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Integrated Bioinformatic Analysis and Validation Identifies Immune Microenvironment-Related Potential Biomarkers in Alzheimer’s Disease

  • Original Research
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The Journal of Prevention of Alzheimer's Disease Aims and scope Submit manuscript

Abstract

Background

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases, accompanied by cognitive and memory impairment, accounting for about 60% - 80% of dementia types. The pathogenesis of AD has not been clarified, and there is no effective therapy to prevent or treat AD. In this study, we aimed to identify the potential biomarkers involved in the brain immune microenvironment in AD.

Methods

AD datasets from GEO database were obtained to identify the differentially expressed disease-related genes (DEDRGs) in AD through weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Functional Enrichment analysis was performed to explore the potential biological function of DEDRGs. The hub DEDRGs were identified through the protein-protein interaction (PPI) network. Furthermore, the CIBERSORT algorithm was employed to bulk gene expression profiles of AD to depict the immune microenvironment characteristics in AD. Pearson’s correlation analysis was utilized to depict the correlation between each of immune cells and hub DEDRGs.

Results

A total of 27 DEDRGs were identified through WGCNA and differential expression analysis. Functional enrichment analysis of 27 DEDRGs indicated that chemokine signaling pathway was the most significantly enriched KEGG pathway, response to biotic stimulus was the most significantly enriched GO term, and most of DEDRGs were enriched into urinary system cancer in DO analysis. 6 hub DEDRGs, ANGPT1, CCL2, CD44, CXCR4, GJA1 and VCAM1, were screened through PPI network and all of them were up-regulated in AD. Immune infiltration analysis revealed that there were higher infiltration levels of T cells CD4 memory activated, T cells gamma delta, NK cells resting and macrophages M0, and lower infiltration level of NK cell activated in AD, and macrophages M2 owned the highest positively association with VCAM1 and CXCR4, but VCAM1 was statistically and negatively correlated to T cells CD8.

Conclusion

Our study identified 6 hub DEDRGs, ANGPT1, CCL2, CD44, CXCR4, GJA1 and VCAM1, were statistically associated with immune infiltrating cells, and were significantly related to the pathological development of AD, which may provide a theoretical basis for developing potential biomarkers and implementing effective therapies against AD.

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Abbreviations

Aβ:

amyloid β

AD:

Alzheimer’s disease

DEDRGs:

differentially expressed disease-related genes

WGCNA:

weighted gene co-expression network analysis

PPI:

protein-protein interaction

NFTs:

neurofibrillary tangles

SP:

senile plaque

ADI:

Alzheimer’s Disease International

CNS:

central nervous system

TOM:

topological overlap matrix

DEGs:

differentially expressed genes

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GO:

Gene Ontology

BP:

biological process

CC:

cellular component

MF:

molecular functio

DO:

disease ontology

MCODE:

Molecular Complex Detection

CMV:

cytomegalovirus

APOE:

apolipoprotein E

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Acknowledgments

We thank the GEO database, AlzData for providing free and public data sets, and we also thank other online platforms such as R software, ggplot2, Cytoscape software, Metascape, String, Sangerbox and Bioinformatics for facilitating data procession and visualization in our study.

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Authors and Affiliations

  1. Department of Geriatric Neurology, Guangdong Provincial Geriatrics Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China

    F. Yang & Shu-wen Xu

  2. Department of Rehabilitation Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China

    N. Zhang

  3. Center for Neuroscience, Shantou University Medical College, Shantou, 515041, China

    G.-Y. Ou

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  1. F. Yang
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Contributions

Author contributions: Data curation, Ni Zhang and Guan-yong Ou; Formal analysis, Fan Yang and Guan-yong Ou; Writing - original draft, Ni Zhang; Writing - review & editing, Fan Yang and Shuwen, Xu. All authors reviewed and approved the final manuscript.

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Correspondence to Shu-wen Xu.

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Competing financial interests: All authors declare no conflict of financial interests.

Ethical standards: GEO belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open-source data, so there are no ethical issues and other conflicts of interest.

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Yang, F., Zhang, N., Ou, GY. et al. Integrated Bioinformatic Analysis and Validation Identifies Immune Microenvironment-Related Potential Biomarkers in Alzheimer’s Disease. J Prev Alzheimers Dis 11, 495–506 (2024). https://doi.org/10.14283/jpad.2024.5

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