Evidence map›Paper›PMID 40190307›Full record

ArticleArchives of medical science : AMS2025

Identification of Alzheimer's disease biomarkers and their immune function characterization.

Mingkai Lin, Yue Zhou, Peixian Liang, Ruoyi Zheng, Minwei Du, Xintong Ke, Wenjing Zhang, Pei Shang

Abstract read
In one paragraph

Article in Archives of medical science : AMS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
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

8 authors.

Mingkai LinDepartment of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Yue ZhouDepartment of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Peixian LiangDepartment of Ultrasound, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Ruoyi ZhengDepartment of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Minwei DuThe Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Xintong KeCollege of Teacher's Education, Guangdong University of Education, Guangzhou, China.
Wenjing ZhangDepartment of Respiratory Medicine, Puyang People's Hospital, Henan, China.
Pei ShangDepartment of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Alzheimer's disease (AD) is a neurodegenerative disease with neurogenic fiber tangles caused by amyloid-β protein plaques and tau protein hyperphosphorylation as the pathological manifestations. This study was based on multi-omics to investigate the mechanisms and immune characterization of AD. Material and methods: Based on bulk RNA-seq (GSE122063 and GSE97760), we screened potential biomarkers for AD by differential expression analysis and machine learning algorithms. Then, we analyzed the expression characteristics and immune functions of the above biomarkers by scRNA-seq (single-cell RNA sequencing) data analysis (GSM4996463 and GSM4996462) and immune infiltration analysis. Results: Five biomarkers (RBM3, GOLGA8A, ALS2, FSD2, and LOC100287628) were identified using machine learning algorithms. Single-cell analysis revealed distinct expression patterns of these biomarkers in astrocytes from AD samples compared to normal samples. Additionally, three key biomarkers were selected based on interaction networks, and the diagnostic models indicated high diagnostic efficacy for these biomarkers. Based on immune infiltration and correlation analyses, RBM3, GOLGA8A, and ALS2 were all highly correlated with CD8 T cell content in the immune microenvironment of AD. Conclusions: The biomarkers identified in this study demonstrate significant diagnostic potential for AD. Notably, the downregulation of RBM3 in astrocytes and the decreased presence of CD8 T cells infiltrating brain tissue are potential risk factors for AD.

Indexed as

Alzheimer’s diseasebiomarkerCD8 T cellmachine learning algorithmtrajectory analysis

Identifiers

PMID40190307
PMCPMC11969561

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