基于生物信息学分析的阿尔茨海默病中铁死亡相关基因的鉴定
Identification of Ferroptosis-Related Genes in Alzheimer's Disease Based on Bioinformatic Analysis.
作者信息
Wang Ying, Chen Guohua, Shao Wei
机构信息
Department of Neurology, Wuhan Hospital of Traditional Chinese and Western Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
出版信息
Front Neurosci. 2022 Feb 7;16:823741. doi: 10.3389/fnins.2022.823741. eCollection 2022.
INTRODUCTION
Alzheimer's disease (AD) is the most prevalent cause of dementia, and emerging evidence suggests that ferroptosis is involved in the pathological process of AD.
MATERIALS AND METHODS
Three microarray datasets (GSE122063, GSE37263, and GSE140829) about AD were collected from the GEO database. AD-related module genes were identified through a weighted gene co-expression network analysis (WGCNA). The ferroptosis-related genes were extracted from FerrDb. The apoptosis-related genes were downloaded from UniProt as a control to show the specificity of ferroptosis. The overlap was performed to obtain the module genes associated with ferroptosis and apoptosis. Then the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and the protein-protein interaction (PPI) were conducted. Cytoscape with CytoHubba was used to identify the hub genes, and the Logistic regression was performed to distinguish the AD patients from controls.
RESULTS
53 ferroptosis-related module genes were obtained. The GO analysis revealed that response to oxidative stress and starvation, and multicellular organismal homeostasis were the most highly enriched terms. The KEGG analysis showed that these overlapped genes were enriched not only in renal cell carcinoma pathways and central carbon metabolism in cancer, but also in autophagy-related pathways and ferroptosis. Ferroptosis-related hub genes in AD (JUN, SLC2A1, TFRC, ALB, and NFE2L2) were finally identified, which could distinguish AD patients from controls (P < 0.05). The area under the ROC curve (AUC) was 0.643. Apoptosis-related hub genes in AD (STAT1, MCL1, and BCL2L11) were also identified and also could distinguish AD patients from controls (P < 0.05). The AUC was 0.608, which was less than the former AUC value, suggesting that ferroptosis was more special than apoptosis in AD.
CONCLUSION
We identified five hub genes (JUN, SLC2A1, TFRC, ALB, and NFE2L2) that are closely associated with ferroptosis in AD and can differentiate AD patients from controls. Three hub genes of apoptosis-related genes in AD (STAT1, MCL1, and BCL2L11) were also identified as a control to show the specificity of ferroptosis. JUN, SLC2A1, TFRC, ALB, and NFE2L2 are thus potential ferroptosis-related biomarkers for disease diagnosis and therapeutic monitoring.
引言
阿尔茨海默病（AD）是痴呆最常见的病因，新出现的证据表明铁死亡参与了AD的病理过程。
材料与方法
从基因表达综合数据库（GEO数据库）收集了3个关于AD的微阵列数据集（GSE122063、GSE37263和GSE140829）。通过加权基因共表达网络分析（WGCNA）鉴定与AD相关的模块基因。从铁死亡数据库（FerrDb）中提取铁死亡相关基因。从通用蛋白质数据库（UniProt）下载凋亡相关基因作为对照，以显示铁死亡的特异性。进行重叠分析以获得与铁死亡和凋亡相关的模块基因。然后进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析以及蛋白质-蛋白质相互作用（PPI）分析。使用带有CytoHubba的Cytoscape软件来鉴定枢纽基因，并进行逻辑回归分析以区分AD患者和对照组。
结果
获得了53个与铁死亡相关的模块基因。GO分析显示，对氧化应激和饥饿的反应以及多细胞生物体稳态是富集程度最高的术语。KEGG分析表明，这些重叠基因不仅在肾细胞癌通路和癌症中的中心碳代谢中富集，还在自噬相关通路和铁死亡中富集。最终鉴定出AD中与铁死亡相关的枢纽基因（JUN、SLC2A1、TFRC、ALB和NFE2L2），它们可以区分AD患者和对照组（P<0.05）。受试者工作特征曲线（ROC）下面积（AUC）为0.643。还鉴定出AD中与凋亡相关的枢纽基因（STAT1、MCL1和BCL2L11），它们也可以区分AD患者和对照组（P<0.05）。AUC为0.608，小于前者的AUC值，表明在AD中铁死亡比凋亡更具特异性。
结论
我们鉴定出5个与AD中铁死亡密切相关且可区分AD患者和对照组的枢纽基因（JUN、SLC2A1、TFRC、ALB和NFE2L2）。还鉴定出AD中凋亡相关基因的3个枢纽基因（STAT1、MCL1和BCL2L11）作为对照，以显示铁死亡的特异性。因此，JUN、SLC2A1、TFRC、ALB和NFE2L2是疾病诊断和治疗监测中潜在的铁死亡相关生物标志物。
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