Evidence map›Paper›PMID 40173189›Full record

ArticlePloS one2025

Potential diagnostic markers and therapeutic targets for DM2 and periodontitis based on bioinformatics analysis.

Rong Luo, Zhenye Liang, Huijun Chen, Dandan Bao, Xinlu Lin

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Rong LuoDepartment of Pharmacy, Taizhou First People's Hospital, Taizhou, China.ORCID https://orcid.org/0009-0006-4768-4456
Zhenye LiangDepartment of Pharmacy, Taizhou First People's Hospital, Taizhou, China.
Huijun ChenDepartment of Pharmacy, Taizhou First People's Hospital, Taizhou, China.
Dandan BaoDepartment of Pharmacy, Taizhou First People's Hospital, Taizhou, China.
Xinlu LinDepartment of Pharmacy, Taizhou First People's Hospital, Taizhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes Mellitus type 2 (DM2) is thought to have a bidirectional relationship with Periodontitis (PD). However, the complex molecular interactions between DM2 and PD remain unclear. This study aimed to explore the shared genes and common signatures of DM2 and PD via bioinformatic analysis.

methodsFirstly, using bioinformatic methods to investigate common genes. The series matrix files of GSE6751 for DM and GSE15932 for PD were downloaded from the Gene Expression Omnibus (GEO) database. The data was normalized using the R package, and the limma package was utilized to identify the Differentially Expressed Genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of DEGs were performed using the "clusterProfiler" package in the R software. The protein-protein network was constructed to analyze the potential relationship among the proteins. CytoHubba, a plugin for the Cytoscape software, was used to identify the hub genes. The validation datasets selected for DM2 and PD were GSE10334 and GSE7014, respectively. Receiver Operating Characteristic (ROC) curve analysis was performed to obtain the area under the ROC curve. Lipopolysaccharide (LPS) +  high glucose-induced DM-related PD was simulated to verify the three hub genes through quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and Western blot (WB).

resultsIn total, 44 common DEGs were identified. ITGAM, H2BC21, S100A9 was identified as he hub genes of DM2 and PD, with all of them were up-regulated. In addition, the area under the curve of all three hub genes was more than 0.65. In-vitro experiments revealed that the relative expression of S100A9 was increased after the treatment with LPS +  high glucose. Besides, TLR4 and p-NF-κB levels were also improved in model group.

conclusionS100A9 was identified as the hub gene of DM2 and PD. S100A9 could trigger TLR4 signaling way to promote disease development, which can be the potential targets for diagnosis and treatment.

Indexed as

Computational BiologyDiabetes Mellitus, Type 2PeriodontitisAnimalsBiomarkersCalgranulin BDatabases, GeneticGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansProtein Interaction MapsBiomarkersCalgranulin BS100A9 protein, human

Identifiers

PMID40173189
PMCPMC11964240

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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.