通过生物信息学分析确定与昼夜节律和帕金森病相关的关键基因和诊断模型。

IF 4.1 2区 医学 Q2 GERIATRICS & GERONTOLOGY
Frontiers in Aging Neuroscience Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI:10.3389/fnagi.2024.1458476
Jiyuan Zhang, Xiaopeng Ma, Zhiguang Li, Hu Liu, Mei Tian, Ya Wen, Shan Wang, Liang Wang
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引用次数: 0

摘要

背景:昼夜节律紊乱是帕金森病(PD)早期的典型症状,对帕金森病晚期治疗效果的预后起着重要作用。越来越多的证据表明,昼夜节律基因可影响帕金森病的发展。因此,本研究通过生物信息学方法探讨了昼夜节律基因(C-基因)在帕金森病中的特定调控机制:方法:采用差异表达分析方法从 GSE22491 样本中鉴定了 PD 和对照样本之间的差异表达基因(DEGs)。通过 WGCNA 分析得出了与 PD 相关性最高的关键模型。然后,将 DEGs、1,288 个 C 基因和关键模块中的基因重叠,得出差异表达的 C 基因(DECGs),并对其进行 LASSO 和 SVM-RFE 分析,得出关键基因。同时,从 GSE22491 和 GSE100054 中,对关键基因进行接收者操作特征(ROC)分析,以确定生物标志物,并应用基因组富集分析(GSEA)来探索生物标志物所涉及的通路。最后,应用免疫浸润分析了解生物标志物对免疫微环境的影响,并预测可能影响生物标志物表达的治疗药物。最后,我们通过 q-PCR 验证了基因的表达:结果:共发现 634 个 DEGs 存在于 PD 和对照样本之间,其中 MEgreen 模块与 PD 的相关性最高,因此被定义为关键模型。对 18 个 DECGs 进行 LASSO 和 SVM-RFE 分析后,确定了四个关键基因(AK3、RTN3、CYP4F2 和 LEPR)。通过 ROC 分析,AK3、RTN3 和 LEPR 被确定为生物标志物,因为它们在区分 PD 和对照样本方面具有出色的能力。此外,生物标志物还与帕金森病和其他功能通路相关:通过生物信息学分析,确定了帕金森病中与昼夜节律相关的生物标志物(AK3、RTN3和LEPR),有助于帕金森病治疗的相关研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of key genes and diagnostic model associated with circadian rhythms and Parkinson's disease by bioinformatics analysis.

Background: Circadian rhythm disruption is typical in Parkinson's disease (PD) early stage, and it plays an important role in the prognosis of the treatment effect in the advanced stage of PD. There is growing evidence that circadian rhythm genes can influence development of PD. Therefore, this study explored specific regulatory mechanism of circadian genes (C-genes) in PD through bioinformatic approaches.

Methods: Differentially expressed genes (DEGs) between PD and control samples were identified from GSE22491 using differential expression analysis. The key model showing the highest correlation with PD was derived through WGCNA analysis. Then, DEGs, 1,288 C-genes and genes in key module were overlapped for yielding differentially expressed C-genes (DECGs), and they were analyzed for LASSO and SVM-RFE for yielding critical genes. Meanwhile, from GSE22491 and GSE100054, receiver operating characteristic (ROC) was implemented on critical genes to identify biomarkers, and Gene Set Enrichment Analysis (GSEA) was applied for the purpose of exploring pathways involved in biomarkers. Eventually, immune infiltrative analysis was applied for understanding effect of biomarkers on immune microenvironment, and therapeutic drugs which could affect biomarkers expressions were also predicted. Finally, we verified the expression of the genes by q-PCR.

Results: Totally 634 DEGs were yielded between PD and control samples, and MEgreen module had the highest correlation with PD, thus it was defined as key model. Four critical genes (AK3, RTN3, CYP4F2, and LEPR) were identified after performing LASSO and SVM-RFE on 18 DECGs. Through ROC analysis, AK3, RTN3, and LEPR were identified as biomarkers due to their excellent ability to distinguish PD from control samples. Besides, biomarkers were associated with Parkinson's disease and other functional pathways.

Conclusion: Through bioinformatic analysis, the circadian rhythm related biomarkers were identified (AK3, RTN3 and LEPR) in PD, contributing to studies related to PD treatment.

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来源期刊
Frontiers in Aging Neuroscience
Frontiers in Aging Neuroscience GERIATRICS & GERONTOLOGY-NEUROSCIENCES
CiteScore
6.30
自引率
8.30%
发文量
1426
期刊介绍: Frontiers in Aging Neuroscience is a leading journal in its field, publishing rigorously peer-reviewed research that advances our understanding of the mechanisms of Central Nervous System aging and age-related neural diseases. Specialty Chief Editor Thomas Wisniewski at the New York University School of Medicine is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
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