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Updated: May 31, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Systematic decoding the functional role of human endogenous retrovirus-derived RNAs in medulloblastoma
Jiaming Zhao1, Wei Yang2, Tingting Wang3
1Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.
Background:
Medulloblastoma (MB) is the most common malignant pediatric brain tumor, but its pathogenesis remains poorly understood. Although human endogenous retrovirus-derived RNAs (hervRNAs) are implicated in tumorigenesis across various cancers, their functional role in the pathogenesis of MB has not been discovered.
Methods:
We collected total RNA samples from 63 pediatric MB patients for RNA sequencing and identified the expression of hervRNAs in a locus-specific manner. The functional role of hervRNAs was analyzed through independent expression analysis, gene set enrichment analysis, weighted correlation network analysis, and further validated by siRNA-knockdown experiment in D283 cells.
Results:
We systematically identified 39,613 MB-expressed hervRNAs and found that their expression profile could distinguish the four consensus molecular subgroups. Specifically, 145 subgroup-specific hervRNAs were identified to be significantly associated with tumor development. Functional annotation revealed that these hervRNAs were enriched in known MB subgroup-related pathways but also implied novel pathways related to regulated necrosis, negative regulation of class C GPCR, extracellular matrix remodeling, as well as DNA methylation. Notably, the expression of Group3-specific hervRNAs showed a significant negative correlation with promoter DNA methylation levels. Experimental validation confirmed that Group3-specific hervRNA_G38830 and hervRNA_G66017 inhibit cell proliferation and promote apoptosis. Finally, a semi-quantitative model based on 70 hervRNA clusters achieved molecular classification with accuracy above 94.8%.
Conclusion:
Our work provided insights into MB tumorigenesis in the layer of hervRNA, uncovering potential targets for therapeutic intervention and novel biomarkers for molecular classification.
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