Liu Mengyu, Sun Haochen, Yao Qun, Wang Duohao, Zhang Jihong, Ye Xing, Qi Xinyang
Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
Department of Neurology, School of Medicine, Southeast University, Nanjing, China.
Front Immunol. 2024 Dec 23;15:1506214. doi: 10.3389/fimmu.2024.1506214. eCollection 2024.
Post-stroke depression (PSD) is the most prevalent neuropsychiatric complication following a stroke. The inflammatory theory suggests that PSD may be associated with an overactive inflammatory response. However, research findings regarding inflammation-related indicators in PSD remain inconsistent and elusive. This study aimed to screen the diagnostic markers that helps to distinguish between PSD and post-stroke non-depressed (PSND) patients.
Two GEO datasets, including patients with major depression disease (MDD) and controls (CON, GSE98793), ischemic stroke (IS) and CON (GSE16561), were used to analyzed differentially expressed genes (DEGs) and perform enrichment analysis. Protein-protein interaction (PPI) network and Random Forest analysis were used to screen the candidate hub genes. CIBERSORT was performed to analyze the immune infiltration. We analyzed the proteins that interact with the hub genes using string database, circRNA-miRNA-mRNA ceRNA network of the hub genes using RNAInter, miRWalk, miRDB and Starbase databases, and the drugs that regulate the hub genes by DSigDB database. We further verified the expression of the hub genes using Quantitative Real-Time PCR from the blood of patients and CON.
From the screened 394 DEGs, the DEGs were found primarily related to activation of immune response. PPI network and random forest analysis obtained the hub genes: IL-7R. ROC analysis showed that IL-7R had a good diagnostic and predictive effect on MDD and IS patients. The proportions of macrophages M0 and monocytes in patients were significantly higher than those in CON. We constructed PPI network and ceRNA network that related to IL-7R. The perturbagen signatures and computational drug signatures were found that can target IL-7R. The expression of IL-7R in MDD, PSND and PSD patients was lower than that in CON, and the expression of IL-7R in PSD patients was lower than that in PSND patients.
These findings indicate that IL-7R may serve as a diagnostic marker to distinguish between PSD and PSND patients, and targeting IL-7R as a therapeutic target could potentially improve treatment outcomes for PSD.
卒中后抑郁(PSD)是卒中后最常见的神经精神并发症。炎症理论表明,PSD可能与过度活跃的炎症反应有关。然而,关于PSD中炎症相关指标的研究结果仍然不一致且难以捉摸。本研究旨在筛选有助于区分PSD和卒中后非抑郁(PSND)患者的诊断标志物。
使用两个GEO数据集,包括重度抑郁症(MDD)患者和对照组(CON,GSE98793)、缺血性卒中(IS)患者和CON(GSE16561),分析差异表达基因(DEG)并进行富集分析。使用蛋白质-蛋白质相互作用(PPI)网络和随机森林分析筛选候选枢纽基因。进行CIBERSORT分析免疫浸润情况。我们使用String数据库分析与枢纽基因相互作用的蛋白质,使用RNAInter、miRWalk、miRDB和Starbase数据库分析枢纽基因的circRNA-miRNA-mRNA ceRNA网络,以及使用DSigDB数据库分析调节枢纽基因的药物。我们通过定量实时PCR从患者和对照组的血液中进一步验证枢纽基因的表达。
从筛选出的394个DEG中,发现这些DEG主要与免疫反应激活有关。PPI网络和随机森林分析获得枢纽基因:IL-7R。ROC分析表明,IL-7R对MDD和IS患者具有良好的诊断和预测作用。患者中巨噬细胞M0和单核细胞的比例显著高于对照组。我们构建了与IL-7R相关的PPI网络和ceRNA网络。发现了可靶向IL-7R的干扰物特征和计算药物特征。MDD、PSND和PSD患者中IL-7R的表达低于对照组,且PSD患者中IL-7R的表达低于PSND患者。
这些发现表明,IL-7R可能作为区分PSD和PSND患者的诊断标志物,将IL-7R作为治疗靶点可能会改善PSD的治疗效果。