Deng Zhifang, Liu Jue, He Shen, Gao Wenqi
Department of Pharmacy, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Pharmacol. 2022 May 19;13:848939. doi: 10.3389/fphar.2022.848939. eCollection 2022.
Pyroptosis is recently identified as an inflammatory form of programmed cell death. However, the roles of pyroptosis-related genes (PS genes) in major depressive disorder (MDD) remain unclear. This study developed a novel diagnostic model for MDD based on PS genes and explored the pathological mechanisms associated with pyroptosis. First, we obtained 23 PS genes that were differentially expressed between healthy controls and MDD cases from GSE98793 dataset. There were obvious variation in immune cell infiltration profiles and immune-related pathway enrichment between healthy controls and MDD cases. Then, a novel diagnostic model consisting of eight PS genes (, , , , , , and ) for MDD was constructed by random forest (RF) and least absolute shrinkage and selection operator (LASSO) analyses. ROC analysis revealed that our model has good diagnostic performance, AUC = 0.795 (95% CI 0.721-0.868). Subsequently, the consensus clustering method based on 23 differentially expressed PS genes was constructed to divide all MDD cases into two distinct pyroptosis subtypes (cluster A and B) with different immune and biological characteristics. Principal component analysis (PCA) algorithm was performed to calculate the pyroptosis scores ("PS-scores") for each sample to quantify the pyroptosis regulation subtypes. The MDD patients in cluster B had higher "PS-scores" than those in cluster A. Furthermore, we also found that MDD patients in cluster B showed lower expression levels of 11 interferon (IFN)-α isoforms. In conclusion, pyroptosis may play an important role in MDD and can provide new insights into the diagnosis and underlying mechanisms of MDD.
细胞焦亡是最近被确定的一种程序性细胞死亡的炎症形式。然而,细胞焦亡相关基因(PS基因)在重度抑郁症(MDD)中的作用仍不清楚。本研究基于PS基因开发了一种新的MDD诊断模型,并探讨了与细胞焦亡相关的病理机制。首先,我们从GSE98793数据集中获得了23个在健康对照和MDD病例之间差异表达的PS基因。健康对照和MDD病例之间的免疫细胞浸润谱和免疫相关通路富集存在明显差异。然后,通过随机森林(RF)和最小绝对收缩和选择算子(LASSO)分析构建了一个由八个PS基因(、、、、、、和)组成的新的MDD诊断模型。ROC分析显示,我们的模型具有良好的诊断性能,AUC = 0.795(95%CI 0.721 - 0.868)。随后,构建了基于23个差异表达PS基因的一致性聚类方法,将所有MDD病例分为具有不同免疫和生物学特征的两种不同的细胞焦亡亚型(A簇和B簇)。进行主成分分析(PCA)算法以计算每个样本的细胞焦亡评分(“PS评分”),以量化细胞焦亡调节亚型。B簇中的MDD患者的“PS评分”高于A簇中的患者。此外,我们还发现B簇中的MDD患者1十一干扰素(IFN)-α亚型的表达水平较低。总之,细胞焦亡可能在MDD中起重要作用,并可为MDD的诊断和潜在机制提供新的见解。