ArticleBMC systems biology2016
A novel integrated gene coexpression analysis approach reveals a prognostic three-transcription-factor signature for glioma molecular subtypes.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Identification of Prognostic Values of Neutrophil Extracellular Traps-Related Genes in Glioma Based on Bioinformatics.Immunity, inflammation and disease · 2026Article
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
- Analyses of hypoxia-related risk factors and clinical relevance in breast cancer.Frontiers in oncology · 2024Article
- A new border for circadian rhythm gene NFIL3 in diverse fields of cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Review
- Review
- Differential gene regulatory networks in development and disease.Cellular and molecular life sciences : CMLS · 2018Review
- Robust method for identification of prognostic gene signatures from gene expression profiles.Scientific reports · 2017Article
- Advancing Systems Biology in the International Conference on Intelligent Biology and Medicine (ICIBM) 2015.BMC systems biology · 2016Article
- Differential Regulatory Analysis Based on Coexpression Network in Cancer Research.BioMed research international · 2016Review
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioma is the most common brain tumor and it has very high mortality rate due to its infiltration and heterogeneity. Precise classification of glioma subtype is essential for proper therapeutic treatment and better clinical prognosis. However, the molecular mechanism of glioma is far from clear and the classical classification methods based on traditional morphologic and histopathologic knowledge are subjective and inconsistent. Recently, classification methods based on molecular characteristics are developed with rapid progress of high throughput technology.
methodsIn the present study, we designed a novel integrated gene coexpression analysis approach, which involves differential coexpression and differential regulation analysis (DCEA and DRA), to investigate glioma prognostic biomarkers and molecular subtypes based on six glioma transcriptome data sets.
resultsWe revealed a novel three-transcription-factor signature including AHR, NFIL3 and ZNF423 for glioma molecular subtypes. This three-TF signature clusters glioma patients into three major subtypes (ZG, NG and IG subtypes) which are significantly different in patient survival as well as transcriptomic patterns. Notably, ZG subtype is featured with higher expression of ZNF423 and has better prognosis with younger age at diagnosis. NG subtype is associated with higher expression of NFIL3 and AHR, and has worse prognosis with elder age at diagnosis. According to our inferred differential networking information and previously reported signalling knowledge, we suggested testable hypotheses on the roles of AHR and NFIL3 in glioma carcinogenesis.
conclusionsWith so far the least biomarkers, our approach not only provides a novel glioma prognostic molecular classification scheme, but also helps to explore its dysregulation mechanisms. Our work is extendable to prognosis-related classification and signature identification in other cancer researches.
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