Sun Lu, Ren CaiLi, Leng HaoBo, Wang Xin, Wang DaoRan, Wang TianQi, Wang ZhiQiang, Zhang GuoFu, Yu Haitao
The Affiliated Mental Health Center of Jiangnan University, Wuxi Mental Health Center, Wuxi 214151, Jiangsu, China.
Department of Rehabilitation Medicine, Wuxi Central Rehabilitation Hospital, Wuxi 214151, Jiangsu, China.
Depress Anxiety. 2024 Oct 3;2024:1089236. doi: 10.1155/2024/1089236. eCollection 2024.
Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fatigue, and, in severe case, recurrent thoughts of death. Despite its prevalence, reliable diagnostic biomarkers for MDD remain elusive. Identifying peripheral biomarkers for MDD is crucial for early diagnosis, timely intervention, and ultimately reducing the risk of suicide. Metabolic changes in peripheral blood mononuclear cells (PBMCs) have been observed in animal models of depression, suggesting that PBMC could serve as a valuable matrix for exploring potential peripheral biomarkers in MDD. We performed a transcriptomic analysis of PBMCs from patients with MDD and age- and sex-matched healthy controls ( = 20 per group). Our analysis identified 270 differentially expressed genes in PBMCs from MDD patients compared to controls, which correlated with the Hamilton Depression Rating Scale scores. These genes are involved in several KEGG pathways, including the herpes simplex virus 1 infection pathway, NOD-like receptor signaling pathway, antigen processing and presentation, and glycerophospholipid metabolism-all of which are linked to various aspects of the immune response. Further machine learning analysis and quantitative real-time PCR (qPCR) validation identified three key genes-TRPV2, ZNF713, and CTSL-that effectively distinguish MDD patients from healthy controls. The immune dysregulation observed in PBMCs is closely related to the pathogenesis of MDD. The candidate biomarkers TRPV2, ZNF713, and CTSL, identified and validated through machine learning and qPCR, hold promise for the objective diagnosis of MDD. Clinical Trial Registry identifier: ChiCTR2300076589.
重度抑郁症(MDD)是一种复杂的病症,其特征为持续的情绪低落、兴趣或愉悦感丧失、精力或疲劳感缺失,在严重情况下还会出现反复的死亡念头。尽管其发病率很高,但MDD可靠的诊断生物标志物仍然难以捉摸。识别MDD的外周生物标志物对于早期诊断、及时干预以及最终降低自杀风险至关重要。在抑郁症动物模型中已观察到外周血单核细胞(PBMC)的代谢变化,这表明PBMC可作为探索MDD潜在外周生物标志物的重要基质。我们对MDD患者以及年龄和性别匹配的健康对照(每组20人)的PBMC进行了转录组分析。我们的分析确定,与对照组相比,MDD患者的PBMC中有270个差异表达基因,这些基因与汉密尔顿抑郁量表评分相关。这些基因参与了多个KEGG通路,包括单纯疱疹病毒1感染通路、NOD样受体信号通路、抗原加工和呈递以及甘油磷脂代谢,所有这些都与免疫反应的各个方面相关。进一步的机器学习分析和定量实时PCR(qPCR)验证确定了三个关键基因——TRPV2、ZNF713和CTSL——它们能有效区分MDD患者和健康对照。在PBMC中观察到的免疫失调与MDD的发病机制密切相关。通过机器学习和qPCR识别并验证的候选生物标志物TRPV2、ZNF713和CTSL有望用于MDD的客观诊断。临床试验注册标识符:ChiCTR2300076589。