Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets

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.

Identification of key module and hub genes in pulpitis using weighted gene co-expression network analysis

“…Moreover, STAT3 has two roles in tumorous inflammatory events and immunoactivity via stimulating pro-oncogenesis inflammation paths, like interleukin-6 (IL-6)-GP130-Janus kinase (JAK) pathway, nuclear factor-kappa B (NF-kappa B) pathway, and opposing STAT1-and NF-kappa B-mediated T helper 1 anticancer immunoresponses [36]. In line with our study, IL6/JAK/STAT3 signaling pathway found to be associated with pulpitis [8]. In present study, we found HMOX1 and STAT3 were significantly higher in pulp of Inflamed group than that of normal group, and lower in pulp of iRoot group than that of Inflamed group, which indicated that HMOX1 and STAT3 are important inflammation factors in pulpitis, and the expression level of them could be reversed by vital pulp treatment.…”

“…At the molecular level, various cell factors are released during pulp inflammation, including cytokines, growth factors, inflammation mediators, antimicrobial peptides, and proteases. Biomarker candidates of pulpitis were screened using bioinformatics analysis of merged datasets [8]. However, understanding and analysis methods of biological characteristics for pulpitis remains limited.…”

RNA‐sequencing analysis reveals the potential molecular mechanism of RAD54B in the proliferation of inflamed human dental pulp cells

“…However, some pathways were not shared by the previous two studies and this study, such as the NOD‐like receptor signalling pathway and PI3K‐Akt signalling, which could be generated from the sample collection process (such as sample size, patient age, sex and individual differences). Several studies have also conducted a deeper analysis combining the raw data from these two studies and developed new diagnostic biomarkers of pulpitis (Al‐Natour et al, 2021; Chen, Zeng, et al, 2020; Lei et al, 2019; Liu, Wang, et al, 2021; Xin et al, 2021), most of which were concordantly expressed in our data (e.g. ITGAX , TREM1 , CD86 and FCGR2A ).…”

A critical analysis of research methods to study clinical molecular biomarkers in Endodontic research

“…biomarkers) that can be invariably linked to a disease or the outcome of a treatment (Naylor, 2003). It is also possible to identify a pattern of biomarkers that can be linked to a disease, a so‐called biosignature (Chen et al, 2020; MacLean et al, 2019). A useful way to classify biomarkers is thus to address their purpose (Winter et al, 2013) and the collection site (Fitzsimmons et al, 2010).…”