Table 1_Exploring the diagnostic potential of IL1R1 in depression and its association with lipid metabolism.xlsx

BackgroundDepression is a complex mental disorder where oxidative stress and lipid metabolism disorders play crucial roles, yet their connection requires further exploration. This study aims to investigate the roles of oxidative stress and lipid metabolism disorders in depression using bioinformatics methods and Mendelian randomization analysis.
MethodsA differential gene expression analysis was performed on the GSE76826 dataset, followed by identification of the intersection with genes related to OS. Subsequently, support vector machine (SVM) and random forest algorithms were employed to determine the optimal division of feature variables. The diagnostic performance was evaluated using a ROC diagnostic model and Diagnostic Nomogram. Furthermore, Mendelian randomization (MR) analysis was conducted to explore the causal relationship between the gene and depression. The expression patterns of key genes in brain tissue were analyzed using the Human eFP Browser database, while their associations with metabolism-related genes were investigated using the STRING database. Finally, DrugnomeAI was utilized to assess the drug development potential of these genes, and small molecule compounds targeting them were identified through dgidb and ChEMBL databases; molecular docking studies were then conducted to evaluate their binding affinity.
ResultsBy conducting a comprehensive analysis of oxidative stress-related genes and depression-related target genes, we have successfully identified 12 overlapping genes. These 12 genes were selected using support vector machine and random forest algorithms. Upon analyzing the diagnostic model, it was revealed that EPAS1 and IL1R1 serve as key biomarkers for OS in depression, with IL1R1 exhibiting the highest diagnostic potential among them. Additionally, MRfen analysis suggests that IL1R1 may play a protective role against depression. Notably, this gene exhibits high expression levels in crucial brain regions such as the olfactory bulb, corpus callosum, and hippocampus. Furthermore, our findings indicate an association between IL1R1 and lipid-related genes PDGFB, PIK3R1, TNFRSFIAA NOD2, and LYN. DrugnomeAI analysis indicated promising medicinal value for ILIRI with BI 639667 demonstrating superior binding affinity among the selected small molecule drugs.
ConclusionThis study provides novel insights into the association between OS and dyslipidemia metabolism in depression, offering potential therapeutic targets for future drug development.

研究背景
抑郁症是一种复杂的精神障碍，氧化应激与脂代谢紊乱在其发病过程中发挥关键作用，但二者的关联仍有待进一步阐明。本研究旨在通过生物信息学方法与孟德尔随机化分析，探究氧化应激与脂代谢紊乱在抑郁症中的作用机制。

研究方法
本研究对GSE76826数据集开展差异基因表达分析，进而筛选与氧化应激（oxidative stress, OS）相关基因的交集基因。随后采用支持向量机（support vector machine, SVM）与随机森林算法确定最优特征变量划分方案，并通过ROC诊断模型与诊断列线图评估其诊断效能。此外，本研究开展孟德尔随机化（Mendelian randomization, MR）分析，以探究相关基因与抑郁症之间的因果关联。利用人类电子表达图谱浏览器（Human eFP Browser）数据库分析关键基因在脑组织中的表达模式，并通过STRING数据库探究其与代谢相关基因的相互作用关联。最后，借助DrugnomeAI工具评估上述基因的药物开发潜力，并通过dgidb与ChEMBL数据库筛选靶向这些基因的小分子化合物，随后开展分子对接实验以评估其结合亲和力。

研究结果
通过对氧化应激相关基因与抑郁症相关靶基因的综合分析，本研究成功筛选出12个交集基因。通过支持向量机与随机森林算法对这12个基因进行筛选后，对诊断模型进行分析发现，EPAS1与IL1R1可作为抑郁症中氧化应激的关键生物标志物，其中IL1R1的诊断潜能最高。此外，MRfen分析结果显示，IL1R1可能对抑郁症具有保护作用。值得注意的是，该基因在嗅球、胼胝体与海马体等关键脑区中呈高表达。此外，本研究发现IL1R1与脂代谢相关基因PDGFB、PIK3R1、TNFRSFIAA、NOD2及LYN存在关联。DrugnomeAI分析显示IL1R1具有良好的药用价值，且所选小分子药物中BI 639667的结合亲和力最优。

研究结论
本研究为阐明抑郁症中氧化应激与脂代谢紊乱的关联提供了全新视角，同时为后续药物开发提供了潜在治疗靶点。