GEO多芯片联合eQTL、机器学习预测抑郁症的生物标志物及免疫浸润分析

GEO Multi-Chip Integration of eQTL, Machine Learning Prediction of Biomarkers for Depression, and Immune Infiltration Analysis

摘要: 抑郁症是一种普遍的心理障碍，影响着全球数以百万计的人群。其疗效不稳定和高复发率。近年来，基因表达数量性状位点(expression quantitative trait loci, eQTL)研究为理解抑郁症的遗传基础提供了新的视角。本研究从eQTLGen联盟获取了顺式eQTL数据(ukb-d-F5_DEPRESSIO、ebi-a-GCST003769和ebi-a-GCST005902)，并结合GEO数据库中的五个基因表达数据集(GSE98793、GSE19738、GSE44593、GSE54568和GSE54570)，筛选出与抑郁症相关的差异表达基因。选择合适的工具变量进行MR分析，采用多种统计方法综合评估eQTL与抑郁症之间的因果关系。随后，利用受试者工作特征(Receiver Operating Characteristic, ROC)曲线分析突出诊断潜力的基因。基因本体(Gene Ontology, GO)和KEGG通路分析生物过程。LASSO回归分析进一步筛选核心基因。构建转录因子(TF)-miRNA-枢纽基因调控网络，识别miRNA和转录因子。最终，通过CIBERSORT算法分析抑郁症患者与健康对照组之间的免疫细胞浸润差异，并探讨核心基因与免疫细胞浸润之间的相关性。结果LASSO回归分析保留了4个关键基因(LTF、OLFM4、AKR1C3、WEE1)，排除了EVI2A，因为其AUC值低于0.5。我们构建了一个转录因子(TF)-miRNA-枢纽基因调控网络，识别出48个miRNA和56个转录因子。免疫细胞浸润分析显示抑郁症组与对照组之间存在显著差异，记忆性CD4+ T细胞和巨噬细胞的比例发生了改变。这些发现强调了识别出的基因和免疫细胞作为治疗靶点的潜力。未来的研究应专注于这些发现的临床应用和机制探索，以改善抑郁症的早期诊断和治疗。

Abstract: Depression is a common mental disorder that affects millions of people worldwide, characterized by unstable efficacy and a high relapse rate. In recent years, research on expression quantitative trait loci (eQTL) has provided new insights into the genetic basis of depression. This study obtained cis-eQTL data from the eQTLGen consortium (ukb-d-F5_DEPRESSIO, ebi-a-GCST003769, and ebi-a-GCST005902) and combined it with five gene expression datasets from the GEO database (GSE98793, GSE19738, GSE44593, GSE54568, and GSE54570) to screen for differentially expressed genes associated with depression. Appropriate instrumental variables were selected for MR analysis, and various statistical methods were used to comprehensively assess the causal relationship between eQTL and depression. Subsequently, receiver operating characteristic (ROC) curve analysis was utilized to highlight genes with diagnostic potential. Gene Ontology (GO) and KEGG pathway analyses were conducted to explore biological processes. LASSO regression analysis further filtered core genes. A transcription factor (TF)-miRNA-hub gene regulatory network was constructed to identify miRNAs and transcription factors. Finally, the CIBERSORT algorithm was used to analyze the differences in immune cell infiltration between depression patients and healthy controls, and to explore the correlation between core genes and immune cell infiltration. Results from the LASSO regression analysis retained four key genes (LTF, OLFM4, AKR1C3, WEE1) and excluded EVI2A due to its AUC value being below 0.5. We constructed a transcription factor (TF)-miRNA-hub gene regulatory network, identifying 48 miRNAs and 56 transcription factors. Immune cell infiltration analysis revealed significant differences between the depression group and the control group, with altered proportions of memory CD4+ T cells and macrophages. These findings emphasize the potential of the identified genes and immune cells as therapeutic targets. Future research should focus on the clinical applications and mechanistic exploration of these findings to improve early diagnosis and treatment of depression.