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Article: Biological links in Periodontitis and Rheumatoid arthritis: discovery via text-mining PubMed abstracts

TitleBiological links in Periodontitis and Rheumatoid arthritis: discovery via text-mining PubMed abstracts
Authors
KeywordsBioinformatics
Biomedical text mining, information extraction
Periodontal diseases
Periodontal medicine
Periodontitis
Rheumatoid arthritis
Issue Date2018
PublisherWiley-Blackwell Publishing, Inc.. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=0022-3484&site=1
Citation
Journal of Periodontal Research, 2018 How to Cite?
AbstractBackground and Objective: Primary research concerning molecular pathways that link rheumatoid arthritis with periodontitis is limited. Biomedical literature data mining can offer insights into putative linkage mechanisms towards hypothesis development, based on information discovery. To aim of this study was to explore potential Periodontitis-Rheumatoid Arthritis biological links by analyzing ‘overlapping’ genes reported in biomedical abstracts. Materials and Methods: PubMed abstracts for terms: a) ‘Periodontitis’ or ‘Periodontal Diseases’ (PD), b) ‘Rheumatoid arthritis’ (RA), and c) their combination with ‘AND’ (RA+PD), were each text-mined to extract genes using ‘Human Genome Nomenclature Committee’ (HGNC) symbols. A gene-set common to RA and PD abstracts was determined (RA∩PD). Gene ontology (GO) profiles of RA∩PD and RA+PD were compared using ‘GoProfiler’. Minimum order protein-protein interaction (PPI) and gene-MiRNA networks of ‘differential genes’ between RA∩PD and RA+PD were constructed with ‘networkAnalyst’. Results: Among 1676 genes documented in RA (105241 abstracts), and 893 genes in PD (80982 abstracts), 535 genes were common (RA∩PD), from which 35 genes were also documented in RA+PD (415 abstracts). 41 GO-terms significantly different between RA∩PD and RA+PD GO profiles represented 38 biological processes including; nitric oxide metabolism, immunoglobulin production, hormonal regulation, catabolic process down-regulation, and leukocyte proliferation. The 500 differential genes’ PPI and gene-miRNA networks showed REL, TRAF2, AQP1 genes, and miRNAs 335-5p, 17-5p, 93-5p with genes HMOX1 and SP1 as hub nodes. Conclusions: Text mining biomedical abstracts revealed potentially shared but un-investigated links between PD and RA, meriting further research.
Persistent Identifierhttp://hdl.handle.net/10722/265997
ISSN
2017 Impact Factor: 2.878
2015 SCImago Journal Rankings: 0.932

 

DC FieldValueLanguage
dc.contributor.authorAcharya, A-
dc.contributor.authorLi, S-
dc.contributor.authorLiu, X-
dc.contributor.authorPelekos, G-
dc.contributor.authorZiebolz, D-
dc.contributor.authorMattheos, N-
dc.date.accessioned2018-12-17T02:16:29Z-
dc.date.available2018-12-17T02:16:29Z-
dc.date.issued2018-
dc.identifier.citationJournal of Periodontal Research, 2018-
dc.identifier.issn0022-3484-
dc.identifier.urihttp://hdl.handle.net/10722/265997-
dc.description.abstractBackground and Objective: Primary research concerning molecular pathways that link rheumatoid arthritis with periodontitis is limited. Biomedical literature data mining can offer insights into putative linkage mechanisms towards hypothesis development, based on information discovery. To aim of this study was to explore potential Periodontitis-Rheumatoid Arthritis biological links by analyzing ‘overlapping’ genes reported in biomedical abstracts. Materials and Methods: PubMed abstracts for terms: a) ‘Periodontitis’ or ‘Periodontal Diseases’ (PD), b) ‘Rheumatoid arthritis’ (RA), and c) their combination with ‘AND’ (RA+PD), were each text-mined to extract genes using ‘Human Genome Nomenclature Committee’ (HGNC) symbols. A gene-set common to RA and PD abstracts was determined (RA∩PD). Gene ontology (GO) profiles of RA∩PD and RA+PD were compared using ‘GoProfiler’. Minimum order protein-protein interaction (PPI) and gene-MiRNA networks of ‘differential genes’ between RA∩PD and RA+PD were constructed with ‘networkAnalyst’. Results: Among 1676 genes documented in RA (105241 abstracts), and 893 genes in PD (80982 abstracts), 535 genes were common (RA∩PD), from which 35 genes were also documented in RA+PD (415 abstracts). 41 GO-terms significantly different between RA∩PD and RA+PD GO profiles represented 38 biological processes including; nitric oxide metabolism, immunoglobulin production, hormonal regulation, catabolic process down-regulation, and leukocyte proliferation. The 500 differential genes’ PPI and gene-miRNA networks showed REL, TRAF2, AQP1 genes, and miRNAs 335-5p, 17-5p, 93-5p with genes HMOX1 and SP1 as hub nodes. Conclusions: Text mining biomedical abstracts revealed potentially shared but un-investigated links between PD and RA, meriting further research.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing, Inc.. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=0022-3484&site=1-
dc.relation.ispartofJournal of Periodontal Research-
dc.subjectBioinformatics-
dc.subjectBiomedical text mining, information extraction-
dc.subjectPeriodontal diseases-
dc.subjectPeriodontal medicine-
dc.subjectPeriodontitis-
dc.subjectRheumatoid arthritis-
dc.titleBiological links in Periodontitis and Rheumatoid arthritis: discovery via text-mining PubMed abstracts-
dc.typeArticle-
dc.identifier.emailAcharya, A: aneesha@hku.hk-
dc.identifier.emailPelekos, G: george74@hku.hk-
dc.identifier.emailMattheos, N: mattheos@hku.hk-
dc.identifier.authorityPelekos, G=rp01894-
dc.identifier.authorityMattheos, N=rp01662-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/jre.12632-
dc.identifier.pmid30536918-
dc.identifier.scopuseid_2-s2.0-85058214074-
dc.identifier.hkuros296248-
dc.publisher.placeUnited States-

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