Meta-analysis of differentially expressed non-coding RNA signatures (lncRNAs, miRNAs, and snoRNAs) in Parkinson's disease

Parkinson's disease (PD), the second most common neurodegenerative disorder globally, arises from dopaminergic neuron degeneration and Lewy body accumulation, with increasing evidence highlighting critical regulatory roles of non-coding RNAs (ncRNAs). Our comprehensive meta-analysis integrating four multi-omics datasets revealed 732 significantly dysregulated ncRNAs (294 upregulated, 439 downregulated), including key candidates like NEAT1, MIR182 and SNORA63 demonstrating strong diagnostic potential. Functional characterization identified distinct pathological networks: upregulated ncRNAs predominantly influenced nuclear organization (NEAT1), RNA processing (SNORA63) and cell cycle regulation (MIR133B), while downregulated species were enriched in vascular dysfunction (MIR-451A/MIR-182) and vesicular trafficking pathways (SFTA3). Notably, we discovered neuroprotective microRNA-mediated activation of oncogenic pathways (hsa05206; MIR133B, MIR18A, MIR451A clusters) concurrent with ATXN8OS-associated suppression of spinocerebellar ataxia pathways (hsa05020). Network analysis uncovered two divergent interactomes - a focused upregulated network (4 hubs/32 proteins/151 edges) versus an extensive downregulated network (5 hubs/245 proteins/2826 edges) - suggesting the latter's predominant role in PD progression. Our findings systematically decode ncRNA regulatory architecture in PD, delivering both mechanistic insights and clinically actionable targets for early diagnosis and therapeutic development.

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Journal
Behavioural Brain Research
Published
2025-10-10
DOI
https://doi.org/10.1016/j.bbr.2025.115863
Primary Topic
RNA regulation and disease
Type
article
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article

Meta-analysis of differentially expressed non-coding RNA signatures (lncRNAs, miRNAs, and snoRNAs) in Parkinson's disease

Tun Lin Aung, Xiaoran Shi, Ye Win Aung
Behavioural Brain Research
RNA regulation and disease
article

Meta-analysis of differentially expressed non-coding RNA signatures (lncRNAs, miRNAs, and snoRNAs) in Parkinson's disease

Tun Lin Aung, Xiaoran Shi, Ye Win Aung
article en

Abstract

Parkinson's disease (PD), the second most common neurodegenerative disorder globally, arises from dopaminergic neuron degeneration and Lewy body accumulation, with increasing evidence highlighting critical regulatory roles of non-coding RNAs (ncRNAs). Our comprehensive meta-analysis integrating four multi-omics datasets revealed 732 significantly dysregulated ncRNAs (294 upregulated, 439 downregulated), including key candidates like NEAT1, MIR182 and SNORA63 demonstrating strong diagnostic potential. Functional characterization identified distinct pathological networks: upregulated ncRNAs predominantly influenced nuclear organization (NEAT1), RNA processing (SNORA63) and cell cycle regulation (MIR133B), while downregulated species were enriched in vascular dysfunction (MIR-451A/MIR-182) and vesicular trafficking pathways (SFTA3). Notably, we discovered neuroprotective microRNA-mediated activation of oncogenic pathways (hsa05206; MIR133B, MIR18A, MIR451A clusters) concurrent with ATXN8OS-associated suppression of spinocerebellar ataxia pathways (hsa05020). Network analysis uncovered two divergent interactomes - a focused upregulated network (4 hubs/32 proteins/151 edges) versus an extensive downregulated network (5 hubs/245 proteins/2826 edges) - suggesting the latter's predominant role in PD progression. Our findings systematically decode ncRNA regulatory architecture in PD, delivering both mechanistic insights and clinically actionable targets for early diagnosis and therapeutic development.

Behavioural Brain ResearchVol. 496
Harbin Institute of Technology (CN), Livestock Breeding and Veterinary Department (MM)
Openalex Percentile: Top 75%
RNA regulation and disease
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Meta-analysis of differentially expressed non-coding RNA signatures (lncRNAs, miRNAs, and snoRNAs) in Parkinson's disease — Tun Lin Aung, Xiaoran Shi, et al. · Behavioural Brain Research (2025) | TGRS Research Map | TGRS