Analysis of key lncRNA related to Parkinson's disease based on gene co-expression weight networks.

IF 1.2 4区医学 Q4 CLINICAL NEUROLOGY

Neurosciences Pub Date : 2025-01-01 DOI:10.17712/nsj.2025.1.20230112

Wenwen Liang, Wei Zhao, Binghan Li, Jiaying Luo, Xuemei Li, Weihua Jia

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引用次数: 0

Abstract

Objectives: To identify a key Long chain non-coding RNAs (lncRNAs) related to PD and provide a new perspective on the role of LncRNAs in Parkinson's disease (PD) pathophysiology.

Methods: Our study involved analyzing gene chips from the substantia nigra and white blood cells, both normal and PD-inclusive, in the Gene Expression Omnibus (GEO) database, utilizing a weighted gene co-expression network analysis (WGCNA). The technique of WGCNA facilitated the examination of differentially expressed genes (DEGs) in the substantia nigra and the white blood cells of individuals with PD. When merged with clinical data, gene modules containing crucial clinical details were chosen for network integration in GO and KEGG enrichment analysis.

Results: A pair of LncRNA modules were identified. The crucial component in GSE7621 was the turquoise module. The DEGs were acquired using GSE133347. GO functions focused on phosphatidylinositol phosphate binding, inflammatory responses, and the regulation of nerves and synapses. KEGG analyses were largely enriched within the P13K-Akt, FaxO, mTOR, Oxytocin, and cGMP-PKG signaling pathways. A Venn diagram revealed that the two key LncRNA were CH17-189H20.1 and RP11-168O16.1.

Conclusion: Using the WGCNA method, we obtained PD-related modules, identified biologically significant gene modules, obtained core LncRNAs, and found potential target genes for enrichment analysis. The objective of our research was to advance more detailed and efficient treatment methods for lncRNAs associated with PD.

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基于基因共表达权重网络的帕金森病相关关键lncRNA分析

目的：鉴定PD相关的关键长链非编码rna (Long chain non-coding rna, lncRNAs)，为lncRNAs在帕金森病（PD）病理生理中的作用提供新的视角。方法：本研究利用加权基因共表达网络分析（WGCNA），分析基因表达综合数据库（gene Expression Omnibus， GEO）中正常和含pd的黑质和白细胞的基因芯片。WGCNA技术有助于PD患者黑质和白细胞中差异表达基因（DEGs）的检测。当与临床数据合并时，选择包含关键临床细节的基因模块进行GO和KEGG富集分析的网络集成。结果：鉴定出一对LncRNA模块。GSE7621的关键组件是绿松石模块。使用GSE133347获取deg。氧化石墨烯的功能主要集中在磷脂酰肌醇磷酸结合、炎症反应以及神经和突触的调节。KEGG分析主要集中在P13K-Akt、FaxO、mTOR、催产素和cGMP-PKG信号通路中。Venn图显示两个关键LncRNA分别是CH17-189H20.1和RP11-168O16.1。结论：利用WGCNA方法，我们获得了pd相关模块，鉴定了具有生物学意义的基因模块，获得了核心lncrna，并找到了潜在的靶基因进行富集分析。我们的研究目的是为PD相关的lncrna提供更详细和有效的治疗方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neurosciences 医学-临床神经学

CiteScore

1.40

自引率

0.00%

发文量

审稿时长

4.5 months

期刊介绍： Neurosciences is an open access, peer-reviewed, quarterly publication. Authors are invited to submit for publication articles reporting original work related to the nervous system, e.g., neurology, neurophysiology, neuroradiology, neurosurgery, neurorehabilitation, neurooncology, neuropsychiatry, and neurogenetics, etc. Basic research withclear clinical implications will also be considered. Review articles of current interest and high standard are welcomed for consideration. Prospective workshould not be backdated. There are also sections for Case Reports, Brief Communication, Correspondence, and medical news items. To promote continuous education, training, and learning, we include Clinical Images and MCQ’s. Highlights of international and regional meetings of interest, and specialized supplements will also be considered. All submissions must conform to the Uniform Requirements.