Evidence map›Paper›PMID 39176085›Full record

ArticleFrontiers in immunology2024

Unveiling the bioinformatic genes and their involved regulatory mechanisms in type 2 diabetes combined with osteoarthritis.

Guangming Mao, Wenhao Xu, Lingli Wan, Hongpin Wang, Shutao Xu, Liangming Zhang, Shiyang Li, Jifa Zhang, Zhongming Lai, Yuping Lan and 1 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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.

Guangming Mao *Department of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Wenhao Xu *Department of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Lingli WanDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Hongpin WangDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Shutao XuDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Liangming ZhangDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Shiyang LiDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Jifa ZhangDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Zhongming LaiDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Yuping LanDepartment of Pharmacy, Panzhihua Central Hospital, Panzhihua, China.
Jianhui LiuCollege of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Type 2 Diabetes Mellitus (T2D) and Osteoarthritis (OA) are both prevalent diseases that significantly impact the health of patients. Increasing evidence suggests that there is a big correlation between T2D and OA, but the molecular mechanisms remain elusive. The aims of this study are to investigate the shared biomarkers and potential molecular mechanisms in T2D combined with OA. Methods: T2D and OA-related differentially expressed genes (DEGs) were identified via bioinformatic analysis on Gene Expression Omnibus (GEO) datasets GSE26168 and GSE114007 respectively. Subsequently, extensive target prediction and network analysis were finished with Gene Ontology (GO), protein-protein interaction (PPI), and pathway enrichment with DEGs. The transcription factors (TFs) and miRNAs coupled in co-expressed DEGs involved in T2D and OA were predicted as well. The key genes expressed both in the clinical tissues of T2D and OA were detected with western blot and qRT-PCR assay. Finally, the most promising candidate compounds were predicted with the Drug-Gene Interaction Database (DGIdb) and molecular docking. Results: In this study, 209 shared DEGs between T2D and OA were identified. Functional analysis disclosed that these DEGs are predominantly related to ossification, regulation of leukocyte migration, extracellular matrix (ECM) structural constituents, PI3K/AKT, and Wnt signaling pathways. Further analysis via Protein-Protein Interaction (PPI) analysis and validation with external datasets emphasized MMP9 and ANGPTL4 as crucial genes in both T2D and OA. Our findings were validated through qRT-PCR and Western blot analyses, which indicated high expression levels of these pivotal genes in T2D, OA, and T2D combined with OA cases. Additionally, the analysis of Transcription Factors (TFs)-miRNA interactions identified 7 TFs and one miRNA that jointly regulate these important genes. The Receiver Operating characteristic (ROC) analysis demonstrated the significant diagnostic potential of MMP9 and ANGPTL4.Moreover, we identified raloxifene, ezetimibe, and S-3304 as promising agents for patients with both T2D and OA. Conclusion: This study uncovers the shared signaling pathways, biomarkers, potential therapeutics, and diagnostic models for individuals suffering from both T2D and OA. These findings not only present novel perspectives on the complex interplay between T2D and OA but also hold significant promise for improving the clinical management and prognosis of patients with this concurrent condition.

Indexed as

Computational BiologyDiabetes Mellitus, Type 2Gene Regulatory NetworksOsteoarthritisProtein Interaction MapsAngiopoietin-Like Protein 4BiomarkersDatabases, GeneticGene Expression ProfilingGene Expression RegulationHumansMicroRNAsMolecular Docking SimulationTranscription FactorsAngiopoietin-Like Protein 4ANGPTL4 protein, humanBiomarkersMicroRNAsTranscription Factorsangiopoietin-like 4 (ANGPTL4)bioinformatics analysismatrix metalloproteinase-9 (MMP9)osteoarthritistype 2 diabetes mellitus

Identifiers

PMID39176085
PMCPMC11338775

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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.