Table 8_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx

BackgroundMajor depressive disorder (MDD) is associated with mitochondrial dysfunction and programmed cell death (PCD), though the underlying mechanisms remain unclear. This study aimed to investigate the molecular pathways involved in MDD using a transcriptomic analysis approach.
MethodsTranscriptomic data related to MDD were obtained from public databases. Differentially expressed genes (DEGs), PCD-related genes (PCDs), and mitochondrial-related genes (MitoGs) were analyzed to identify key gene sets: PCD-DEGs and MitoG-DEGs. Correlation analysis (|correlation coefficient| > 0.9, p < 0.05) was performed to select candidate genes. Protein-protein interaction (PPI) network analysis and intersection of four algorithms were used to identify key candidate genes. Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. Additional analyses included immune infiltration, regulatory networks, and drug predictions.
ResultsCD63, IL17RA, and IL1R1 were identified as potential biomarkers, with significantly higher expression levels in the MDD cohort. These findings were validated by RT-qPCR. A nomogram based on these biomarkers demonstrated predictive capacity for MDD. Differential immune cell infiltration was observed, with significant differences in nine immune cell types, including activated T cells and eosinophils, between the MDD and control groups. ATF1 was identified as a common transcription factor for CD63, IL17RA, and IL1R1. Shared miRNAs for CD63 and IL1R1 included hsa-miR-490-3p and hsa-miR-125a-3p. Drug prediction analysis identified 50 potential drugs, including verteporfin, etynodiol, and histamine, targeting these biomarkers.
ConclusionCD63, IL17RA, and IL1R1 are key biomarkers for MDD, providing insights for diagnostic development and targeted therapies. The predictive nomogram and drug predictions offer valuable tools for MDD management.

背景：重度抑郁症（Major Depressive Disorder, MDD）与线粒体功能障碍及程序性细胞死亡（Programmed Cell Death, PCD）密切相关，但其潜在分子机制尚未明确。本研究采用转录组学分析方法，探究与重度抑郁症相关的分子通路。
研究方法：本研究从公共数据库获取与重度抑郁症相关的转录组学数据。对差异表达基因（Differentially Expressed Genes, DEGs）、程序性细胞死亡相关基因（PCD-related genes, PCDs）及线粒体相关基因（mitochondrial-related genes, MitoGs）进行分析，以筛选关键基因集：PCD-差异表达基因（PCD-DEGs）与线粒体-差异表达基因（MitoG-DEGs）。通过相关性分析（相关系数绝对值>0.9，P<0.05）筛选候选基因。采用蛋白质相互作用（Protein-Protein Interaction, PPI）网络分析结合四种算法取交集的方法，鉴定关键候选基因。随后采用机器学习与基因表达验证进行初步验证，并通过逆转录定量聚合酶链反应（Reverse Transcription-Quantitative Polymerase Chain Reaction, RT-qPCR）完成进一步验证。基于生物标志物构建预测重度抑郁症发病概率的列线图。其余分析内容还包括免疫浸润分析、调控网络分析及药物预测分析。
研究结果：CD63、IL17RA及IL1R1被鉴定为潜在生物标志物，在重度抑郁症队列中的表达水平显著升高。上述研究结果经RT-qPCR验证。基于上述生物标志物构建的列线图对重度抑郁症具有良好的预测效能。研究观察到免疫细胞浸润存在差异，重度抑郁症组与对照组在9种免疫细胞类型（包括活化T细胞、嗜酸性粒细胞）上均存在显著差异。ATF1被鉴定为CD63、IL17RA及IL1R1的共同转录因子。CD63与IL1R1的共有微小RNA（microRNA, miRNA）包括hsa-miR-490-3p及hsa-miR-125a-3p。药物预测分析共筛选出50种可靶向上述生物标志物的潜在药物，包括维替泊芬（verteporfin）、炔诺醇（etynodiol）及组胺（histamine）。
研究结论：CD63、IL17RA及IL1R1是重度抑郁症的关键生物标志物，可为该病的诊断试剂开发与靶向治疗提供理论参考；本研究构建的预测列线图与药物预测结果可为重度抑郁症的临床管理提供实用工具。