Cuevas Díaz Durán, RaquelCampa Carranza, Jocelyn Nikita2021-08-192021-08-1920192019Campa Carranza, J.N. (2020). 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 (Tesis de Maestría / master Thesis). Instituto Tecnológico y de Estudios Superiores de Monterrey. Monterrey, Nuevo León, México. Recuperado de: https://hdl.handle.net/11285/637587https://hdl.handle.net/11285/637587359186The 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.TextoengopenAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0MEDICINA Y CIENCIAS DE LA SALUD::CIENCIAS MÉDICAS::BIOLOGÍA HUMANA::GENÉTICA BIOQUÍMICAScienceTesis de maestríahttps://orcid.org/0000-0002-0523-1970stem cellsprominin-1CD133differentially expressed genescancer stem cellsmeta-analysisfunctional relationshipsco-expression networkbioinformatics923850