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

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 GSE109...

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Autores principales: Tian, Yu, Lu, Yaoheng, Cao, Yuze, Dang, Chun, Wang, Na, Tian, Kuo, Luo, Qiqi, Guo, Erliang, Luo, Shanshun, Wang, Lihua, Li, Qian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372364/
https://www.ncbi.nlm.nih.gov/pubmed/35966794
http://dx.doi.org/10.3389/fnagi.2022.919614
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author Tian, Yu
Lu, Yaoheng
Cao, Yuze
Dang, Chun
Wang, Na
Tian, Kuo
Luo, Qiqi
Guo, Erliang
Luo, Shanshun
Wang, Lihua
Li, Qian
author_facet Tian, Yu
Lu, Yaoheng
Cao, Yuze
Dang, Chun
Wang, Na
Tian, Kuo
Luo, Qiqi
Guo, Erliang
Luo, Shanshun
Wang, Lihua
Li, Qian
author_sort Tian, Yu
collection PubMed
description 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.
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spelling pubmed-93723642022-08-13 Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies Tian, Yu Lu, Yaoheng Cao, Yuze Dang, Chun Wang, Na Tian, Kuo Luo, Qiqi Guo, Erliang Luo, Shanshun Wang, Lihua Li, Qian Front Aging Neurosci Neuroscience 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. Frontiers Media S.A. 2022-07-29 /pmc/articles/PMC9372364/ /pubmed/35966794 http://dx.doi.org/10.3389/fnagi.2022.919614 Text en Copyright © 2022 Tian, Lu, Cao, Dang, Wang, Tian, Luo, Guo, Luo, Wang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Tian, Yu
Lu, Yaoheng
Cao, Yuze
Dang, Chun
Wang, Na
Tian, Kuo
Luo, Qiqi
Guo, Erliang
Luo, Shanshun
Wang, Lihua
Li, Qian
Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
title Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
title_full Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
title_fullStr Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
title_full_unstemmed Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
title_short Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
title_sort identification of diagnostic signatures associated with immune infiltration in alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372364/
https://www.ncbi.nlm.nih.gov/pubmed/35966794
http://dx.doi.org/10.3389/fnagi.2022.919614
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