Translational cancer research · 2026
Multi-omics clustering combined with multiple machine learning to identify epigenetic features in low-grade glioma patients.
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Qiu Lixuan, Lian Xiaofu, Qiang Xiaoyu, Lian Chaoqun, Zhang Jing
Аннотация
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.
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.