About: Quiescent stem cell marker genes in glioma gene networks are sufficient to distinguish between normal and glioblastoma (GBM) samples

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Quiescent stem cell marker genes in glioma gene networks are sufficient to distinguish between normal and glioblastoma (GBM) samples
Creator	Mukherjee, Shradha
source	PMC
abstract	Grade 4 glioma or GBM has poor prognosis and is the most aggressive grade of glioma. Accurate diagnosis and classification of tumor grade is a critical determinant for development of treatment pathway. Extensive genomic sequencing of gliomas, different cell types, brain tissue regions and advances in bioinformatics algorithms, have presented an opportunity to identify molecular markers that can complement existing histology and imaging methods used to diagnose and classify gliomas. ‘Cancer stem cell theory’ purports that a minor population of stem cells among the heterogeneous population of different cell types in the tumor, drive tumor growth and resistance to therapies. However, characterization of stem cell states in GBM and ability of stem cell state signature genes to serve as diagnostic or prognostic molecular markers are unknown. In this work, two different network construction algorithms, Weighted correlation network analysis (WGCNA) and Multiscale Clustering of Geometric Network (MEGENA), were applied on publicly available glioma, control brain and stem cell gene expression RNA-seq datasets, to identify gene network regulatory modules associated with GBM. Both gene network algorithms identified consensus or equivalent modules, HuAgeGBsplit_18 (WGCNA) and c1_HuAgeGBsplit_32/193 (MEGENA), significantly associated with GBM. Characterization of HuAgeGBsplit_18 (WGCNA) and c1_HuAgeGBsplit_32/193 (MEGENA) modules showed significant enrichment of rodent quiescent stem cell marker genes (GSE70696_QNPbyTAP). A logistic regression model built with eight of these quiescent stem cell marker genes (GSE70696_QNPbyTAP) was sufficient to distinguish between control and GBM samples. This study demonstrates that GBM associated gene regulatory modules are characterized by diagnostic quiescent stem cell marker genes, which may potentially be used clinically as diagnostic markers and therapeutic targets in GBM.
has issue date	2020-07-02(xsd:dateTime)
bibo:doi	10.1038/s41598-020-67753-5
bibo:pmid	32616845
has license	no-cc
sha1sum (hex)	f4b1a85ef5cef5d8512e331283984d6421d3d942
schema:url	https://doi.org/10.1038/s41598-020-67753-5
resource representing a document's title	Quiescent stem cell marker genes in glioma gene networks are sufficient to distinguish between normal and glioblastoma (GBM) samples
has PubMed Central identifier	PMC7363816
has PubMed identifier	32616845
schema:publication	Sci Rep
resource representing a document's body	covid:f4b1a85ef5cef5d8512e331283984d6421d3d942#body_text
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