Li Shengjie, Xiao Jinting, Yu Zaiyang, Li Junliang, Shang Hao, Zhang Lei
Nanchang University, Nanchang, China.
Department of Neurosurgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Heliyon. 2023 Mar 11;9(3):e14470. doi: 10.1016/j.heliyon.2023.e14470. eCollection 2023 Mar.
To identify potential immune-related biomarkers, molecular mechanism, and therapeutic agents of intracranial aneurysms (IAs).
We identified the differentially expressed genes (DEGs) between IAs and control samples from GSE75436, GSE26969, GSE6551, and GSE13353 datasets. We used weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) analysis to identify immune-related hub genes. We evaluated the expression of hub genes by using qRT-PCR analysis. Using miRNet, NetworkAnalyst, and DGIdb databases, we analyzed the regulatory networks and potential therapeutic agents targeting hub genes. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to identify optimal biomarkers among hub genes. The diagnostic value was validated by external GSE15629 dataset.
We identified 227 DEGs and 22 differentially infiltrating immune cells between IAs and control samples from GSE75436, GSE26969, GSE6551, and GSE13353 datasets. We further identified 41 differentially expressed immune-related genes (DEIRGs), which were primarily enriched in the chemokine-mediated signaling pathway, myeloid leukocyte migration, endocytic vesicle membrane, chemokine receptor binding, chemokine activity, and viral protein interactions with cytokines and their receptors. Among 41 DEIRGs, 10 hub genes including C3AR1, CD163, CCL4, CXCL8, CCL3, TLR2, TYROBP, C1QB, FCGR3A, and FCGR1A were identified with good diagnostic values (AUC >0.7). Hsa-mir-27a-3p and transcription factors, including YY1 and GATA2, were identified the primary regulators of hub genes. 92 potential therapeutic agents targeting hub genes were predicted. C3AR1 and CD163 were finally identified as the best diagnostic biomarkers using LASSO logistic regression (AUC = 0.994). The diagnostic value of C3AR1 and CD163 was validated by the external GSE15629 dataset (AUC = 0.914).
This study revealed the importance of C3AR1 and CD163 in immune infiltration in IAs pathogenesis. Our finding provided a valuable reference for subsequent research on the potential targets for molecular mechanisms and intervention of IAs.
识别颅内动脉瘤(IA)潜在的免疫相关生物标志物、分子机制和治疗药物。
我们从GSE75436、GSE26969、GSE6551和GSE13353数据集中识别IA与对照样本之间的差异表达基因(DEG)。我们使用加权基因共表达网络分析(WGCNA)和蛋白质-蛋白质相互作用(PPI)分析来识别免疫相关的核心基因。我们通过qRT-PCR分析评估核心基因的表达。使用miRNet、NetworkAnalyst和DGIdb数据库,我们分析了针对核心基因的调控网络和潜在治疗药物。进行最小绝对收缩和选择算子(LASSO)逻辑回归以在核心基因中识别最佳生物标志物。通过外部GSE15629数据集验证诊断价值。
我们从GSE75436、GSE26969、GSE6551和GSE13353数据集中识别出IA与对照样本之间的227个DEG和22种差异浸润免疫细胞。我们进一步识别出41个差异表达的免疫相关基因(DEIRG),它们主要富集于趋化因子介导的信号通路、髓样白细胞迁移、内吞囊泡膜、趋化因子受体结合、趋化因子活性以及病毒蛋白与细胞因子及其受体的相互作用。在41个DEIRG中,识别出10个具有良好诊断价值(AUC>0.7)的核心基因,包括C3AR1、CD163、CCL4、CXCL8、CCL3、TLR2、TYROBP、C1QB、FCGR3A和FCGR1A。已确定hsa-mir-27a-3p以及包括YY1和GATA2在内的转录因子是核心基因的主要调节因子。预测了92种针对核心基因的潜在治疗药物。使用LASSO逻辑回归最终确定C3AR1和CD163为最佳诊断生物标志物(AUC = 0.994)。C3AR1和CD163的诊断价值通过外部GSE15629数据集得到验证(AUC = 0.914)。
本研究揭示了C3AR1和CD163在IA发病机制免疫浸润中的重要性。我们的发现为后续关于IA分子机制和干预潜在靶点的研究提供了有价值的参考。