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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1398582

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

Provisionally accepted
Chujun Peng Chujun Peng 1Jinhang Huang Jinhang Huang 1Mingyue Li Mingyue Li 2Guanru Liu Guanru Liu 2Lingxian Liu Lingxian Liu 2Jiechun Lin Jiechun Lin 2Weijun Sun Weijun Sun 2Hongtao Liu Hongtao Liu 2Yonghui Huang Yonghui Huang 2Xin Chen Xin Chen 2*
  • 1 School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China
  • 2 School of Automation, Guangdong University of Technology, Guangzhou, China

The final, formatted version of the article will be published soon.

    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. Conclusions: The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.

    Keywords: Periodontitis, biomarkers, Network analysis, integration, Public microarrary datasets

    Received: 19 Mar 2024; Accepted: 10 May 2024.

    Copyright: © 2024 Peng, Huang, Li, Liu, Liu, Lin, Sun, Liu, Huang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xin Chen, School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.