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pmid:32439908
Bioinformatic analysis reveals the importance of epithelial-mesenchymal transition in the development of endometriosis.
Meihong Chen · Yilu Zhou · Hong Xu · Charlotte Hill · Rob M Ewing · Deming He · Xiaoling Zhang · Yihua Wang
Scientific reports · 2020 doi:10.1038/s41598-020-65606-9
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Experiment
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Complete file
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However, its etiology and pathogenesis are still unclear.\nMETHODS: To identify key genes/pathways involved in the pathogenesis of endometriosis, we recruited 3 raw microarray datasets (GSE11691, GSE7305, and GSE12768) from Gene Expression Omnibus database (GEO), which contain endometriosis tissues and normal endometrial tissues. We then performed in-depth bioinformatic analysis to determine differentially expressed genes (DEGs), followed by gene ontology (GO), Hallmark pathway enrichment and protein-protein interaction (PPI) network analysis. The findings were further validated by immunohistochemistry (IHC) staining in endometrial tissues from endometriosis or control patients.\nRESULTS: We identified 186 DEGs, of which 118 were up-regulated and 68 were down-regulated. The most enriched DEGs in GO functional analysis were mainly associated with cell adhesion, inflammatory response, and extracellular exosome. We found that epithelial-mesenchymal transition (EMT) ranked first in the Hallmark pathway enrichment. EMT may potentially be induced by inflammatory cytokines such as CXCL12. IHC confirmed the down-regulation of E-cadherin (CDH1) and up-regulation of CXCL12 in endometriosis tissues.\nCONCLUSIONS: Utilizing bioinformatics and patient samples, we provide evidence of EMT in endometriosis. Elucidating the role of EMT will improve the understanding of the molecular mechanisms involved in the development of endometriosis.", "claims": [{"id": "claim-method", "statement": "Differential expression analysis was performed on these datasets in limma, and those genes with P value <0.05 and Log[FoldChange] (Log[FC]) > 1 were considered as DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32439908", "locator": "sections[12] \"Identification of Differentially Expressed Genes (DEGs) using integrated bioinformatics\" paragraph 2", "quote": "Differential expression analysis was performed on these datasets in limma, and those genes with P value <0.05 and Log[FoldChange] (Log[FC]) > 1 were considered as DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data and materials from this study are available upon a written request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32439908", "locator": "sections[25] \"notes\" paragraph 1", "quote": "Data and materials from this study are available upon a written request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified 186 DEGs, of which 118 were up-regulated and 68 were down-regulated.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:32439908", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We identified 186 DEGs, of which 118 were up-regulated and 68 were down-regulated."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified 186 DEGs, of which 118 were up-regulated and 68 were down-regulated.", "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:32439908"], "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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