Zhang Jiyuan, Ma Xiaopeng, Li Zhiguang, Liu Hu, Tian Mei, Wen Ya, Wang Shan, Wang Liang
Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
School of Basic Medicine, Hebei Medical University, Shijiazhuang, China.
Front Aging Neurosci. 2024 Oct 16;16:1458476. doi: 10.3389/fnagi.2024.1458476. eCollection 2024.
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.
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.
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.
Through bioinformatic analysis, the circadian rhythm related biomarkers were identified (AK3, RTN3 and LEPR) in PD, contributing to studies related to PD treatment.
昼夜节律紊乱在帕金森病（PD）早期较为典型，且在PD晚期治疗效果的预后中起重要作用。越来越多的证据表明，昼夜节律基因可影响PD的发展。因此，本研究通过生物信息学方法探索昼夜节律基因（C基因）在PD中的具体调控机制。
使用差异表达分析从GSE22491中鉴定出PD样本与对照样本之间的差异表达基因（DEG）。通过加权基因共表达网络分析（WGCNA）得出与PD相关性最高的关键模型。然后，将DEG、1288个C基因和关键模块中的基因进行重叠，以产生差异表达的C基因（DECG），并对其进行套索回归分析（LASSO）和支持向量机递归特征消除分析（SVM-RFE）以产生关键基因。同时，从GSE22491和GSE100054中，对关键基因进行受试者工作特征（ROC）分析以鉴定生物标志物，并应用基因集富集分析（GSEA）来探索与生物标志物相关的通路。最终，进行免疫浸润分析以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验证了这些基因的表达。
PD样本与对照样本之间共产生634个DEG，MEgreen模块与PD的相关性最高，因此将其定义为关键模型。对18个DECG进行LASSO和SVM-RFE分析后，鉴定出四个关键基因（AK3、RTN3、CYP4F2和LEPR）。通过ROC分析，AK3、RTN3和LEPR因其出色的区分PD与对照样本的能力而被鉴定为生物标志物。此外，生物标志物与帕金森病及其他功能通路相关。
通过生物信息学分析，在PD中鉴定出了与昼夜节律相关的生物标志物（AK3、RTN3和LEPR），有助于PD治疗相关的研究。
