Peng Haoran, Cheng Yanwei, Chen Qiao, Qin Lijie
Department of Neurology, People's Hospital of Henan University, Henan Provincial People's Hospital, Zhengzhou, Henan, 450003, People's Republic of China.
Department of Emergency, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, People's Republic of China.
Int J Gen Med. 2024 Nov 25;17:5547-5562. doi: 10.2147/IJGM.S486214. eCollection 2024.
Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons. This study aims to discover potential new genetic biomarkers for PD.
Transcriptome data from a total of 56 patients with PD and 61 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database. Differential gene expression (DEG) analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms (LASSO, Random Forest, SVM-RFE) were employed to identify pivotal PD-associated genes. Additionally, RT-qPCR experiments were conducted to validate our findings in clinical specimens. Functional enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed to explore the functional and pathway mechanisms of the identified genes in PD. Molecular docking studies revealed potential small-molecule drug targets for the key genes.
The results from the three machine learning algorithms identified () as a key gene in PD. Gene expression analysis indicated that is significantly downregulated in PD patients, and the receiver operating characteristic (ROC) analysis validated the diagnostic potential of . The results from RT-qPCR on clinical specimens confirmed the findings from public database analyses. Functional enrichment analysis suggested that is involved in dopamine biosynthesis and synaptic transmission for PD pathology. Additionally, expression correlated significantly with immune cell infiltration. Furthermore, molecular docking results indicated that Acalabrutinib, Tirabrutinib Hydrochloride, and Ibrutinib are potential targeted therapeutic agents for .
These findings underscore as a novel diagnostic biomarker and potential therapeutic target for PD, warranting further mechanistic studies and clinical validation.
帕金森病（PD）是一种常见的神经退行性疾病，其特征是多巴胺能神经元逐渐丧失。本研究旨在发现帕金森病潜在的新基因生物标志物。
从基因表达综合数据库（GEO）下载了总共56例帕金森病患者和61例健康对照的转录组数据。采用差异基因表达（DEG）分析、加权基因共表达网络分析（WGCNA）和三种机器学习算法（LASSO、随机森林、支持向量机递归特征消除法（SVM-RFE））来识别关键的帕金森病相关基因。此外，进行了逆转录定量聚合酶链反应（RT-qPCR）实验以在临床标本中验证我们的发现。进行了功能富集分析和基因集富集分析（GSEA）以探索所识别基因在帕金森病中的功能和通路机制。分子对接研究揭示了关键基因潜在的小分子药物靶点。
三种机器学习算法的结果确定（）为帕金森病的关键基因。基因表达分析表明，该基因在帕金森病患者中显著下调，受试者工作特征（ROC）分析验证了该基因的诊断潜力。临床标本的RT-qPCR结果证实了公共数据库分析的结果。功能富集分析表明，该基因参与帕金森病病理学中的多巴胺生物合成和突触传递。此外，该基因表达与免疫细胞浸润显著相关。此外，分子对接结果表明，阿卡拉布替尼、盐酸替拉布替尼和伊布替尼是该基因潜在的靶向治疗药物。
这些发现强调该基因是帕金森病一种新的诊断生物标志物和潜在治疗靶点，值得进一步进行机制研究和临床验证。
