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Background  Ovarian cancer is the second fatal malignancy of the female reproductive system. Based on the cancer stem cell (CSC) theory, its poor prognosis of ovarian cancer attributed to tumor recurrence caused by CSCs. A variety of cell surface-specific markers have been employed to identify ovarian cancer stem cells (OCSCs). In this study, we attempted to explore the common feature in ovarian cancer stem cells sorted by multiple approaches.    Methods  We collected the gene expression profiles of OCSCs were from 5 public cohorts and employed R software and Bioconductor packages to establish differently expressed genes (DEGs) between OCSCs and parental cells. We extracted the integrated DEGs by protein-protein interaction (PPI) network construction and explored potential treatment by the Cellminer database.    Results  We identified and integrated the DEGs of OCSCs sorted by multiple isolation approaches. Besides, we identified OCSCs share characteristics in the lipid metabolism and extracellular matrix changes. Moreover, we obtained 16 co-expressed core genes, such as  FOXQ1, MMP7, AQP5, RBM47, ETV4, NPW, SUSD2, SFRP2, IDO1, ANPEP, CXCR4, SCNN1A, SPP1  and  IFI27  (upregulated) and  SERPINE1, DUSP1, CD40,  and  IL6  (downregulated). Through correlation analysis, we screened out ten potential drugs to target the core genes.    Conclusion  Based on the comprehensive analysis of the genomic datasets with different sorting methods of OCSCs, we figured out the common driving genes to regulating OCSC and obtained ten new potential therapies for eliminating ovarian cancer stem cells. Hence, the findings of our study might have potential clinical significance.

Integrative analysis of the common genetic characteristics in ovarian cancer stem cells sorted by multiple approaches