Shen Zhuzhen, Zhang Jieli, Jing Xiuna, Tao Enxiang
The Eighth Affiliated Hospital of Sun Yat-Sen University Neurology Department, Shenzhen, Guangdong, China.
Department of Neurology, Sun Yat-Sen Memorial Hospital, Guangzhou, Guangdong, China.
Parkinsons Dis. 2025 May 7;2025:2323585. doi: 10.1155/padi/2323585. eCollection 2025.
Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Inflammation, marked by the infiltration of inflammatory mediators and the proliferation of inflammatory cells, is closely linked to PD. This study aims to identify and validate inflammation-related biomarkers in PD and construct a TF-mRNA-miRNA coexpression network through bioinformatics analysis. The PD-associated dataset GSE7621 and inflammation-related genes were downloaded from the GEO Database and GeneCards platform to obtain inflammation-related differential expression genes (IRDEGs). The key IRDEGs were generated by PPI network analysis. The gene expression levels of the key IRDEGs were validated by blood samples from PD patients using QPCR analysis. We utilized the ENCODE, hTFtarget, CHEA, miRWALK, and miRDB databases to obtain upstream and downstream molecular network models for constructing the TF-mRNA-miRNA interaction network of the key IRDEGs. Finally, based on CIBERSORT algorithm, the associations between IRDEs and immune cell infiltration were investigated. A total of four key IRDEGs (CXCR4, LEP, SLC18A2, and TAC1) were screened and validated. Through biological function analysis, key-related pathways and coexpression networks of PD were identified. These genes may be closely related to the onset of PD. Additionally, we found that increased CD4 T-cell infiltration might be associated with the occurrence of PD. We identified four potential inflammation-related treatment target and constructed a TF-mRNA-miRNA regulatory network. This information provides an initial basis for understanding the complex PD regulatory mechanisms.
帕金森病（PD）是全球第二常见的神经退行性疾病。以炎症介质浸润和炎症细胞增殖为特征的炎症与PD密切相关。本研究旨在识别和验证PD中与炎症相关的生物标志物，并通过生物信息学分析构建一个转录因子-信使核糖核酸-微小核糖核酸共表达网络。从基因表达综合数据库（GEO Database）和基因卡片平台（GeneCards platform）下载了与PD相关的数据集GSE7621和与炎症相关的基因，以获得与炎症相关的差异表达基因（IRDEGs）。通过蛋白质-蛋白质相互作用（PPI）网络分析生成关键的IRDEGs。使用定量聚合酶链反应（QPCR）分析，通过帕金森病患者的血液样本验证关键IRDEGs的基因表达水平。我们利用ENCODE、hTFtarget、CHEA、miRWALK和miRDB数据库来获取上游和下游分子网络模型，以构建关键IRDEGs的转录因子-信使核糖核酸-微小核糖核酸相互作用网络。最后，基于CIBERSORT算法，研究IRDEs与免疫细胞浸润之间的关联。共筛选并验证了四个关键的IRDEGs（CXCR4、LEP、SLC18A2和TAC1）。通过生物学功能分析，确定了帕金森病的关键相关通路和共表达网络。这些基因可能与帕金森病的发病密切相关。此外，我们发现CD4 T细胞浸润增加可能与帕金森病的发生有关。我们确定了四个潜在的与炎症相关的治疗靶点，并构建了一个转录因子-信使核糖核酸-微小核糖核酸调控网络。这些信息为理解复杂的帕金森病调控机制提供了初步依据。
