several metabolic programs in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3382. doi:10.1158/1538-7445.AM2017-3382