Zhang Lu, Lv YuJing, Ma Mengqing, Lv Jile, Chen Jie, Lei Shang, Man Yi, Xing Guimei, Wang Yu
Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Neurology, Anhui No. 2 Provincial People's Hospital, Hefei, China.
Front Neurosci. 2025 Apr 30;19:1479616. doi: 10.3389/fnins.2025.1479616. eCollection 2025.
Some studies indicated that histone modification may be involved in depression disorder (DD). The maintenance of the histone acetylation state is the work of histone acetyltransferase (HAT) and histone deacetylase (HDAC), which is thought to be a potential diagnostic biomarker of depression. However, it is still unknown how histone acetylation-related genes (HAC-RGs) contribute to the onset and progression of DD.
GSE76826 and GSE98793were obtained from the Gene Expression Omnibus (GEO) database, HAC-RGs were acquired from the GeneCards database. Initially, the differentially expressed genes (DEGs) in GSE76826 were investigated. We used weighted gene co-expression network analysis (WGCNA) to screen key module genes. Candidate genes were selected by intersecting DEGs, key module genes, and HAC-RGs, followed by functional analysis. Two machine learning algorithms were used to identify hub genes, which were used for drug prediction, immunological infiltration studies, nomogram construction, and regulatory network building. The expression levels were verified using the GSE76826 and GSE98793 datasets. Hub gene expression levels in the clinical samples were verified using reverse transcription quantitative PCR (RT-qPCR).
The 23 candidate genes were obtained by intersecting 2,316 DEGs, 1,010 HAC-RGs and 2,617 key module genes. Three hub genes (, , and ) were gained by two machine learning algorithms. The nomogram constructed based on these three hub genes showed high predictive accuracy. Additionally, the three hub genes were enriched in the kegg_ribosome. The 9 different immune cells were identified in GSE76826, which were associated with three hub genes. A hub gene-drug network (98 nodes, 106 edges) and an lncRNA-miRNA-mRNA network (56 nodes, 87 edges), were built using the database. The expression level verification indicated that, with the exception of the KPNB1 gene, the DD group had higher levels of JDP2 and ALOX5 and that the expression patterns in GSE76826 and GSE98793 were consistent, with RT-qPCR confirming higher ALOX5 and JDP2 expression in DD samples.
This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.
一些研究表明，组蛋白修饰可能与抑郁症（DD）有关。组蛋白乙酰化状态的维持是组蛋白乙酰转移酶（HAT）和组蛋白去乙酰化酶（HDAC）的工作，它们被认为是抑郁症的潜在诊断生物标志物。然而，组蛋白乙酰化相关基因（HAC-RGs）如何促成DD的发生和发展仍不清楚。
从基因表达综合数据库（GEO）中获取GSE76826和GSE98793，从基因卡片数据库中获取HAC-RGs。最初，研究了GSE76826中的差异表达基因（DEGs）。我们使用加权基因共表达网络分析（WGCNA）来筛选关键模块基因。通过将DEGs、关键模块基因和HAC-RGs相交来选择候选基因，随后进行功能分析。使用两种机器学习算法来识别枢纽基因，这些基因用于药物预测、免疫浸润研究、列线图构建和调控网络构建。使用GSE76826和GSE98793数据集验证表达水平。使用逆转录定量PCR（RT-qPCR）验证临床样本中的枢纽基因表达水平。
通过将2316个DEGs、1010个HAC-RGs和2617个关键模块基因相交，获得了23个候选基因。通过两种机器学习算法获得了三个枢纽基因（、和）。基于这三个枢纽基因构建的列线图显示出较高的预测准确性。此外，这三个枢纽基因在kegg_核糖体中富集。在GSE76826中鉴定出9种不同的免疫细胞，它们与三个枢纽基因相关。使用数据库构建了一个枢纽基因-药物网络（98个节点，106条边）和一个lncRNA-miRNA-mRNA网络（56个节点，87条边）。表达水平验证表明，除KPNB1基因外，DD组的JDP2和ALOX5水平较高，并且GSE76826和GSE98793中的表达模式一致，RT-qPCR证实DD样本中ALOX5和JDP2表达较高。
本研究鉴定了三个与组蛋白乙酰化相关的枢纽基因（JDP2、ALOX5和KPNB1），为DD的诊断和治疗提供了新的见解。
