ArticleCerebellum (London, England)2019
Differential Expression of Genes for Ubiquitin Ligases in Medulloblastoma Subtypes.
Article in Cerebellum (London, England), 2019. 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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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- RCBTB2 in cancer: context-dependent functions and translational boundaries.Frontiers in oncology · 2026Review
- PP2A activation overcomes leptomeningeal dissemination in group 3 medulloblastoma.The Journal of biological chemistry · 2024Article
- Smurf1 and Smurf2 mediated polyubiquitination and degradation of RNF220 suppresses Shh-group medulloblastoma.Cell death & disease · 2023Article
- Molecular landscape of IDH-mutant astrocytoma and oligodendroglioma grade 2 indicate tumor purity as an underlying genomic factor.Molecular medicine (Cambridge, Mass.) · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using publically available datasets on gene expression in medulloblastoma (MB) subtypes, we selected genes for ubiquitin ligases and identified statistically those that best predicted each of the four major MB subgroups as separate disease entities. We identify a gene coding for an ubiquitin ligase, ZNRF3, whose overexpression alone can predict the WNT subgroup for 100% in the Pfister dataset. For the SHH subgroup, we identify a gene for a regulatory subunit of the protein phosphatase 2A (PP2A), PPP2R2C, as the major predictor among the E3 ligases genes. The ubiquitin and ubiquitin-like conjugation database (UUCD) lists PPP2R2C as coding for a Cullin Ring ubiquitin ligase adaptor. For group 3 MBs, the best ubiquitin ligase predictor was PPP2R2B, a gene which codes for another regulatory subunit of the PP2A holoenzyme. For group 4, the best E3 gene predictors were MID2, ZBTB18, and PPP2R2A, which codes for a third PP2A regulatory subunit. Heatmap analysis of the E3 gene data shows that expression of ten genes for ubiquitin ligases can be used to classify MBs into the four major consensus subgroups. This was illustrated by analysis of gene expression of ubiquitin ligases of the Pfister dataset and confirmed in the dataset of Cavalli. We conclude that genes for ubiquitin ligases can be used as genetic markers for MB subtypes and that the proteins coded for by these genes should be investigated as subtype specific therapeutic targets for MB.
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