Evidence map›Paper›PMID 40678591›Full record

ArticleJournal of Alzheimer's disease reports

Identification and experimental validation of Alzheimer's disease hub genes via bioinformatics and machine learning.

Ying Hu, Zhaoshuyu Pan, Jianping Li, Bin Tang, Mingbo Luo, Yu Li, Xue Cao, Kaiwen Zheng, Nana Wang, Chuanjie Xu

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Article in Journal of Alzheimer's disease reports. 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

10 authors.

Ying HuDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.ORCID https://orcid.org/0009-0005-3286-0157
Zhaoshuyu PanDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Jianping LiDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Bin TangDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Mingbo LuoDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Yu LiDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Xue CaoDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Kaiwen ZhengDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Nana WangDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.
Chuanjie XuDepartment of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder. Objective: To identify diagnostic and predictive biomarkers for AD. Methods: Based on three GEO datasets of human brain tissue from AD patients and controls, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were used to identify AD-related gene modules. Hub genes were screened via protein-protein interaction (PPI) analysis and three machine learning algorithms. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration and hub gene expression correlations were analyzed using xCell. In vivo validation was performed using an AD mouse model. Results: The magenta module was significantly correlated with AD. PPI network analysis identified 15 AD-related genes, mainly enriched in mitochondria and ribosomes. Two hub genes, DLAT and CCDC88b, were identified. DLAT was significantly downregulated in AD, and CCDC88b was upregulated (p < 0.01); both findings were validated via qPCR in AD model mice. ROC analysis showed good diagnostic performance. Immune infiltration analysis revealed macrophages as the dominant cell type, with hub gene expression associated with immune cell presence. Conclusions: DLAT and CCDC88b are potential novel biomarkers for AD and may serve as targets for therapeutic intervention.

Indexed as

Alzheimer's diseaseclinical diagnostic modelexperimental validationmachine learningWGCNA

Identifiers

PMID40678591
PMCPMC12267949

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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.