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Updated: May 26, 2026
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Multi-omics clustering combined with multiple machine learning to identify epigenetic features in low-grade glioma
Lixuan Qiu1, Xiaofu Lian2, Xiaoyu Qiang3
1Department of First Clinical Medicine, Bengbu Medical University, Bengbu, China.
Background:
Epigenetic heterogeneity has been demonstrated in a wide range of cancers including leukemia, colorectal cancer, and glioma; in the case of low-grade glioma (LGG), its association with tumor malignant progression and immune microenvironmental regulation has been less reported. This study aimed to further explore the application of epigenetics in LGG.
Methods:
Epigenetic subtypes were identified based on consensus clustering of multi-omics data, and prognostic models were constructed based on 101 machine learning combinations. Biological differences among different risk groups were explored by functional enrichment analysis, and finally Jumonji Domain-Containing 8 (JMJD8) was identified as a potential molecular marker in LGGs by pan-cancer survival analysis.
Results:
We revealed three epigenetic molecular subtypes in LGG patients, and the chromosomal regulator-associated risk score, defined as an independent prognostic factor for LGGs, was effective in predicting the response to treatment in LGG patients. JMJD8, a key model gene, was also strongly associated with the prognosis of LGG patients.
Conclusions:
This study reveals the molecular pattern and prognostic model of epigenetic inheritance in LGG, and the key gene JMJD8 is a potential prognostic biomarker of LGG, which is a guide for the clinical application of epigenetics.
