ArticleComputational and mathematical methods in medicine2021
Identification of Pulpitis-Related Potential Biomarkers Using Bioinformatics Approach.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Pilot spatial transcriptomics of dental pulpitis suggests immune-fibroblast profiling linked to reversibility.Journal of translational medicine · 2026Article
- Integrated bioinformatics and network search identify biomarkers and comorbid mechanisms of atrial fibrillation and ischemic stroke.Medicine · 2026Article
- Using Spatial Transcriptomics to Identify a Diagnostic Molecular Signature Associated with Reversible Dental Pulpitis.Research square · 2026Article
- Pulpitis as a microbial disease: single-cell insights into host responses and diagnostic biomarkers for vital pulp therapy.Journal of oral microbiology · 2026Review
- Prediction of hub genes in pulpal inflammation and regeneration using autoencoders and a generative AI approach.Scientific reports · 2025Article
- Drug sensitivity testing during dental pulp-dentin complex repair: its effect on treatment outcome and prognosis.American journal of translational research · 2024Article
- Identification and Experimental Validation of LINC00582 Associated with B Cell Immune and Development of Pulpitis: Bioinformatics and In Vitro Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Identification of key module and hub genes in pulpitis using weighted gene co-expression network analysis.BMC oral health · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inflammatory reaction of pulp tissue plays a role in the pathogen elimination and tissue repair. The evaluation of severity of pulpitis can serve an instructive function in therapeutic scheme. However, there are many limitations in the traditional evaluation methods for the severity of pulpitis. Based on the Gene Expression Omnibus (GEO) database, our study discovered 843 differentially expressed genes (DEGs) related to pulpitis. Afterwards, we constructed a protein-protein interaction (PPI) network of DEGs and used MCODE plugin to determine the key functional subset. Meanwhile, genes in the key functional subset were subjected to GO and KEGG enrichment analyses. The result showed that genes were mainly enriched in inflammatory reaction-related functions. Next, we screened out intersections of PPI network nodes and pulpitis-related genes. Then, 20 genes were obtained as seed genes. In the PPI network, 50 genes that had the highest correlation with seed genes were screened out using random walk with restart (RWR). Furthermore, 4 pulpitis-related hub genes were obtained from the intersection of the top 50 genes and genes in the key functional subset. Finally, GeneMANIA was utilized to predict genes coexpressed with hub genes, and expression levels of the 4 hub genes in normal and pulpitis groups were analyzed based on GEO data. The result demonstrated that the 4 hub genes were mainly coexpressed with chemokine-related genes and were remarkably upregulated in the pulpitis group. In short, we eventually determined 4 potential biomarkers of pulpitis.
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