Bioinformatics analysis and verification of molecular targets in ovarian cancer stem-like cells.

backgroundEpithelial 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.

methodsTwo 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 log

resultsA 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.

conclusionsThe 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.