Zhang Haofuzi, Sun Jidong, Zou Peng, Huang Yutao, Yang Qiuzi, Zhang Zhuoyuan, Luo Peng, Jiang Xiaofan
Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Biochemistry and Molecular Biology, College of Life Science, Northwest University, Xi'an, China.
Heliyon. 2024 Feb 7;10(4):e25866. doi: 10.1016/j.heliyon.2024.e25866. eCollection 2024 Feb 29.
The immune microenvironment and hypoxia play crucial roles in the pathophysiology of ischemic stroke (IS). Hence, in this study, we aimed to identify hypoxia- and immune-related biomarkers in IS.
The IS microarray dataset GSE16561 was examined to determine differentially expressed genes (DEGs) utilizing bioinformatics-based analysis. The intersection of hypoxia-related genes and DEGs was conducted to identify differentially expressed hypoxia-related genes (DEHRGs). Then, using weighted correlation network analysis (WGCNA), all of the genes in GSE16561 dataset were examined to create a co-expression network, and module-clinical trait correlations were examined for the purpose of examining the genes linked to immune cells. The immune-related DEHRGs were submitted to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was constructed by Cytoscape plugin MCODE, in order to extract hub genes. The miRNet was used to predict hub gene-related transcription factors (TFs) and miRNAs. Finally, a diagnostic model was developed by least absolute shrinkage and selection operator (LASSO) logistic regression.
Between the control and IS samples, 4171 DEGs were found. Thereafter, the intersection of hypoxia-related genes and DEGs was conducted to obtain 45 DEHRGs. Ten significantly differentially infiltrated immune cells were found-namely, CD56dim natural killer cells, activated CD8 T cells, activated dendritic cells, activated B cells, central memory CD8 T cells, effector memory CD8 T cells, natural killer cells, gamma delta T cells, plasmacytoid dendritic cells, and neutrophils-between IS and control samples. Subsequently, we identified 27 immune-related DEHRGs through the intersection of DEHRGs and genes in important modules of WGCNA. The immune-related DEHRGs were primarily enriched in response to hypoxia, cellular polysaccharide metabolic process, response to decreased oxygen levels, polysaccharide metabolic process, lipid and atherosclerosis, and HIF-1 signaling pathway H. Using MCODE, FOS, DDIT3, DUSP1, and NFIL3 were found to be hub genes. In the validation cohort and training set, the AUC values of the diagnostic model were 0.9188034 and 0.9395085, respectively.
In brief, we identified and validated four hub genes-FOS, DDIT3, DUSP1, and NFIL3-which might be involved in the pathological development of IS, potentially providing novel perspectives for the diagnosis and treatment of IS.
免疫微环境和缺氧在缺血性脑卒中（IS）的病理生理过程中起关键作用。因此，在本研究中，我们旨在识别IS中与缺氧和免疫相关的生物标志物。
利用基于生物信息学的分析方法检查IS微阵列数据集GSE16561，以确定差异表达基因（DEG）。进行缺氧相关基因与DEG的交集分析，以识别差异表达的缺氧相关基因（DEHRG）。然后，使用加权基因共表达网络分析（WGCNA）检查GSE16561数据集中的所有基因，以创建共表达网络，并检查模块与临床特征的相关性，以研究与免疫细胞相关的基因。将免疫相关的DEHRG提交至基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。通过Cytoscape插件MCODE构建蛋白质-蛋白质相互作用（PPI）网络，以提取枢纽基因。使用miRNet预测枢纽基因相关的转录因子（TF）和微小RNA（miRNA）。最后，通过最小绝对收缩和选择算子（LASSO）逻辑回归建立诊断模型。
在对照样本和IS样本之间，发现了4171个DEG。此后，进行缺氧相关基因与DEG的交集分析，获得45个DEHRG。在IS样本和对照样本之间发现了10种显著差异浸润的免疫细胞，即CD56dim自然杀伤细胞、活化的CD8 T细胞、活化的树突状细胞、活化的B细胞、中枢记忆CD8 T细胞、效应记忆CD8 T细胞、自然杀伤细胞、γδ T细胞、浆细胞样树突状细胞和中性粒细胞。随后，我们通过DEHRG与WGCNA重要模块中的基因的交集，鉴定出27个免疫相关的DEHRG。免疫相关的DEHRG主要富集于对缺氧的反应、细胞多糖代谢过程、对氧水平降低的反应、多糖代谢过程、脂质与动脉粥样硬化以及缺氧诱导因子-1（HIF-1）信号通路。使用MCODE发现，FOS、DDIT3、DUSP1和NFIL3为枢纽基因。在验证队列和训练集中，诊断模型的曲线下面积（AUC）值分别为0.9188034和0.9395085。
简而言之，我们鉴定并验证了四个枢纽基因——FOS、DDIT3、DUSP1和NFIL3，它们可能参与IS的病理发展，可能为IS的诊断和治疗提供新的视角。
