Exploring the Diagnostic Potential of IL1R1 in Depression and Its Association with Lipid Metabolism

Yao Gao^1,2Xiao-Na Song³Nan Zhang³Huang-Hu Liu^1,2Jian-Zhen Hu^1,2Xin-Zhe Du^1,2Guo-Hua Song³Sha Liu^1,2*

• ¹Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China
• ²Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China
• ³Department of Basic Medical Sciences, Shanxi Medical University, 56 Xinjian South Rd, Taiyuan 030001, China., Shanxi province, China

Background: Depression 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.Methods: A 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.Results: By 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.Conclusion: This study provides novel insights into the association between OS and dyslipidemia metabolism in depression, offering potential therapeutic targets for future drug development.