ArticleActa neuropathologica communications2023
Dissection of transcriptomic and epigenetic heterogeneity of grade 4 gliomas: implications for prognosis.
Article in Acta neuropathologica communications, 2023. 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.
- Overexpression of key complement regulators in glioblastoma.PloS one · 2026Article
- Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.BMC cancer · 2025Article
- Epigenetic Alterations in Glioblastoma Multiforme as Novel Therapeutic Targets: A Scoping Review.International journal of molecular sciences · 2025Article
- MRI-based habitat imaging predicts high-risk molecular subtypes and early risk assessment of lower-grade gliomas.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas.NPJ precision oncology · 2025Article
- Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma.PloS one · 2025Article
- The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model.Journal of cancer research and clinical oncology · 2024Article
- A 5-Hydroxymethylcytosine-Based Noninvasive Model for Early Detection of Colorectal Carcinomas and Advanced Adenomas: The METHOD-2 Study.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Observational
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9 authors.
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Abstract
backgroundGrade 4 glioma is the most aggressive and currently incurable brain tumor with a median survival of one year in adult patients. Elucidating novel transcriptomic and epigenetic contributors to the molecular heterogeneity underlying its aggressiveness may lead to improved clinical outcomes.
methodsTo identify grade 4 glioma -associated 5-hydroxymethylcytosine (5hmC) and transcriptomic features as well as their cross-talks, genome-wide 5hmC and transcriptomic profiles of tissue samples from 61 patients with grade 4 gliomas and 9 normal controls were obtained for differential and co-regulation/co-modification analyses. Prognostic models on overall survival based on transcriptomic features and the 5hmC modifications summarized over genic regions (promoters, gene bodies) and brain-derived histone marks were developed using machine learning algorithms.
resultsDespite global reduction, the majority of differential 5hmC features showed higher modification levels in grade 4 gliomas as compared to normal controls. In addition, the bi-directional correlations between 5hmC modifications over promoter regions or gene bodies and gene expression were greatly disturbed in grade 4 gliomas regardless of IDH1 mutation status. Phenotype-associated co-regulated 5hmC-5hmC modules and 5hmC-mRNA modules not only are enriched with different molecular pathways that are indicative of the pathogenesis of grade 4 gliomas, but also are of prognostic significance comparable to IDH1 mutation status. Lastly, the best-performing 5hmC model can predict patient survival at a much higher accuracy (c-index = 74%) when compared to conventional prognostic factor IDH1 (c-index = 57%), capturing the molecular characteristics of tumors that are independent of IDH1 mutation status and gene expression-based molecular subtypes.
conclusionsThe 5hmC-based prognostic model could offer a robust tool to predict survival in patients with grade 4 gliomas, potentially outperforming existing prognostic factors such as IDH1 mutations. The crosstalk between 5hmC and gene expression revealed another layer of complexity underlying the molecular heterogeneity in grade 4 gliomas, offering opportunities for identifying novel therapeutic targets.
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