Computational identification of specific genes for glioblastoma stem-like cells identity.

ABSTRACT: Glioblastoma, the most malignant brain cancer, contains self-renewing, stem-like cells that sustain tumor growth and therapeutic resistance. Identifying genes promoting stem-like cell differentiation might unveil targets for novel treatments. To detect them, here we apply SWIM - a software able to unveil genes (named switch genes) involved in drastic changes of cell phenotype - to public datasets of gene expression profiles from human glioblastoma cells. By analyzing matched pairs of stem-like and differentiated glioblastoma cells, SWIM identified 336 switch genes, potentially involved in the transition from stem-like to differentiated state. A subset of them was significantly related to focal adhesion and extracellular matrix and strongly down-regulated in stem-like cells, suggesting that

SUBMITTER: Fiscon G 

PROVIDER: S-EPMC5958093 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature