Evidence map›Paper›PMID 42723412›Full record

ArticleGenes, brain, and behavior2026

An Integrated Analysis of Manganese Metabolism-Related Genes and Their Association With Biomarkers, Immune Infiltration, and Clinical Subtypes in Alzheimer's Disease.

Shengnan Shu, Jiahao Hu, Shanshan Hu, Yefeng Yao

Abstract read
In one paragraph

Article in Genes, brain, and behavior, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shengnan ShuDepartment of Neurology, People's Hospital of Longyou County, Zhejiang, China.ORCID https://orcid.org/0009-0006-3564-1838
Jiahao HuDepartment of Internal Medicine, Longyou County Xikou Central Health Center, Zhejiang, China.
Shanshan HuDepartment of Neurology, People's Hospital of Longyou County, Zhejiang, China.
Yefeng YaoDepartment of Hepatobiliary Surgery, People's Hospital of Longyou County, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD), a neurodegenerative condition marked by amyloid-beta plaques and tau protein neurofibrillary tangles, progresses against a backdrop of essential biological processes. Manganese, an indispensable trace element for vital functions including energy metabolism and antioxidant defense, is integral to neurological health. Its precise metabolic role throughout the course of AD pathogenesis is not fully elucidated. Four essential manganese metabolism-related genes were identified as diagnostic markers through a multi-omic framework. This approach integrated Weighted Gene Co-Expression Network Analysis with machine learning ensembles (Least Absolute Shrinkage and Selection Operator/Random Forest/Extreme Gradient Boosting), followed by rigorous testing in an independent dataset. Beyond identification, we utilized CIBERSORT and ssGSEA to characterize immune infiltration and leveraged GSEA/GO/KEGG for pathway elucidation. Finally, AD patients were stratified into molecular subgroups based on these hub genes, and their underlying regulatory networks involving miRNAs and transcription factors were reconstructed. An integrated bioinformatics framework identified 12 differentially expressed genes related to manganese metabolism in AD. Machine-learning-based feature selection further pinpointed four key diagnostic biomarkers (TSPO, PTBP1, GLO1, and ACACB) with high discriminative power (AUC: 0.831-0.905). Immune infiltration analysis revealed substantial immune remodeling in AD, including an elevation of naïve B cells. Moreover, AD samples were classified into two molecular subtypes displaying distinct immune and metabolic characteristics, and regulatory miRNA/TF interaction networks were constructed for the core genes. Our results shed new light on the molecular mechanisms underlying AD and support future efforts toward early diagnosis and personalized therapeutic interventions based on molecular subtypes.

Indexed as

Alzheimer DiseaseManganeseBiomarkersGene Regulatory NetworksHumansBiomarkersManganeseAlzheimer's diseasebiomarkermanganese metabolism‐related genessubtype identification

Identifiers

PMID42723412
PMCPMC13563062

What OpenQuestion holds

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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.