Evidence map›Paper›PMID 42436416›Full record

ArticleBMC neurology2026

Bioinformatic analysis of differentially expressed mitochondrion-related genes, immune cell infiltration, and diagnostic value in Alzheimer's disease.

Lingjia Tang, Ningning Wu, Yao Zhu, Hong Xu, Yuxuan Mo

Abstract read
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Article in BMC neurology, 2026. 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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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lingjia TangDepartment of Geriatrics, Ningbo No.2 Hospital, Ningbo, Zhejiang, 315000, China.
Ningning WuDepartment of Geriatrics, Ningbo No.2 Hospital, Ningbo, Zhejiang, 315000, China.
Yao ZhuDepartment of Geriatrics, Ningbo No.2 Hospital, Ningbo, Zhejiang, 315000, China.
Hong XuDepartment of Geriatrics, Ningbo No.2 Hospital, Ningbo, Zhejiang, 315000, China.
Yuxuan MoDepartment of General Surgery, Ningbo No.2 Hospital, Ningbo, Zhejiang, 315000, China. myx498175344@126.com.

Funding

2024 Ningbo Municipal Health Science and Technology Plan Project 2024Y12
6 · The paper itself

Abstract

backgroundMitochondrial dysfunction and neuroinflammation are critically implicated in the pathogenesis of Alzheimer's disease (AD). However, a systematic exploration of key mitochondrion-related genes (MRGs) in AD, and their specific roles in reshaping the immune microenvironment and serving as diagnostic biomarkers, remains insufficient.

methodsTo address this, we conducted an integrative bioinformatics analysis. Differentially expressed MRGs were identified from public AD transcriptomic datasets. Their biological functions were elucidated through enrichment analyses. The correlations between core MRGs and ssGSEA-derived immune-cell signature enrichment scores were quantified using transcriptome-based computational analysis. Finally, machine learning models were constructed and validated to assess the diagnostic potential of identified MRG signatures.

resultsA robust set of dysregulated MRGs was identified in AD brains, showing predominant enrichment in pathways of oxidative phosphorylation and energy metabolism. Notably, the expression of key MRGs correlated significantly with altered infiltration abundances of specific immune cell types, including neutrophil-, eosinophil-, NK CD56bright cell-, and T follicular helper cell-related signatures. A diagnostic model constructed from a refined MRG signature exhibited promising predictive accuracy, with area under the curve (AUC) values reaching approximately 0.82 in the training cohort and around 0.74 in independent validation cohorts.

conclusionOur study defines a novel landscape of MRGs in AD, deciphers their tight crosstalk with the immune microenvironment, and establishes a promising MRG-based signature for AD diagnosis. These findings provide fresh insights into the potential molecular interplay between mitochondrial dysfunction and neuroinflammation in AD and nominate candidate mitochondrion-related biomarkers and regulatory mechanisms that warrant further experimental and clinical validation.

Indexed as

Alzheimer DiseaseComputational BiologyGenes, MitochondrialMitochondriaBiomarkersFemaleGene Expression ProfilingHumansMachine LearningTranscriptomeBiomarkersAlzheimer's diseaseImmune-related signature enrichmentLASSO Regressionmitochondrial dysfunction

Identifiers

PMID42436416
PMCPMC13644109

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.