Evidence map›Paper›PMID 41868603›Full record

ArticleFrontiers in molecular biosciences2025

miRNA-associated gene networks reveal potential candidate markers for Alzheimer's disease.

Shujuan Pan, Yanfang Zhou, Xiaoyu Wang, Jinghui Tong, Yanli Li, Junchao Huang, Song Chen, Yimin Cui, Zhiren Wang, Yun-Long Tan

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

10 authors.

Shujuan PanPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Yanfang ZhouPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Xiaoyu WangPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Jinghui TongPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Yanli LiPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Junchao HuangPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Song ChenPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Yimin CuiDepartment of Pharmacy, Peking University First Hospital, Beijing, China.
Zhiren WangPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.
Yun-Long TanPeking University HuiLongGuan Clinical Medical School, Beijing Huilongguan Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by memory and cognitive decline. Recent studies highlight the significant role of microRNAs (miRNAs) in regulating genes related to AD. This research aims to develop miRNA-associated gene regulatory networks as candidate AD biomarkers. Methods: We recruited 85 AD patients and 74 healthy controls, conducting whole blood miRNA sequencing and applying machine learning to identify differentially expressed miRNAs, which were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). We used bioinformatics databases to predict target genes for these miRNAs and obtained gene expression data from the Gene Expression Omnibus (GEO) database (GSE122063 and GSE18309). Using the ggplot2 package in R, we discovered the overlap between miRNA target genes and differentially expressed genes (DEGs) from the GSE datasets. Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of the DEGs were then conducted using the Metascape database. Key hub genes were pinpointed by constructing a protein-protein interaction (PPI) network with the Retrieval of Interacting Genes (STRING) database and analyzing it with cytoHubba. Drug-gene interactions were predicted and examined using the Drug-Gene Interaction database (DGIdb) (http://www.dgidb.org/). Results: qRT-PCR was used to confirm the expression of the hub genes. The results showed that four miRNAs (miR-192-5p, miR-484, miR-21-5p, and miR-24-2-5p) were downregulated, while two target RNAs (SLC32A1 and GAD1) were upregulated. Discussion: This regulatory network, which is strongly linked to AD, has been initially identified as a candidate biomarker for AD. Our research provides new insights into the pathogenic mechanisms of AD, potentially improving the understanding of miRNAs' role in the disease.

Indexed as

Alzheimer’s diseaseGAD1gene networksmiRNASLC32A1

Identifiers

PMID41868603
PMCPMC13002409

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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.