ArticleScientific reports2018
Computational identification of specific genes for glioblastoma stem-like cells identity.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 58 citations in OpenAlex.
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- Identification of fibronectin 1 (FN1) and complement component 3 (C3) as immune infiltration-related biomarkers for diabetic nephropathy using integrated bioinformatic analysis.Bioengineered · 2021Article
- MEOX2 Transcription Factor Is Involved in Survival and Adhesion of Glioma Stem-like Cells.Cancers · 2021Article
- Improved supervised prediction of aging-related genes via weighted dynamic network analysis.BMC bioinformatics · 2021Article
- In silico drug repurposing in COVID-19: A network-based analysis.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2021Article
- The role of FOSL1 in stem-like cell reprogramming processes.Scientific reports · 2021Article
- Network Analysis Identifies Sex-Specific Gene Expression Changes in Blood of Amyotrophic Lateral Sclerosis Patients.International journal of molecular sciences · 2021Article
- The risk of pancreatic adenocarcinoma following SARS-CoV family infection.Scientific reports · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 they may promote differentiation and restrain tumor growth. Their expression in differentiated cells strongly correlated with the down-regulation of transcription factors like OLIG2, POU3F2, SALL2, SOX2, capable of reprogramming differentiated glioblastoma cells into stem-like cells. These findings were corroborated by the analysis of expression profiles from glioblastoma stem-like cell lines, the corresponding primary tumors, and conventional glioma cell lines. Switch genes represent a distinguishing feature of stem-like cells and we are persuaded that they may reveal novel potential therapeutic targets worthy of further investigation.
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