ArticleActa neuropathologica communications2024
Systematic transcriptomic analysis of childhood medulloblastoma identifies N6-methyladenosine-dependent lncRNA signatures associated with molecular subtype, immune cell infiltration, and prognosis.
Article in Acta neuropathologica communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Association of Recorded Cerebrospinal Fluid Diversion with Registry Outcome Status in Pediatric Medulloblastoma: An Exploratory 10-Year Single-Centre Cohort Study.Journal of clinical medicine · 2026Article
- Medulloblastoma stem cell programs: Molecular roadmaps of disease progression.Developmental cell · 2026Review
- Long Non-Coding RNAs in Malignant Human Brain Tumors: Driving Forces Behind Progression and Therapy.International journal of molecular sciences · 2025Review
- Systematic decoding the functional role of human endogenous retrovirus-derived RNAs in medulloblastoma.Neuro-oncology advancesArticle
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Abstract
Medulloblastoma, the most common malignant pediatric brain tumor, is classified into four main molecular subgroups, but group 3 and group 4 tumors are difficult to subclassify and have a poor prognosis. Rapid point-of-care diagnostic and prognostic assays are needed to improve medulloblastoma risk stratification and management. N6-methyladenosine (m6A) is a common RNA modification and long non-coding RNAs (lncRNAs) play a central role in tumor progression, but their impact on gene expression and associated clinical outcomes in medulloblastoma are unknown. Here we analyzed 469 medulloblastoma tumor transcriptomes to identify lncRNAs co-expressed with m6A regulators. Using LASSO-Cox analysis, we identified a five-gene m6A-associated lncRNA signature (M6LSig) significantly associated with overall survival, which was combined in a prognostic clinical nomogram. Using expression of the 67 m6A-associated lncRNAs, a subgroup classification model was generated using the XGBoost machine learning algorithm, which had a classification accuracy > 90%, including for group 3 and 4 samples. All M6LSig genes were significantly correlated with at least one immune cell type abundance in the tumor microenvironment, and the risk score was positively correlated with CD4
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