Bioinformatics analysis and verification of molecular targets in ovarian cancer stem-like cells. Issue 9 (September 2020)

• Record Type: Journal Article
• Authors: Behera, Abhijeet; Ashraf, Rahail; Srivastava, Amit Kumar; Kumar, Sanjay
• Is Part Of: Heliyon. Volume 6:Issue 9(2020)
• Publication Date: 2020-09
• Subjects: Cell biology -- Biochemistry -- Cancer research -- Data mining -- Cancer stem-like cells -- Differential gene expression -- Carboplatin -- Ovarian cancer -- Interferon-alpha/beta signaling; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2020.e04820
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Background: Epithelial ovarian cancer (EOC) is a lethal and aggressive gynecological malignancy. Despite recent advances, existing therapies are challenged by a high relapse rate, eventually resulting in disease recurrence and chemoresistance. Emerging evidence indicates that a subpopulation of cells known as cancer stem-like cells (CSLCs) exists with non-tumorigenic cancer cells (non-CSCs) within a bulk tumor and is thought to be responsible for tumor recurrence and drug-resistance. Therefore, identifying the molecular drivers for cancer stem cells (CSCs) is critical for the development of novel therapeutic strategies for the treatment of EOC. Methods: Two gene datasets were downloaded from the Gene Expression Omnibus (GEO) database based on our search criteria. Differentially expressed genes (DEGs) in both datasets were obtained by the GEO2R web tool. Based on log2 (fold change) >2, the top thirteen up-regulated genes and log2 (fold change) < -1.5 top thirteen down-regulated genes were selected, and the association between their expressions and overall survival was analyzed by OncoLnc web tool. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways analysis, and protein-protein interaction (PPI) networks were performed for all the common DEGs found in both datasets. SK-OV-3 cells were cultured in an adherent culture medium and spheroids were generated in suspension culture with CSCs specific medium. RNA from both cell population was extracted to validate the selected DEGs expression by q-PCR. Growth inhibition assay was performed in SK-OV-3 cells after carboplatin treatment. Results: A total of 200 DEGs, 117 up-regulated and 83 down-regulated genes were commonly identified in both datasets. Analysis of pathways and enrichment tests indicated that the extracellular matrix part, cell proliferation, tissue development, and molecular function regulation were enriched in CSCs. Biological pathways such as interferon-alpha/beta signaling, molecules associated with elastic fibers, and synthesis of bile acids and bile salts were significantly enriched in CSCs. Among the top 13 up-regulated and down-regulated genes, MMP1 and PPFIBP1 expression were associated with overall survival. Higher expression of ADM, CXCR4, LGR5, and PTGS2 in carboplatin treated SK-OV-3 cells indicate a potential role in drug resistance. Conclusions: The molecular signature and signaling pathways enriched in ovarian CSCs were identified by bioinformatics analysis. This analysis could provide further research ideas to find the new mechanism and novel potential therapeutic targets for ovarian CSCs. Abstract : Cell biology; Biochemistry; Cancer research; Data mining; Cancer stem-like cells; Differential gene expression; Carboplatin; Ovarian cancer; Interferon-alpha/beta signaling