Evidence map›Paper›PMID 40211218›Full record

ArticleBMC neurology2025

Dysregulated interleukin networks drive immune heterogeneity in Alzheimer's disease: an immunogenomic approach to subgroup classification and predictive modeling.

Bin Zhang, Binglei Xu, Ruoxian Zhang, Baoying Gong, Jianwen Guo

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Article in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Bin ZhangGuangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510405, China.
Binglei XuGuangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510405, China.
Ruoxian ZhangInstitutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Baoying GongGuangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510405, China.
Jianwen GuoThe Second Affiliated Hospital of Guangzhou, Guangdong Provincial Hospital of Traditional Chinese Medicine, University of Chinese Medicine, N. 111 Dade Road, Yuexiu District, Guangzhou City, 510030, Guangdong Province, China. guojw_n@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's Disease (AD) is marked by intricate immunological alterations, including the dysregulation of interleukin signaling. This study investigates the differential expression and potential roles of interleukins and their receptors in AD patients.

methodsWe analyzed the GSE48350 dataset to assess the single-sample Gene Set Enrichment Analysis (ssGSEA) scores for interleukins and their receptors between normal and AD groups. Differentially expressed interleukin-related genes (DIGs) were identified. Enrichment analysis was conducted to understand functional implications. LASSO and logistic regression were used to identify key interleukin genes, which were employed to construct a predictive nomogram. This model was validated using the GSE132903 dataset. Unsupervised clustering and immune cell infiltration analyses were performed to examine AD patient heterogeneity.

resultsThe ssGSEA scores indicated significantly elevated interleukin and receptor levels in AD patients. A total of 23 DIGs were discovered, and the enrichment analysis emphasized their participation in immune signaling pathways. The nomogram based on key interleukin genes demonstrated strong predictive capability, with an AUC of 0.882 in the training set and 0.837 in the validation set. Unsupervised clustering revealed two AD subgroups with distinct immune profiles and pathway activities. Subgroup C2 exhibited higher immune cell infiltration and pathway activity than subgroup C1.

conclusionInterleukins and their receptors are significantly upregulated in AD patients, with distinct immune profiles identified in AD subgroups. The predictive nomogram effectively stratifies AD patients based on interleukin gene expression. These findings provide insights into AD's immunological landscape and suggest potential biomarkers for personalized therapeutic strategies.

Indexed as

Alzheimer DiseaseInterleukinsAgedFemaleGene Expression ProfilingHumansMaleNomogramsInterleukinsAlzheimer's diseaseImmune profilingImmune subtypesInterleukinsPredictive nomogram

Identifiers

PMID40211218
PMCPMC11984269

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