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pmid:33046027

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

Ming Chen · Junkai Zeng · Yeqing Yang · Buling Wu

BMC oral health · 2020 doi:10.1186/s12903-020-01266-5

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complete

Experiment

complete

Data

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Analysis

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pmid:33046027
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2026-11-01T15:41:12Z
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title
Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets.
doi
10.1186/s12903-020-01266-5
pmid
33046027
authors array · 4 items
[0]
Ming Chen
[1]
Junkai Zeng
[2]
Yeqing Yang
[3]
Buling Wu
journal
BMC oral health
year
2020
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.
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Then, the raw data was converted into the form of an expression matrix and handled with the Linear Models for Microarray data (limma, https://bioconductor.org/packages/limma/) package in Bioconductor.
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Then, the raw data was converted into the form of an expression matrix and handled with the Linear Models for Microarray data (limma, https://bioconductor.org/packages/limma/) package in Bioconductor.
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The datasets generated and analysed during the current study are available in GEO DataSets repository, https://www.ncbi.nlm.nih.gov/gds.
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The datasets generated and analysed during the current study are available in GEO DataSets repository, https://www.ncbi.nlm.nih.gov/gds.
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Summary of two individual datasets of pulpitis
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Supplementary information of GO analysis
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Top 10 hub genes with higher degrees of PPI network of pulpitis
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PPI network and hub genes
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Top 10 hub genes with higher degrees of PPI network of pulpitis
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[1]
Receptor-type tyrosine-protein phosphatase C
[2]
Protein tyrosine-protein phosphatase required for T-cell activation through the antigen receptor. Acts as a positive regulator of T-cell coactivation upon binding to DPP4.
[3]
126
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[0]
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[1]
Toll-like receptor 8
[2]
Key component of innate and adaptive immunity
[3]
100
[3] array · 4 items
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[1]
T-lymphocyte activation antigen CD86
[2]
Receptor involved in the costimulatory signal essential for T-lymphocyte proliferation and interleukin-2 production, by binding CD28 or CTLA-4.
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[0]
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[1]
Interleukin-6
[2]
Cytokine with a wide variety of biological functions. Plays an essential role in the final differentiation of B-cells into Ig-secreting cells involved in lymphocyte and monocyte differentiation.
[3]
135
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CCL2
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C-C motif chemokine 2
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Chemotactic factor that attracts monocytes and basophils
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80
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[0]
MMP9
[1]
Matrix metalloproteinase-9
[2]
May play an essential role in local proteolysis of the extracellular matrix and in leukocyte migration
[3]
83
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[0]
IL8
[1]
Interleukin-8
[2]
A chemotactic factor that attracts neutrophils, basophils, and T-cells, major mediator of the inflammatory response
[3]
101
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[0]
SPI1
[1]
Spi-1 Proto-Oncogene
[2]
This protein is a transcriptional activator that may be specifically involved in the differentiation or activation of macrophages or B- cells.
[3]
76
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[0]
ICAM1
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Intercellular adhesion molecule 1
[2]
Encodes a cell surface glycoprotein which is typically expressed on endothelial cells and cells of the immune system
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[0]
LCP2
[1]
Lymphocyte cytosolic protein 2
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Involved in T-cell antigen receptor mediated signalling
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Automated Assays Laravel application and simulated adapters
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evealed by bioinformatics analysis of merged microarray gene expression datasets.
[1]
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[2]
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33046027", "kind": "science-record", "revision": 8, "content_hash": "sha256:4372d93728e58425c994bfa3adf266ddfb795ab66f994f98ee5cdd59de324711", "created_at": "2026-11-01T15:41:12Z", "updated_at": "2026-11-01T22:45:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets.", "doi": "10.1186/s12903-020-01266-5", "pmid": "33046027", "authors": ["Ming Chen", "Junkai Zeng", "Yeqing Yang", "Buling Wu"], "journal": "BMC oral health", "year": 2020, "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.\nMETHODS: 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.\nRESULTS: 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.\nCONCLUSIONS: 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.", "claims": [{"id": "claim-method", "statement": "Then, the raw data was converted into the form of an expression matrix and handled with the Linear Models for Microarray data (limma, https://bioconductor.org/packages/limma/) package in Bioconductor.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33046027", "locator": "sections[3] \"Data processing\" paragraph 2", "quote": "Then, the raw data was converted into the form of an expression matrix and handled with the Linear Models for Microarray data (limma, https://bioconductor.org/packages/limma/) package in Bioconductor."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analysed during the current study are available in GEO DataSets repository, https://www.ncbi.nlm.nih.gov/gds.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33046027", "locator": "sections[20] \"Availability of data and materials\" paragraph 2", "quote": "The datasets generated and analysed during the current study are available in GEO DataSets repository, https://www.ncbi.nlm.nih.gov/gds."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33046027"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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Expression of LncRNAs and mRNAs in normal and inflamed human pulp", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:33046027:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:bf043104e8099321f80fc5b9d6deeea9413e76956c7c80e6b34df66a9125596e", "bytes": 971, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[2]/table-wrap[1]#Tab1", "version": null, "note": "Summary of two individual datasets of pulpitis", "structure": {"pmcid": "PMC7552454", "section_index": 2, "section_title": "Methods", "row_count": 9, "column_count": 3}, "table": {"label": "Table 1", "caption": "Summary of two individual datasets of pulpitis", "rows": [["GEO gene set ID", "GSE77459", "GSE92681"], ["Title", "Gene Expression Profile of 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Acts as a positive regulator of T-cell coactivation upon binding to DPP4.", "126"], ["TLR8", "Toll-like receptor 8", "Key component of innate and adaptive immunity", "100"], ["CD86", "T-lymphocyte activation antigen CD86", "Receptor involved in the costimulatory signal essential for T-lymphocyte proliferation and interleukin-2 production, by binding CD28 or CTLA-4.", "100"], ["IL6", "Interleukin-6", "Cytokine with a wide variety of biological functions. Plays an essential role in the final differentiation of B-cells into Ig-secreting cells involved in lymphocyte and monocyte differentiation.", "135"], ["CCL2", "C-C motif chemokine 2", "Chemotactic factor that attracts monocytes and basophils", "80"], ["MMP9", "Matrix metalloproteinase-9", "May play an essential role in local proteolysis of the extracellular matrix and in leukocyte migration", "83"], ["IL8", "Interleukin-8", "A chemotactic factor that attracts neutrophils, basophils, and T-cells, major mediator of the inflammatory response", "101"], ["SPI1", "Spi-1 Proto-Oncogene", "This protein is a transcriptional activator that may be specifically involved in the differentiation or activation of macrophages or B- cells.", "76"], ["ICAM1", "Intercellular adhesion molecule 1", "Encodes a cell surface glycoprotein which is typically expressed on endothelial cells and cells of the immune system", "83"], ["LCP2", "Lymphocyte cytosolic protein 2", "Involved in T-cell antigen receptor mediated signalling", "77"]], "row_count": 11, "column_count": 4, "numeric_cells": 21, "header_rows": 1, "locator": "section[11]/table-wrap[1]#Tab3"}}], "gaps": ["asset:data:geo:GSE77459 not downloaded", "asset:data:geo:GSE77459 licence not read", "asset:data:geo:GSE92681 not downloaded", "asset:data:geo:GSE92681 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Then, the raw data was converted into the form of an expression matrix and handled with the Linear Models for Microarray data (limma, https://bioconductor.org/packages/limma/) package in Bioconductor."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "Protein-protein interaction"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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