Evidence map›Paper›PMID 35145474›Full record

ArticleFrontiers in endocrinology2021

Identification of Shared Genes and Pathways in Periodontitis and Type 2 Diabetes by Bioinformatics Analysis.

Junho Kang, Eun Jung Kwon, Mihyang Ha, Hansong Lee, Yeuni Yu, Ji Wan Kang, Yeongjoo Kim, Eun Young Lee, Ji-Young Joo, Hye Jin Heo and 4 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.9field-weighted citation impact, top 15% of its field
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

7 citing papers in PubMed, 12 citations in OpenAlex.

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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

14 authors at 2 institutions in 1 country.

Junho KangMedical Research Institute, Pusan National University, Busan, South Korea.
Eun Jung KwonInterdisciplinary Program of Genomic Data Science, Pusan National University, Busan, South Korea.
Mihyang HaInterdisciplinary Program of Genomic Data Science, Pusan National University, Busan, South Korea.
Hansong LeeInterdisciplinary Program of Genomic Data Science, Pusan National University, Busan, South Korea.
Yeuni YuInterdisciplinary Program of Genomic Data Science, Pusan National University, Busan, South Korea.
Ji Wan KangInterdisciplinary Program of Genomic Data Science, Pusan National University, Busan, South Korea.
Yeongjoo KimInterdisciplinary Program of Genomic Data Science, Pusan National University, Busan, South Korea.
Eun Young LeeDepartment of Oral Pathology, School of Dentistry, Pusan National University, Yangsan, South Korea.
Ji-Young JooDepartment of Periodontology, School of Dentistry, Pusan National University, Yangsan, South Korea.
Hye Jin HeoDepartmment of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea.
Eun Kyoung KimDepartmment of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea.
Tae Woo KimDepartment of Orthopaedic Surgery, Pusan National University Yangsan Hospital, School of Medicine, Pusan National University, Yangsan, South Korea.
Yun Hak KimDepartmment of Anatomy, School of Medicine, Pusan National University, Yangsan, South Korea.
Hae Ryoun ParkDepartment of Oral Pathology, School of Dentistry, Pusan National University, Yangsan, South Korea.
Pusan National University · KRPusan National University Yangsan Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: It is well known that the presence of diabetes significantly affects the progression of periodontitis and that periodontitis has negative effects on diabetes and diabetes-related complications. Although this two-way relationship between type 2 diabetes and periodontitis could be understood through experimental and clinical studies, information on common genetic factors would be more useful for the understanding of both diseases and the development of treatment strategies. Materials and Methods: Gene expression data for periodontitis and type 2 diabetes were obtained from the Gene Expression Omnibus database. After preprocessing of data to reduce heterogeneity, differentially expressed genes (DEGs) between disease and normal tissue were identified using a linear regression model package. Gene ontology and Kyoto encyclopedia of genes and genome pathway enrichment analyses were conducted using R package ' Results: We identified 152 commonly DEGs, including 125 upregulated and 27 downregulated genes. Through common DEGs, we constructed a protein-protein interaction and identified highly connected hub genes. The hub genes were up-regulated in both diseases and were most significantly enriched in the Fc gamma R-mediated phagocytosis pathway. Discussion: We have identified three up-regulated genes involved in Fc gamma receptor-mediated phagocytosis, and these genes could be potential therapeutic targets in patients with periodontitis and type 2 diabetes.

Indexed as

AdultAgedComputational BiologyDatabases, GeneticDiabetes Mellitus, Type 2Down-RegulationFemaleHumansLinear ModelsMaleMiddle AgedPeriodontitisPhagocytosisProtein Interaction MapsReceptors, IgGTranscriptomeReceptors, IgGbiomarkerdifferentially expressed genesFc gamma receptorgene expression omnibushub genes

Identifiers

PMID35145474
PMCPMC8822582
OpenAlexW4210401217

What OpenQuestion holds

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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.