Algorithmic reconstruction of glioblastoma network complexity.

ABSTRACT: Glioblastoma is a complex disease that is difficult to treat. Network and data science offer alternative approaches to classical bioinformatics pipelines to study gene expression patterns from single-cell datasets, helping to distinguish genes associated with the control of differentiation and aggression. To identify the key molecular regulators of the networks driving glioblastoma/GSC and predict their cell fate dynamics, we applied a host of data theoretic techniques to gene expression patterns from pediatric and adult glioblastoma, and adult glioma-derived stem cells (GSCs). We identified eight transcription factors (OLIG1/2, TAZ, GATA2, FOXG1, SOX6, SATB2, and YY1) and four signaling genes (ATL3, MTSS1, EMP1, and TPT1) as coordinators of cell state transitions and, thus, clinically tar

SUBMITTER: Uthamacumaran A 

PROVIDER: S-EPMC9036113 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature