Integrative Transcriptomic Analysis Characterizes the Gene Expression Patterns of RNA G-Quadruplex-Associated Genes in Parkinson's Disease.

introductionRNA G-quadruplexes (rG4s) are guanine-rich secondary RNA structures that regulate multiple aspects of post-transcriptional gene expression, including RNA stability, splicing, localization, and translation. Emerging evidence suggests that rG4s contribute to the pathogenesis of neurodegenerative diseases; however, the transcriptomic landscape profile of experimentally validated rG4-interacting genes in Parkinson's disease (PD) remains poorly characterized. This study aimed to investigate the gene expression profiles of rG4-associated genes in PD and examine the relationships between rG4 structural characteristics and expression changes.

methodsA hybrid in silico transcriptomic analysis was performed by integrating RNA sequencing data from the Gene Expression Omnibus dataset GSE136666 with experimentally validated rG4 annotations from G4Atlas. Following transcript-to-gene mapping and data integration, 33 rG4-associated genes with matched gene expression and G4Atlas rG4 propensity score data were identified. The G4Atlas propensity score is a sequence-based metric that estimates the likelihood of an RNA sequence forming an rG4 structure. Gene expression change, rG4 propensity score, rG4 density, and Spearman correlation analyses were performed to evaluate the relationships between rG4 structural features and gene expression in PD. Functional enrichment analysis was performed using g:Profiler to identify overrepresented Gene Ontology (GO) terms among the matched rG4-associated genes.  Results: Among the 33 rG4-associated genes, 16 exhibited negative LogFC values, eight displayed positive LogFC values, and nine exhibited minimal differences in PD expression compared with healthy controls. The largest positive LogFC values were observed for NAT8B and OR9A1P, whereas KRT6B and MUC19 exhibited the largest negative LogFC values. Spearman correlation analysis identified a weak negative correlation between the rG4 propensity score and gene expression change (ρ = -0.1969, p = 0.2720, n = 33), which was not statistically significant, indicating that the rG4 propensity score alone is not a strong predictor of transcriptomic alterations in PD. Similarly, genes with higher rG4 density did not exhibit consistently greater observed expression change, suggesting that structural enrichment is not predictive of expression differences in PD.  Conclusion: This integrative transcriptomic analysis indicates that experimentally validated rG4-associated genes exhibit modest expression changes in PD. Although the rG4 propensity score and rG4 density were not strong independent determinants of observed gene expression differences, the findings suggest that rG4-mediated post-transcriptional regulation may contribute to PD through context-dependent mechanisms. These results provide a foundation for future mechanistic studies investigating the role of rG4-associated transcripts in PD pathogenesis.