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

BMC Oral Health ◽

10.1186/s12903-020-01266-5 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Ming Chen ◽

Junkai Zeng ◽

Yeqing Yang ◽

Buling Wu

Keyword(s):

Gene Expression ◽

Dental Pulp ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Pathways ◽

Signalling Pathway ◽

Diagnostic Biomarker ◽

Hub Genes ◽

Geo Database

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

10.21203/rs.2.20729/v3 ◽

2020 ◽

Author(s):

Ming Chen ◽

Junkai Zeng ◽

Yeqing Yang ◽

Buling Wu

Keyword(s):

Gene Expression ◽

Dental Pulp ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Pathways ◽

Signalling Pathway ◽

Diagnostic Biomarker ◽

Hub Genes ◽

Go Analysis ◽

Geo Database

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.

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

10.21203/rs.2.20729/v4 ◽

2020 ◽

Author(s):

Ming Chen ◽

Junkai Zeng ◽

Yeqing Yang ◽

Buling Wu

Keyword(s):

Gene Expression ◽

Dental Pulp ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Pathways ◽

Signalling Pathway ◽

Diagnostic Biomarker ◽

Hub Genes ◽

Geo Database

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

10.21203/rs.2.20729/v2 ◽

2020 ◽

Author(s):

Ming Chen ◽

Junkai Zeng ◽

Yeqing Yang ◽

Buling Wu

Keyword(s):

Gene Expression ◽

Dental Pulp ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Biological Pathways ◽

Signalling Pathway ◽

Diagnostic Biomarker ◽

Hub Genes ◽

Go Analysis ◽

Geo Database

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.

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Bioinformatic Identification of Hub Genes and Biological Pathways for Sepsis-Associated Acute Lung Injury

10.21203/rs.3.rs-623296/v1 ◽

2021 ◽

Author(s):

Chao Zhang ◽

Feng Xu ◽

Fang Fang

Keyword(s):

Gene Expression ◽

Acute Lung Injury ◽

Lung Injury ◽

Function Analysis ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Biological Pathways ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Hub Genes

Abstract Background: Sepsis-associated acute lung injury (ALI) is a potentially lethal complication associated with a poor prognosis and high mortality worldwide, especially in the outbreak of COVID-19. However, the fundamental mechanisms of this complication were still not fully elucidated. Thus, we conducted this study to identify hub genes and biological pathways of sepsis-associated ALI, mainly focus on two pathways of LPS and HMGB1. Methods: Gene expression profile GSE3037 were downloaded from Gene Expression Omnibus (GEO) database, including 8 patients with sepsis-induced acute lung injury, with 8 unstimulated blood neutrophils, 8 LPS- induced neutrophils and 8 HMGB1-induced neutrophils. Diﬀerentially expressed genes (DEGs) identifications, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network constructions were performed to obtain hub genes and relevant biological pathways.Results: We identified 534 and 317 DEGs for LPS- and HMGB1-induced ALI, respectively. The biological pathways involved in LPS- and HMGB1-induced ALI were also identified accordingly. By PPI network analysis, we found that ten hub genes for LPS-induced ALI (CXCL8, TNF, IL6, IL1B, ICAM1, CXCL1, CXCL2, IL1A, IL1RN and CXCL3) and another ten hub genes for HMGB1-induced ALI (CCL20, CXCL2, CXCL1, CCL4, CXCL3, CXCL9, CCL21, CXCR6, KNG1 and SST). Furthermore, by combining analysis, the results revealed that genes of TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3 were potential biomarkers for sepsis-associated ALI. Conclusions: Our study revealed that ten hub genes associated with sepsis-induced ALI were TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3, which may serve as genetic biomarkers and be further verified in prospective experimental trials.

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Identification of Candidate Biomarkers Correlated Poorer Prognosis of Breast Cancer via Bioinformatics Method

10.21203/rs.3.rs-402557/v1 ◽

2021 ◽

Author(s):

Gang Chen ◽

Mingwei Yu ◽

Jianqiao Cao ◽

Huishan Zhao ◽

Yuanping Dai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Kegg Pathway ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Molecular Therapy ◽

Hub Genes

Abstract Background: Breast cancer (BC) is a malignancy with a high incidence among women in the world, and it is very urgent to identify significant biomarkers and molecular therapy methods.Methods: Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic values and molecular mechanism of these hub genes Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). Results: 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. GO and KEGG pathway analysis revealed that DEGs were mainly related to cell cycles and cell proliferation. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree of genes were selected, among which, 11 hub gene were significantly correlated with the prognosis of patients with BC. From GSEA reviewed correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. Conclusion: this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

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Bioinformatic Analysis Reveals VEGFA Promotes the Occurrence of PLA2R-associated Idiopathic Membranous Nephropathy by Angiogenesis via PI3K/AKT Signaling Pathway

10.21203/rs.3.rs-76655/v1 ◽

2020 ◽

Author(s):

Ben Ke ◽

Wen Shen ◽

Zhibing Duan

Keyword(s):

Signaling Pathway ◽

Kegg Pathway ◽

Kidney Diseases ◽

Vesicular Transport ◽

Enrichment Analysis ◽

Akt Signaling ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Hub Genes

Abstract Backround: PLA2R-associated IMN covers 70% of IMN, which is one of the main types of chronic kidney disease in adults and one of the most common causes of end‑stage renal disease. Vascular endothelial growth factor A (VEGFA), a homodimeric vasoactive glycoprotein, is the key mediator of angiogenesis, which lead to numerous kidney diseases, including TSHD7A-associated IMN. However, the role of VEGFA in PLA2R-associated IMN is still poorly understood.Methods: We downloaded the microarray data GSE115857 from GEO. The DEGs were identified with R software, and the functional and pathway enrichment analysis of DEGs was performed utilizing the DAVID and Cytoscape ClueGo plug-in. A comprehensive list of interacting DEGs was constructed using the STRING database and visualized by Cytoscape software. The Cytoscape MCODE and cytoHubba plug-in were used to identify clustered sub-networks, and hub genes from the protein-protein interaction network. Gene set enrichment analysis (GSEA) was used to identify signaling pathway in IMN. Results: There were 1422 genes (952 up-regulated genes and 470 down-regulated genes) were identified as DEGs in GSE115857. The BP of DEGs in GSE115857 was clustered in regulation of transcription from RNA polymerase II promoter, positive regulation of nuclear-transcribed mRNA poly(A) tail shortening, cell adhesion et al. The KEGG pathway of DEGs in GSE115857 was clustered in Rheumatoid arthritis, ABC transporters, PI3K/AKT signaling pathway et al. Then we got a huge PPI network from STRING. 6 modules were screen out to study the functional changes in IMN. The KEGG pathway of module 3 was enriched in soluble N-ethylmaleimide–sensitive factor attachment protein receptor (SNARE) interactions in vesicular transport. There were 3 hub genes screened out, namely, VEGFA, JUN,and FOS. Following the random walk, all genes were ranked and GSEA analysis showed that the signaling pathway of DEGs in GSE115857 was focused on angiogenesis, in which VEGFA acts as a core gene. CONCLUSION: In summary, this study reveals VEGFA promotes PLA2R-associated IMN by stimulating angiogenesis via PI3K/AKT signaling. Moreover, SNARE interactions in vesicular transport was involved in the development of PLA2R-associated IMN, which may offer a novel therapeutic strategy in treatment of IMN.

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Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

PeerJ ◽

10.7717/peerj.8276 ◽

2020 ◽

Vol 8 ◽

pp. e8276 ◽

Cited By ~ 1

Author(s):

Yichong Zhang ◽

Yuanbo Zhan ◽

Yuhui Kou ◽

Xiaofeng Yin ◽

Yanhua Wang ◽

...

Keyword(s):

Gene Expression ◽

Text Mining ◽

Heterotopic Ossification ◽

Signaling Pathway ◽

Molecular Mechanisms ◽

Egf Receptor ◽

Enrichment Analysis ◽

Biological Pathways ◽

Specific Gene ◽

Cord Injury

Background Neurogenic heterotopic ossification is a disorder of aberrant bone formation affecting one in five patients sustaining a spinal cord injury or traumatic brain injury (SCI-TBI-HO). However, the underlying mechanisms of SCI-TBI-HO have proven difficult to elucidate. The aim of the present study is to identify the most promising candidate genes and biological pathways for SCI-TBI-HO. Methods In this study, we used text mining to generate potential explanations for SCI-TBI-HO. Moreover, we employed several additional datasets, including gene expression profile data, drug data and tissue-specific gene expression data, to explore promising genes that associated with SCI-TBI-HO. Results We identified four SCI-TBI-HO-associated genes, including GDF15, LDLR, CCL2, and CLU. Finally, using enrichment analysis, we identified several pathways, including integrin signaling, insulin pathway, internalization of ErbB1, urokinase-type plasminogen activator and uPAR-mediated signaling, PDGFR-beta signaling pathway, EGF receptor (ErbB1) signaling pathway, and class I PI3K signaling events, which may be associated with SCI-TBI-HO. Conclusions These results enhance our understanding of the molecular mechanisms of SCI-TBI-HO and offer new leads for researchers and innovative therapeutic strategies.

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Identification of Key Immune-Related Genes, Molecular Pathways and Immune Infiltration as Diagnostic and Therapeutic Candidate Targets for RA: an integrated bioinformatics-based analysis

10.21203/rs.3.rs-296982/v1 ◽

2021 ◽

Author(s):

Sheng Fang ◽

Xiao Fang ◽

Xin Xu ◽

Lin Zhong ◽

An-quan Wang ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Mast Cells ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Systemic Autoimmune Disease ◽

Molecular Pathways ◽

Hub Genes ◽

Immune Infiltration

Abstract Relevance Rheumatoid arthritis (RA) is a systemic autoimmune disease with an aggressive, chronic synovial inflammation as the main pathological change. However, the specific etiology, pathogenesis, and related biomarkers in diagnosis and treatment are still not fully elucidated. This study attempts to provide new perspectives and insights into RA at the genetic, molecular, and cellular levels through the tenet of personalized medicine. Methods Gene expression profiles of four individual knee synovial tissues were downloaded from a comprehensive gene expression database, R language was used to screen for significantly differentially expressed genes (DEGs), Gene Ontology Enrichment Analysis, Kyoto Gene Encyclopedia, and Gene Set Enrichment Analysis were performed to analyze the biological functions and signaling pathways of these DEGs, STRING online database was used to establish protein-protein interaction networks, Cytoscape software to obtain ten hub genes, Goplot to get six inflammatory immune-related hub genes, and CIBERSORT algorithm to impute immune infiltration. Results Molecular pathways that play important roles in RA were obtained: Toll-like receptors, AMPK, MAPK, TNF, FoxO, TGF-beta, PI3K and NF-κB pathways, Ten hub genes: Ccr1, Ccr2, Ccr5, Ccr7, Cxcl5, Cxcl6, Cxcl13, Ccl13, Adcy2, and Pnoc. among which Adcy2 and Pnoc have not been reported in RA studies, suggesting that they may be worthy targets for further study. It was also found that among the synoviocytes in RA, the proportions of plasma cells, CD8 T cells, follicular helper T cells, monocytes, γ delta T cells, and M0 macrophages were higher, while the proportions of CD4 memory resting T cells, regulatory T cells (Tregs), activated NK cells, resting dendritic cells, M1 macrophages, eosinophils, activated mast cells, resting mast cells were lower in proportion, and each cell played an important role in RA. Conclusions This study may help understand the key genes, molecular pathways, the role of inflammatory immune infiltrating cells in RA’s pathogenesis and provide new targets and ideas for the diagnosis and personalized treatment of RA.

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Potential Novel Therapeutic Targets In Epilepsy From The View of Intestinal Comorbidities - Inflammatory Bowel Disease

10.21203/rs.3.rs-960451/v1 ◽

2021 ◽

Author(s):

Mi Jiang ◽

Jia Li ◽

Zhi Song

Keyword(s):

Gene Expression ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Refractory Epilepsy ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Inflammatory Bowel ◽

Novel Therapeutic

Abstract Background: Epilepsy is a complicated neurological disorder with almost 30% refractory. Recent years, several studies showed that epilepsy is associated with its comorbidities by shared molecular mechanisms. However, the association of epilepsy and digestive comorbidities are still unclear. In this study, we aim to explore the association between inflammatory bowel disease (IBD) and epilepsy, and to find promising therapeutic targets for refractory epilepsy. Methods: Two gene expression profiles (GSE134697 and GSE59071) were selected from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R and the DESeq2 package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of intersection DEGs and Gene Set Enrichment Analysis (GSEA) were conducted by clusterProfiler package. The protein-protein interactions (PPI) network was established by using STRING and visualized by Cytoscape. Genes in the most significant module identified by MCODE plug-in were considered as candidate hub genes. Validation of hub genes were performed by using the GSE143272 dataset. Results: Cytokine-cytokine receptor interaction pathway is identified as a communal pathway between IBD and epilepsy. CXCL8, CXCR4 and ITGAX were identified as the hub genes. Conclusions: The identification of the communal pathway and hub genes in this study contributes to a potential novel therapeutic target in refractory epilepsy.

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