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

Abstract

ObjectiveAs 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.MethodsThe 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.ResultsA 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.ConclusionMAFF, 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.

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

“…Using the GEO database, researchers have identified in recent years numerous hub genes that are differentially expressed in AD and control brain samples and have further determined many possible diagnostic biomarkers of AD using the ROC prediction model. Tian et al (2022) identified three hub genes ( MAFF , ADCYAP1 , and ZFP36L1 ; AUC = 0.850) and verified their expression in the AD brain (AUC = 0.935). Wu et al (2021) found 10 hub genes, namely SERPINE1 , ZBTB16 , CD44 , BCL6 , HMOX1 , SLC11A1 , CEACAM8 , ITGA5 , SOCS3 , and IL4R , all of which have good diagnostic value (AUC > 0.75).…”

Identification of the shared genes in type 2 diabetes mellitus and osteoarthritis and the role of quercetin

“…It is reported that PPP1R15A inactivation could increase insulin resistance 31 and play a proinflammatory role through endoplasmic reticulum stress 32 . The decrease of MAFF can inhibit the expression of antioxidant genes and increase the apoptosis of β cells induced by oxidative stress, 33 while the overexpression of MAFF can inhibit the transcription of NF‐E2‐related factors, which leads to immune and inflammatory reactions 34 . GADD45B can be used as a prognostic biomarker in the diagnosis of DN, 35 and it has been proved that the expression of GADD45B is downregulated in synovial tissue of OA 36 .…”

“…32 The decrease of MAFF can inhibit the expression of antioxidant genes and increase the apoptosis of β cells induced by oxidative stress, 33 while the overexpression of MAFF can inhibit the transcription of NF-E2-related factors, which leads to immune and inflammatory reactions. 34 GADD45B can be used as a prognostic biomarker in the diagnosis of DN, 35 and it has been proved that the expression of GADD45B is downregulated in synovial tissue of OA. 36 KLF4 can inhibit inflammation and bone destruction in OA, 37 and inhibition of KLF4 expression can promote the conduction of the NF-kB signal pathway and then improve the insulin resistance of macrophages.…”

Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning

“…Our research comprehensively explored 28 immune cell subsets and various immune-modulators in the AD immune microenvironment, and based on the advantages of machine learning in clinical applications, we finally identified six key immune cell subtypes (plasmacytoid dendritic cell, type 17 T helper cell, immature B cell, natural killer cell, MDSC, and neutrophil) and five vital immune genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that can accurately predict AD progression, some of which have never been reported in AD before. In addition, recent studies only depict the expression landscape of immune cell subsets or immune-related genes based on small sample sizes, and lack more in-depth studies ( 50 , 70 , 71 ). What’s more, the immune-related molecular subtypes in AD patients also remain unknown and need further clarification.…”