Transcriptomic meta-analysis of sorted CD133+ stem cells and their analogues in cancer yields a set of common differentially expressed genes and overrepresented functional categories

Abstract

The use of stem cells has been exploited for their potential application in regenerative medicine due to their properties of self-renewal, proliferation, differentiation, and immunomodulation. The isolation of primitive stem cells focuses on the presence of surface biomarkers, prominin-1/CD133 among them, on account of the potential therapeutic applications that have been reported for CD133+ stem cells in preclinical studies. However, CD133 is also one of the most prominent and commonly reported surface biomarkers for cancer stem cells (CSCs). Prominin-1 has also been associated with proliferation, cell survival, and autophagy in precursor and mature cells. Accordingly, prominin-1 appears to be a good candidate for targeting but its biological implication remains to be further determined. Here, we made use of publicly available gene expression data of sorted CD133+ cells from normal and cancerous sources to perform an integrated meta-analysis to identify a set of differentially expressed (DE) genes and attempt to find functional relationships among them. A subset of statistically significant genes was further validated in silico. The identification of representative genes and a co-expression network had the aim to better elucidate the underlying biological function of prominin-1/CD133. The present project melds biomedical knowledge with the use of bioinformatics, exploiting the availability of large databases of genomic information. Moreover, our methodology is a cost-effective approach to extract knowledge from biological data in a fast and accurate way.