Evidence map›Paper›PMID 40438507›Full record

ArticleFrontiers in aging neuroscience2025

Identification and validation of pyroptosis-related genes in Alzheimer's disease based on multi-transcriptome and machine learning.

Yuntai Wang, Yilin Li, Lu Zhou, Yihuan Yuan, Chuanfei Liu, Zimeng Zeng, Yuanqi Chen, Qi He, Zhuoze Wu

Abstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed.

  1. Trial
  2. How to recognize if a cell dies from pyroptosis?Molecular biology reports · 2026
    Review
  3. Review
  4. Article
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

9 authors.

Yuntai Wang *Institute of Basic Medicine, North Sichuan Medical College, Nanchong, China.
Yilin Li *School of Integrated Traditional Chinese and Western Clinical Medicine, North Sichuan Medical College, Nanchong, China.
Lu ZhouSchool of Clinical Medicine, North Sichuan Medical College, Nanchong, China.
Yihuan YuanSchool of Clinical Medicine, North Sichuan Medical College, Nanchong, China.
Chuanfei LiuSchool of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Zimeng ZengInstitute of Basic Medicine, North Sichuan Medical College, Nanchong, China.
Yuanqi ChenSchool of Nursing, North Sichuan Medical College, Nanchong, China.
Qi HeInstitute of Basic Medicine, North Sichuan Medical College, Nanchong, China.
Zhuoze WuInstitute of Basic Medicine, North Sichuan Medical College, Nanchong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Alzheimer's disease (AD) progression is characterized by persistent neuroinflammation, where pyroptosis-an inflammatory programmed cell death mechanism-has emerged as a key pathological contributor. However, the molecular mechanisms through which pyroptosis-related genes (PRGs) drive AD pathogenesis remain incompletely elucidated. Methods: We integrated multiple transcriptomes of AD patients from the GEO database and analyzed the expression of PRGs in combined datasets. Machine learning algorithms and comprehensive bioinformatics analysis (including immune infiltration and receiver operating characteristic (ROC)) were applied to identify the hub genes. Additionally, we validated the expression patterns of these key genes using the expression data from AD mice and constructed potential regulatory networks through time series and correlation analysis. Results: We identified 91 PRGs in AD using the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes analysis. By application of the protein-protein interaction and machine learning algorithms, seven pyroptosis feature genes (CHMP2A, EGFR, FOXP3, HSP90B1, MDH1, METTL3, and PKN2) were identified. Crucially, MDH1 and PKN2 demonstrated superior performance in terms of immune cell infiltration, ROC curves, and experimental validation. Furthermore, we constructed the long non-coding RNA and mRNA (lncRNA-mRNA) regulatory network of these characteristic genes using the gene expression profiles from AD mice at varying ages, revealing the potential regulatory mechanism in AD. Conclusion: This study provides the first comprehensive characterization of pyroptosis-related molecular signatures in AD. Seven hub genes were identified, with particular emphasis on MDH1 and PKN2. Their superior performances were validated through comprehensive bioinformatic analysis in both patient and mouse transcriptomes, as well as the experimental data. Our findings establish foundational insights into pyroptosis mechanisms in AD that may inform novel treatment strategies targeting neuroinflammatory pathways.

Indexed as

Alzheimer’s diseasebioinformaticsimmune infiltrationmachine learningpyroptosisregulatory network

Identifiers

PMID40438507
PMCPMC12116433

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.