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ORIGINAL RESEARCH article

Front. Aging Neurosci.
Sec. Parkinson’s Disease and Aging-related Movement Disorders
Volume 16 - 2024 | doi: 10.3389/fnagi.2024.1458476
This article is part of the Research Topic Advances in Parkinson's Disease Research: Exploring Biomarkers and Therapeutic Strategies for Halting Disease Progression View all 14 articles

Identification of Key Genes and Diagnostic Model Associated with Circadian Rhythms and Parkinson's Disease by Bioinformatics Analysis

Provisionally accepted
Jiyuan Zhang Jiyuan Zhang 1Xiaopeng Ma Xiaopeng Ma 1Zhiguang Li Zhiguang Li 2Hu Liu Hu Liu 1Mei Tian Mei Tian 1Ya Wen Ya Wen 1Shan Wang Shan Wang 1Liang Wang Liang Wang 1*
  • 1 Second Hospital of Hebei Medical University, Shijiazhuang, China
  • 2 Xingtai Central Hospital, Xingtai, Shandong, China

The final, formatted version of the article will be published soon.

    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, 1288 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.

    Keywords: Parkinson's disease, Circadian Rhythm, GEO, BioInformatic, biomarkers

    Received: 02 Jul 2024; Accepted: 02 Oct 2024.

    Copyright: © 2024 Zhang, Ma, Li, Liu, Tian, Wen, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Liang Wang, Second Hospital of Hebei Medical University, Shijiazhuang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.