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. 2022 Jul 29:14:919614.
doi: 10.3389/fnagi.2022.919614. eCollection 2022.

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

Yu Tian  1   2 , Yaoheng Lu  3 , Yuze Cao  4 , Chun Dang  5 , Na Wang  1 , Kuo Tian  1 , Qiqi Luo  2 , Erliang Guo  1 , Shanshun Luo  2 , Lihua Wang  1 , Qian Li  1
Affiliations

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

Yu Tian et al. Front Aging Neurosci. .

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.

Keywords: Alzheimer’s disease; bioinformatic analysis; diagnostic biomarkers; immune cell infiltration; machine-learning strategies.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The flowchart of the analysis process.
FIGURE 2
FIGURE 2
Differentially expressed genes (DEGs) identified between Alzheimer’s disease and control brain tissues. (A) Volcano plot. (B) Heatmap.
FIGURE 3
FIGURE 3
Enrichment analysis to investigate the potential function of differentially expressed genes (DEGs). (A) GSEA_GO analysis in Alzheimer’s disease (AD) or control group. (B) GSEA_KEGG analysis in AD or control group.
FIGURE 4
FIGURE 4
Screen for potential biomarkers of Alzheimer’s disease (AD) diagnosis. (A) Identified genes using Least Absolute Shrinkage Selection Operator (LASSO) algorithm. (B) The optimal feature biomarkers selection via support vector machine-recursive feature elimination (SVM-RFE) algorithm. (C) Venn diagram displaying one diagnostic marker intersected by LASSO, SVM-RFE algorithms, and differentially expressed genes (DEGs) in AD validated brain.
FIGURE 5
FIGURE 5
Validation of the expression levels of diagnostic biomarkers in the GSE5281 dataset. (A) MAFF expression level. (B) ADCYAP1 expression level. (C) ZFP36L1 expression level.
FIGURE 6
FIGURE 6
Diagnostic effectiveness of feature biomarkers. (A) Receiver operating curve (ROC) curves of candidate biomarkers (MAFF, ADCYAP1, ZFP36L1, and combined) in the training cohort. (B) ROC curves of candidate biomarkers (MAFF, ADCYAP1, ZFP36L1, and combined) in the validation cohort.
FIGURE 7
FIGURE 7
Comparison and correlation of immune cell infiltration. (A) Comparison of 22 infiltrated immune cell subtypes between Alzheimer’s disease (AD) and control brain tissues. Blue and red colors represent normal and AD samples, respectively. (B) Correlation analysis of these 22 immune cell subtypes mutually.
FIGURE 8
FIGURE 8
Correlation analysis between the feature biomarkers and infiltrating immune cells in Alzheimer’s disease (AD). (A) MAFF. (B) ADCYAP1. (C) ZFP36L1.
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