Table 9_RNA networks of lysosomal-related biomarkers in Parkinson’s disease and their correlations with freezing of gait-associated genes.xlsx

BackgroundParkinson’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.
ObjectiveThis study aims to identify biomarkers specific to PD that exhibit robust disease prediction capabilities.
MethodsDatasets 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.
ResultsKey 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. FGD4 and MAN2B1 were selected as biomarkers, and a gene expression-based risk prediction table was created. These biomarkers were significantly correlated with IRGs and several FOG-related genes. Gene localization analysis revealed that FGD4 and LRRK2, both critical to the FOG pathway, are located on chromosome 12. Drug prediction revealed that Tetrachlorodibenzodioxin and bisphenol A target both FGD4 and MAN2B1. qPCR analysis confirmed that FGD4 and MAN2B1 expression levels were significantly higher in patients with PD compared to healthy controls (p < 0.05).
ConclusionFGD4 and MAN2B1 act as lysosomal biomarkers associated with PD and exhibit strong correlations with genes involved in PD-related freezing of gait. This study offers novel insights into PD diagnosis.

背景 帕金森病（Parkinson’s disease, PD）受多种因素影响，溶酶体功能在其发病过程中发挥关键调控作用，但溶酶体相关基因（lysosome-related genes, LRGs）在PD中的具体参与机制仍不明确。
研究目的 本研究旨在筛选出具有稳健疾病预测能力的PD特异性生物标志物。
研究方法 从在线数据库获取PD患者、溶酶体相关基因（LRGs）及炎症相关基因（inflammation-related genes, IRGs）的数据集。通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）筛选关键模块中的miRNAs与mRNAs，证实其与PD存在显著关联。基于这些模块中与PD相关的LRGs（PD-LRGs）及miRNAs构建miRNA-mRNA调控网络。通过取靶基因、差异表达基因（differentially expressed genes, DEGs）、PD-LRGs及模块关联mRNAs的交集，筛选候选基因。采用机器学习方法与表达验证确认候选生物标志物，构建列线图（nomogram），并通过混淆矩阵评估其诊断效能。基于上述生物标志物开展药物预测分析。通过Spearman相关分析评估IRGs、步态冻结（freezing of gait, FOG）相关基因与生物标志物之间的关联。结合数据集与在线资源构建分子调控网络。最后收集临床样本，采用实时定量PCR（quantitative PCR, qPCR）验证生物标志物的表达水平。
研究结果 本研究筛选得到与PD相关的关键模块，共包含190个miRNAs与7633个mRNAs。基于55个PD-LRGs与181个miRNAs构建miRNA-mRNA调控网络，最终鉴定出26个与溶酶体功能密切相关的候选基因。筛选得到FGD4与MAN2B1作为生物标志物，并构建基于基因表达的风险预测模型。上述生物标志物与IRGs及多个FOG相关基因均存在显著相关性。基因定位分析显示，FGD4与FOG通路关键基因LRRK2均定位于12号染色体。药物预测结果表明，四氯二苯并二噁英与双酚A可同时靶向FGD4与MAN2B1。实时定量PCR分析证实，PD患者样本中FGD4与MAN2B1的表达水平显著高于健康对照（p < 0.05）。
研究结论 FGD4与MAN2B1可作为与PD相关的溶酶体生物标志物，且与PD相关步态冻结通路基因存在显著关联。本研究为PD的临床诊断提供了全新的研究思路。