Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression da
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RESEARCH ARTICLE
Open Access
Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets Ming Chen1,2†, Junkai Zeng2,3†, Yeqing Yang1,2 and Buling Wu2,4*
Abstract Background: Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis. Methods: By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein– protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Results: A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1. Conclusions: With bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis. Keywords: Pulpitis, Bioinformatics analysis, Diagnostic biomarker, Microarray gene expression dataset
* Correspondence: [email protected] † Ming Chen and Junkai Zeng contributed equally to this work. 2 School of Stomatology, Southern Medical University, Guangzhou, China 4 Shenzhen Stomatology Hospital (Pingshan), Southern Medical University, Shenzhen, Guangdong 510515, P.R. China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link
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