ArticleFrontiers in genetics2026
RNA networks of lysosomal-related biomarkers in Parkinson's disease and their correlations with freezing of gait-associated genes.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Parkinson's disease (PD) is influenced by various factors, with lysosome function playing a critical role. However, the specific involvement of lysosome-related genes (LRGs) in PD remains unclear. Objective: This study aims to identify biomarkers specific to PD that exhibit robust disease prediction capabilities. Methods: Datasets for patients with PD, LRGs, and inflammation-related genes (IRGs) were retrieved from online databases. miRNAs and mRNAs within key modules were selected through Weighted Gene Co-expression Network Analysis (WGCNA), revealing strong associations with PD. A miRNA-mRNA network was constructed based on highly correlated PD-related LRGs (PD-LRGs) and miRNAs within these modules. Candidate genes were identified by intersecting target genes, differentially expressed genes (DEGs), PD-LRGs, and module-associated mRNAs. Machine learning and expression validation were employed to confirm these biomarkers. A nomogram was established, and its diagnostic performance was evaluated using a confusion matrix. Drug predictions were conducted based on these biomarkers. Spearman's correlation analyses were performed to assess the relationship between IRGs, freezing of gait (FOG)-related genes, and biomarkers. Molecular regulatory networks were constructed using datasets and online resources. Finally, clinical samples were collected for quantitative PCR (qPCR) validation of biomarker expression. Results: Key modules related to PD were identified, comprising 190 miRNAs and 7,633 mRNAs. A miRNA-mRNA network was constructed based on 55 PD-LRGs and 181 miRNAs, resulting in the identification of 26 candidate genes strongly linked to lysosomal function. Conclusion:
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