ArticleFrontiers in oncology2021
DNA Methylation-Driven Genes for Developing Survival Nomogram for Low-Grade Glioma.
Article in Frontiers in oncology, 2021. 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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Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Exosome-Related Gene Signature Predicts Prognosis and Immunotherapy in Gastric Cancer.International journal of genomics · 2026Article
- Prognostic value of metal-based ferroptosis and cuproptosis genes and score in lower grade gliomas.Frontiers in immunology · 2025Article
- Development and approval of a Lasso score based on nutritional and inflammatory parameters to predict prognosis in patients with glioma.Frontiers in oncology · 2025Article
- A multi-omics analysis-based model to predict the prognosis of low-grade gliomas.Scientific reports · 2024Article
- Cuproptosis-related gene-located DNA methylation in lower-grade glioma: Prognosis and tumor microenvironment.Cancer biomarkers : section A of Disease markers · 2024Article
- Article
- HighBrain sciences · 2023Article
- DNA Methylation and Histone Modification in Low-Grade Gliomas: Current Understanding and Potential Clinical Targets.Cancers · 2023Review
- Article
- Interfering withAnnals of translational medicine · 2022Article
- Prognostic biomarker SGSM1 and its correlation with immune infiltration in gliomas.BMC cancer · 2022Article
- Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes
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