ArticleScientific reports2020
Quiescent stem cell marker genes in glioma gene networks are sufficient to distinguish between normal and glioblastoma (GBM) samples.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 17 citations in OpenAlex.
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- Tumor heterogeneity and resistance in glioblastoma: the role of stem cells.Apoptosis : an international journal on programmed cell death · 2025Review
- Identification of Key Molecular Pathways and Associated Genes as Targets to Overcome Radiotherapy Resistance Using a Combination of Radiotherapy and Immunotherapy in Glioma Patients.International journal of molecular sciences · 2024Article
- A paracrine circuit of IL-1β/IL-1R1 between myeloid and tumor cells drives genotype-dependent glioblastoma progression.The Journal of clinical investigation · 2023Article
- Characterization of EGFR-reprogrammable temozolomide-resistant cells in a model of glioblastoma.Cell death discovery · 2022Article
- Adapt to Persist: Glioblastoma Microenvironment and Epigenetic Regulation on Cell Plasticity.Biology · 2022Review
- Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data.Nucleic acids research · 2021Article
- WNT Signaling as a Therapeutic Target for Glioblastoma.International journal of molecular sciences · 2021Review
- Cervical Cancer Stem-Like Cell Transcriptome Profiles Predict Response to Chemoradiotherapy.Frontiers in oncology · 2021Article
- Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset.International journal of general medicine · 2021Article
- Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis.Cancer management and research · 2021Article
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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.
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