Evidence map›Paper›PMID 39919076›Full record

ArticlePloS one2025

Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study.

Nian Liu, Qian Deng, Zining Peng, Danning Mao, Yuanbo Huang, Fanyu Meng, Xiaoyu Zhang, Jiayan Shen, Zhaofu Li, Weitian Yan and 1 more

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

11 authors.

Nian LiuFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Qian DengFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Zining PengFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.ORCID 0000-0003-3081-5967
Danning MaoFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Yuanbo HuangFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Fanyu MengFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Xiaoyu ZhangFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Jiayan ShenFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Zhaofu LiFirst School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Weitian YanDepartment of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.
Jiangyun PengDepartment of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Chinese Medicine, Kunming, Yunnan, PR China.ORCID 0000-0003-1820-2252

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) and Osteoarthritis (OA) have been shown to have a close association in previous studies, but the pathogenesis of both diseases are unclear. This study explores the potential common molecular mechanisms between AD and OA through bioinformatics analysis, providing new insights for clinical treatment strategies.

methodsThe AD and OA-related datasets were downloaded from the gene expression database GEO. The datasets were analyzed to obtain differentially expressed gene (DEG) datasets for OA and AD, respectively. The intersection of these DEGs was analyzed to identify common DEGs (Co-DEGs). Subsequently, the Co-DEGs were enriched, and a protein-protein interaction network was constructed to identify core genes. The expression of these genes was validated in a separate dataset, and their diagnostic value for the diseases was analyzed. In addition, the core genes were analyzed using gene set enrichment analysis and single-gene genome variation analysis.

resultsAnalysis of DEGs on gene chips from OA and AD patients revealed significant changes in gene expression patterns. Notably, EFEMP2 and TSPO, genes associated with inflammatory responses, showed lower expression levels in both AD and OA patients, suggesting a downregulation in the pathological backgrounds of these diseases. Additionally, GABARAPL1, which is crucial for the maturation of autophagosomes, was found to be upregulated in both conditions. These findings suggest the potential of these genes as diagnostic biomarkers and potential therapeutic targets. However, to confirm the effectiveness of these genes as therapeutic targets, more in-depth mechanistic studies are needed in the future, particularly to explore the feasibility and specific mechanisms of combating disease progression by regulating the expression of these genes.

conclusionsThis study suggests that AD and OA shares common molecular mechanisms. The identification of EFEMP2, GABARAPL1, and TSPO as key target genes highlights potential common factors in both diseases. Further investigation into these findings could lead to new candidate targets and treatment directions for AD and OA, offering promising avenues for developing more effective and targeted therapeutic interventions.

Indexed as

Alzheimer DiseaseComputational BiologyOsteoarthritisTranscriptomeDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksHumansMicrotubule-Associated ProteinsProtein Interaction MapsMicrotubule-Associated Proteins

Identifiers

PMID39919076
PMCPMC11805404

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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.