Identification of potential biomarkers in subtypes of epithelial ovarian cancer

Abstract

Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer. Due to the lack of specific symptoms, ~80% of epithelial ovarian cancer is diagnosed at an advanced stage and often metastasize to the distant organ. Epithelial ovarian cancer is a heterogeneous disease that is classified into four major histological subtypes namely, serous carcinoma (SC), endometrioid carcinoma (EC), mucinous carcinoma (MC), and clear cell carcinoma (CCC).Ovarian cancer treatment is complicated due to the heterogeneity of the tumors. Patients with different subtypes respond differently to the same treatment and also have different prognoses. This diversity extends to various clinical outcomes of the disease. Thus, identifying new reliable potential biomarkers irrespective of their subtypes is an urgent need for the diagnosis and prognosis of epithelial ovarian cancer. In this study, we performed comparative gene expression analysis for identifying potential biomarkers in four histological subtypes of epithelial ovarian cancer (EOC) that include serous, endometrioid, mucinous, and clear cell carcinomas. Differentially expressed genes (DEGs) between cancerous and normal tissue samples were identified by considering the criteria of absolute logarithmic fold change |log2fc|>1 and adjusted p (padj) value<0.05. Pathway enrichment analysis of the DEGs showed that pathways in cancer, PI3K-AKT signaling pathway, RAP1 signaling pathway, cell cycle, cell adhesion molecules, and proteoglycans in cancer were common among the selected cancer subtypes. Further, we constructed the co-expression network of DEGs and identified 15 candidate genes. Finally, based on the survival analysis of the candidate genes, a total of nine genes namely ASPM, CDCA8, CENPM, CEP55, HMMR, RACGAP1, TPX2, UBE2C, and ZWINT with significant prognostic value was proposed as the potential biomarker.