Identification of Biomarkers Related to M2 Macrophage Infiltration in Alzheimer’s Disease

Lin, Caixiu; Xu, Congcong; Zhou, Yongji; Chen, Anqi; Jin, Baiye

doi:10.3390/cells11152365

Cited by 8 publications

(7 citation statements)

References 45 publications

(51 reference statements)

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“…Studies have shown that both microglia and astrocytes, as well as peripheral immune cells, are involved in neuroinflammation associated with AD ( Leng and Edison, 2021 ). Therefore, we investigated immune infiltration in AD, we found that M2 macrophages’ infiltration was significantly increased in AD, which is consistent with previous study ( Lin et al, 2022 ). In addition, neutrophils and monocytes were also upregulated in AD.…”

Section: Discussionsupporting

confidence: 90%

Identification ferroptosis-related hub genes and diagnostic model in Alzheimer’s disease

Zhao,

Wang,

et al. 2023

Front. Mol. Neurosci.

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BackgroundFerroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer’s disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD).MethodsThe microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R “limma” package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes.ResultsWe identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened.ConclusionThese results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.

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Section: Discussionsupporting

confidence: 90%

Identification ferroptosis-related hub genes and diagnostic model in Alzheimer’s disease

Zhao,

Wang,

et al. 2023

Front. Mol. Neurosci.

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“…Thus, this methodology is more of a holistic methodology that measures the co-expressed gene modules among samples and explores the linkages between these modules and sample traits. The performance of WGCNA in hub discovery for AD and other neurological disorders was well supported by the literature (Mukherjee, 2021;Lin et al, 2022). However, we must also realize that there are limitations in WGCNA analysis: 1.…”

Section: Discussionmentioning

confidence: 73%

Identification of novel hub genes for Alzheimer’s disease associated with the hippocampus using WGCNA and differential gene analysis

Chen,

Li,

et al. 2024

Front. Neurosci.

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BackgroundAlzheimer’s disease (AD) is a common, refractory, progressive neurodegenerative disorder in which cognitive and memory deficits are highly correlated with abnormalities in hippocampal brain regions. There is still a lack of hippocampus-related markers for AD diagnosis and prevention.MethodsDifferently expressed genes were identified in the gene expression profile GSE293789 in the hippocampal brain region. Enrichment analyses GO, KEGG, and GSEA were used to identify biological pathways involved in the DEGs and AD-related group. WGCNA was used to identify the gene modules that are highly associated with AD in the samples. The intersecting genes of the genes in DEGs and modules were extracted and the top ten ranked hub genes were identified. Finally GES48350 was used as a validation cohort to predict the diagnostic efficacy of hub genes.ResultsFrom GSE293789, 225 DEGs were identified, which were mainly associated with calcium response, glutamatergic synapses, and calcium-dependent phospholipid-binding response. WGCNA analysis yielded dark green and bright yellow modular genes as the most relevant to AD. From these two modules, 176 genes were extracted, which were taken to be intersected with DEGs, yielding 51 intersecting genes. Then 10 hub genes were identified in them: HSPA1B, HSPB1, HSPA1A, DNAJB1, HSPB8, ANXA2, ANXA1, SOX9, YAP1, and AHNAK. Validation of these genes was found to have excellent diagnostic performance.ConclusionTen AD-related hub genes in the hippocampus were identified, contributing to further understanding of AD development in the hippocampus and development of targets for therapeutic prevention.

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“…A variety of immune cells were signi cantly in ltrated in Cluster 2, and multiple biological processes related to synapse formation were signi cantly upregulated in Cluster 2. M2 macrophages have signi cant anti-in ammatory properties and can improve cognitive dysfunction in AD model mice [24], and increased synapse formation is associated with improved cognitive function [25]. Based on this, we speculate that the AD process of Cluster 2 patients may be suppressed, resulting in a better prognosis.…”

Section: Discussionmentioning

confidence: 86%

Identification of pyroptosis-related molecular clusters in alzheimer’s disease based on multiple machine learning models

Fan

Niu

Wang

et al. 2023

Preprint

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Background: Alzheimer's disease (AD) is the most common neurodegenerative disease. Pyroptosis is a new type of programmed cell death, which can lead to the progression of various diseases. The aim of this study was to explore the role of pyroptosis-related genes (PRGs) in Alzheimer's disease and to build the predictive model. Methods: The expression of PRGs in AD was analyzed based on the GSE33000 dataset, and molecular clustering and immune microenvironment analysis were performed on 310 patient samples. The WGCNA algorithm was used to identify the genes that were specifically expressed between different clusters, and then four machine learning models (RF, GLM, SVM and XGB) were used to construct the predictive models for the risk of AD. The prediction capability of the model was verified by nomogram, calibration, decision curve analyses and five external data sets. Results: Multiple PRGs were differentially expressed between AD and normal brain tissue. Based on differentially expressed PRGs, 310 AD patients were divided into two subtypes by consistent clustering. Immune microenvironment analysis showed significant differences in the degree of immune activation among different subtypes. WGCNA algorithm identified the specific genes between AD and normal individuals, Cluster 1 and Cluster 2. The SVM model has the best prediction performance with low residual error and root mean square error, and high area under ROC curve (AUC=0.933). Finally, a prediction model based on five genes (GPR4, STAT3, CASP4, CLIC1 and TNFRSF10B) was constructed and showed satisfactory performance on five externally validated data sets. Nomogram, calibration curve and decision curve analysis proved the prediction performance of the model. Conclusions: This study systematically analyzed the complex relationship between PRGs and AD, and constructed a good prediction model to distinguish AD from normal individuals, which is expected to provide reference for related research.

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Identification of Biomarkers Related to M2 Macrophage Infiltration in Alzheimer’s Disease

Cited by 8 publications

References 45 publications

Identification ferroptosis-related hub genes and diagnostic model in Alzheimer’s disease

Identification ferroptosis-related hub genes and diagnostic model in Alzheimer’s disease

Identification of novel hub genes for Alzheimer’s disease associated with the hippocampus using WGCNA and differential gene analysis

Identification of pyroptosis-related molecular clusters in alzheimer’s disease based on multiple machine learning models

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