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通过整合生物信息学分析和机器学习策略鉴定与阿尔茨海默病免疫浸润相关的诊断特征

Identification of diagnostic signatures associated with immune infiltration in Alzheimer's disease by integrating bioinformatic analysis and machine-learning strategies.

作者信息

Tian Yu, Lu Yaoheng, Cao Yuze, Dang Chun, Wang Na, Tian Kuo, Luo Qiqi, Guo Erliang, Luo Shanshun, Wang Lihua, Li Qian

机构信息

Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

Department of Gerontology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.

出版信息

Front Aging Neurosci. 2022 Jul 29;14:919614. doi: 10.3389/fnagi.2022.919614. eCollection 2022.

Abstract

OBJECTIVE

As a chronic neurodegenerative disorder, Alzheimer's disease (AD) is the most common form of progressive dementia. The purpose of this study was to identify diagnostic signatures of AD and the effect of immune cell infiltration in this pathology.

METHODS

The expression profiles of GSE109887, GSE122063, GSE28146, and GSE1297 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between AD and control brain samples. Functional enrichment analysis was performed to reveal AD-associated biological functions and key pathways. Besides, we applied the Least Absolute Shrinkage Selection Operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analysis to screen potential diagnostic feature genes in AD, which were further tested in AD brains of the validation cohort (GSE5281). The discriminatory ability was then assessed by the area under the receiver operating characteristic curves (AUC). Finally, the CIBERSORT algorithm and immune cell infiltration analysis were employed to assess the inflammatory state of AD.

RESULTS

A total of 49 DEGs were identified. The functional enrichment analysis revealed that leukocyte transendothelial migration, cytokine receptor interaction, and JAK-STAT signaling pathway were enriched in the AD group. MAF basic leucine zipper transcription factor F (MAFF), ADCYAP1, and ZFP36L1 were identified as the diagnostic biomarkers of AD with high discriminatory ability (AUC = 0.850) and validated in AD brains (AUC = 0.935). As indicated from the immune cell infiltration analysis, naive B cells, plasma cells, activated/resting NK cells, M0 macrophages, M1 macrophages, resting CD4 T memory cells, resting mast cells, memory B cells, and resting/activated dendritic cells may participate in the development of AD. Additionally, all diagnostic signatures presented different degrees of correlation with different infiltrating immune cells.

CONCLUSION

MAFF, ADCYAP1, and ZFP36L1 may become new candidate biomarkers of AD, which were closely related to the pathogenesis of AD. Moreover, the immune cells mentioned above may play crucial roles in disease occurrence and progression.

摘要

目的

作为一种慢性神经退行性疾病,阿尔茨海默病(AD)是进行性痴呆最常见的形式。本研究旨在确定AD的诊断特征以及免疫细胞浸润在此病理过程中的作用。

方法

从基因表达综合数据库(GEO)下载GSE109887、GSE122063、GSE28146和GSE1297的表达谱,以获取AD与对照脑样本之间的差异表达基因(DEG)。进行功能富集分析以揭示与AD相关的生物学功能和关键途径。此外,我们应用最小绝对收缩选择算子(LASSO)和支持向量机递归特征消除(SVM-RFE)分析来筛选AD中潜在的诊断特征基因,并在验证队列(GSE5281)的AD脑中进行进一步测试。然后通过受试者操作特征曲线(AUC)下的面积评估鉴别能力。最后,采用CIBERSORT算法和免疫细胞浸润分析来评估AD的炎症状态。

结果

共鉴定出49个DEG。功能富集分析显示,白细胞跨内皮迁移、细胞因子受体相互作用和JAK-STAT信号通路在AD组中富集。MAF碱性亮氨酸拉链转录因子F(MAFF)、ADCYAP1和ZFP36L1被鉴定为具有高鉴别能力(AUC = 0.850)的AD诊断生物标志物,并在AD脑中得到验证(AUC = 0.935)。免疫细胞浸润分析表明,幼稚B细胞、浆细胞、活化/静息自然杀伤细胞、M0巨噬细胞、M1巨噬细胞、静息CD4 T记忆细胞、静息肥大细胞、记忆B细胞以及静息/活化树突状细胞可能参与AD的发生发展。此外,所有诊断特征与不同浸润免疫细胞呈现不同程度的相关性。

结论

MAFF、ADCYAP1和ZFP36L1可能成为AD的新候选生物标志物,它们与AD的发病机制密切相关。此外,上述免疫细胞可能在疾病发生和进展中起关键作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0861/9372364/c99ed9e77352/fnagi-14-919614-g001.jpg

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