Qiu Huaide, Shen Likui, Shen Ying, Mao Yiming
Faculty of Rehabilitation Science, Nanjing Normal University of Special Education, Nanjing, China.
Department of Neurosurgery, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, Jiangsu, China.
Front Neurol. 2023 Jul 17;14:1096911. doi: 10.3389/fneur.2023.1096911. eCollection 2023.
The study aimed to explore the miRNA and mRNA biomarkers in post-stroke depression (PSD) and to develop a miRNA-mRNA regulatory network to reveal its potential pathogenesis.
The transcriptomic expression profile was obtained from the GEO database using the accession numbers GSE117064 (miRNAs, stroke vs. control) and GSE76826 [mRNAs, late-onset major depressive disorder (MDD) vs. control]. Differentially expressed miRNAs (DE-miRNAs) were identified in blood samples collected from stroke patients vs. control using the Linear Models for Microarray Data (LIMMA) package, while the weighted correlation network analysis (WGCNA) revealed co-expressed gene modules correlated with the subject group. The intersection between DE-miRNAs and miRNAs identified by WGCNA was defined as stroke-related miRNAs, whose target mRNAs were stroke-related genes with the prediction based on three databases (miRDB, miRTarBase, and TargetScan). Using the GSE76826 dataset, the differentially expressed genes (DEGs) were identified. Overlapped DEGs between stroke-related genes and DEGs in late-onset MDD were retrieved, and these were potential mRNA biomarkers in PSD. With the overlapped DEGs, three machine-learning methods were employed to identify gene signatures for PSD, which were established with the intersection of gene sets identified by each algorithm. Based on the gene signatures, the upstream miRNAs were predicted, and a miRNA-mRNA network was constructed.
Using the GSE117064 dataset, we retrieved a total of 667 DE-miRNAs, which included 420 upregulated and 247 downregulated ones. Meanwhile, WGCNA identified two modules (blue and brown) that were significantly correlated with the subject group. A total of 117 stroke-related miRNAs were identified with the intersection of DE-miRNAs and WGCNA-related ones. Based on the miRNA-mRNA databases, we identified a list of 2,387 stroke-related genes, among which 99 DEGs in MDD were also embedded. Based on the 99 overlapped DEGs, we identified three gene signatures (SPATA2, ZNF208, and YTHDC1) using three machine-learning classifiers. Predictions of the three mRNAs highlight four miRNAs as follows: miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p. Subsequently, a miRNA-mRNA network was developed.
The study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.
本研究旨在探索中风后抑郁症（PSD）中的miRNA和mRNA生物标志物，并构建一个miRNA-mRNA调控网络以揭示其潜在的发病机制。
使用登录号GSE117064（miRNAs，中风患者与对照）和GSE76826 [mRNAs，迟发性重度抑郁症（MDD）与对照]从GEO数据库中获取转录组表达谱。使用微阵列数据线性模型（LIMMA）软件包在中风患者与对照采集的血液样本中鉴定差异表达的miRNA（DE-miRNA），而加权基因共表达网络分析（WGCNA）揭示与研究组相关的共表达基因模块。将DE-miRNA与通过WGCNA鉴定的miRNA之间的交集定义为中风相关miRNA，其靶mRNA是基于三个数据库（miRDB、miRTarBase和TargetScan）预测的中风相关基因。使用GSE76826数据集鉴定差异表达基因（DEG）。检索中风相关基因与迟发性MDD中的DEG之间的重叠DEG，这些是PSD中潜在的mRNA生物标志物。利用这些重叠的DEG，采用三种机器学习方法鉴定PSD的基因特征，这些特征通过每种算法鉴定的基因集的交集来确定。基于这些基因特征，预测上游miRNA，并构建miRNA-mRNA网络。
使用GSE117064数据集，我们共检索到667个DE-miRNA，其中420个上调，247个下调。同时，WGCNA鉴定出两个与研究组显著相关的模块（蓝色和棕色）。通过DE-miRNA与WGCNA相关miRNA的交集，共鉴定出117个中风相关miRNA。基于miRNA-mRNA数据库，我们鉴定出2387个中风相关基因列表，其中还包含99个MDD中的DEG。基于这99个重叠的DEG，我们使用三种机器学习分类器鉴定出三个基因特征（SPATA2、ZNF208和YTHDC1）。对这三个mRNA的预测突出了四个miRNA，分别为：miR-6883-5p、miR-6873-3p、miR-4776-3p和miR-6738-3p。随后，构建了一个miRNA-mRNA网络。
本研究突出了PSD的基因特征，涉及三个基因（SPATA2、ZNF208和YTHDC1）和四个上游miRNA（miR-6883-5p、miR-6873-3p、miR-4776-3p和miR-6738-3p）。这些生物标志物有助于我们进一步了解PSD的发病机制。
