Wang Peng, Chen Qi, Tang Zhuqian, Wang Liang, Gong Bizhen, Li Min, Li Shaodan, Yang Minghui
Postgraduate School, Medical School of Chinese PLA, Beijing, China.
Department of Traditional Chinese Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
Front Genet. 2023 Jul 6;14:1231707. doi: 10.3389/fgene.2023.1231707. eCollection 2023.
Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson's disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformatics and machine learning approach to predict the genes associated with ferroptosis in PD and investigate the interactions between natural products and their active ingredients with these genes. We comprehensively analyzed differentially expressed genes (DEGs) for ferroptosis associated with PD (PDFerDEGs) by pairing 3 datasets (GSE7621, GSE20146, and GSE202665) from the NCBI GEO database and the FerrDb V2 database. A machine learning approach was then used to screen PDFerDEGs for signature genes. We mined the interacted natural product components based on screened signature genes. Finally, we mapped a network combined with ingredients and signature genes, then carried out molecular docking validation of core ingredients and targets to uncover potential therapeutic targets and ingredients for PD. We identified 109 PDFerDEGs that were significantly enriched in biological processes and KEGG pathways associated with ferroptosis (including iron ion homeostasis, iron ion transport and ferroptosis, etc.). We obtained 29 overlapping genes and identified 6 hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2) by screening with two machine learning algorithms. Based on this, we screened 263 natural product components and subsequently mapped the "Overlapping Genes-Ingredients" network. According to the network, top 5 core active ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) were molecularly docked to hub genes to reveal their potential role in the treatment of ferroptosis in PD. Our findings suggested that PDFerDEGs are associated with ferroptosis and play a role in the progression of PD. Taken together, core ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) bind well to hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2), highlighting novel biomarkers for PD.
铁死亡是一种新型细胞死亡形式，与帕金森病（PD）黑质中积累的过量铁密切相关。尽管进行了广泛研究，但PD中铁死亡的潜在分子机制仍不清楚。在此，我们采用生物信息学和机器学习方法来预测与PD中铁死亡相关的基因，并研究天然产物及其活性成分与这些基因之间的相互作用。我们通过将来自NCBI GEO数据库和FerrDb V2数据库的3个数据集（GSE7621、GSE20146和GSE202665）配对，全面分析了与PD相关的铁死亡差异表达基因（PDFerDEGs）。然后使用机器学习方法从PDFerDEGs中筛选特征基因。我们基于筛选出的特征基因挖掘相互作用的天然产物成分。最后，我们绘制了一个结合成分和特征基因的网络，然后对核心成分和靶点进行分子对接验证，以揭示PD的潜在治疗靶点和成分。我们鉴定出109个PDFerDEGs，它们在与铁死亡相关的生物学过程和KEGG通路（包括铁离子稳态、铁离子转运和铁死亡等）中显著富集。通过两种机器学习算法筛选，我们获得了29个重叠基因并鉴定出6个枢纽基因（TLR4、IL6、ADIPOQ、PTGS2、ATG7和FADS2）。基于此，我们筛选了263种天然产物成分，随后绘制了“重叠基因-成分”网络。根据该网络，将排名前5的核心活性成分（槲皮素、17-β-雌二醇、甘油、反式白藜芦醇和生育酚）与枢纽基因进行分子对接，以揭示它们在治疗PD铁死亡中的潜在作用。我们的研究结果表明，PDFerDEGs与铁死亡相关，并在PD进展中起作用。综上所述,核心成分（槲皮素、17-β-雌二醇、甘油、反式白藜芦醇和生育酚）与枢纽基因（TLR4、IL6、ADIPOQ、PTGS2、ATG7和FADS2）结合良好，突出了PD的新型生物标志物。
