Evidence map›Paper›PMID 38919957›Full record

ArticleFrontiers in genetics2024

Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources.

Chujun Peng, Jinhang Huang, Mingyue Li, Guanru Liu, Lingxian Liu, Jiechun Lin, Weijun Sun, Hongtao Liu, Yonghui Huang, Xin Chen

Abstract read
In one paragraph

Article in Frontiers in genetics, 2024. 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

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

2 citing papers in PubMed.

  1. Exacerbation of inflammatory bone loss in TET2-driven clonal hematopoiesis.Journal of immunology (Baltimore, Md. : 1950) · 2026
    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

10 authors.

Chujun PengSchool of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China.
Jinhang HuangSchool of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China.
Mingyue LiSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Guanru LiuSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Lingxian LiuSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Jiechun LinSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Weijun SunSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Hongtao LiuSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Yonghui HuangSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Xin ChenSchool of Automation, Guangdong University of Technology, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Periodontitis, a common chronic inflammatory disease, significantly impacted oral health. To provide novel biological indicators for the diagnosis and treatment of periodontitis, we analyzed public microarray datasets to identify biomarkers associated with periodontitis. Method: The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other's expression by sharing microRNA (miRNA) target sites. Results: RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis. Conclusion: The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.

Indexed as

biomarkersintegrationnetwork analysisperiodontitispublic microarrary datasets

Identifiers

PMID38919957
PMCPMC11196414

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