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pmid:32825936
Identification of methylated-differentially expressed genes and pathways in esophageal squamous cell carcinoma.
Liang Sang · Zhanwu Yu · Ang Wang · Hao Li · Xiantong Dai · Liping Sun · Hongxu Liu · Yuan Yuan
Pathology, research and practice · 2020 doi:10.1016/j.prp.2020.153050
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Experiment
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Complete file
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This research is devoted to discover methylated-differentially expressed genes (MDEGs) in esophageal squamous cell carcinoma (ESCC) and explore special associated pathways. We downloaded GSE51287 methylation profiles and GSE26886 expression profiles from GEO DataSets, and performed a comprehensive bioinformatics analysis. Totally, 19 hypermethylated, lowly expressed genes (Hyper-LGs) were identified, and involved in regulation of cell proliferation, phosphorus metabolic process and protein kinase activity. Meanwhile, 17 hypomethylated, highly expressed genes (Hypo-HGs) were participated in collagen catabolic process, metallopeptidase and cytokine activity. Pathway analysis determined that Hyper-LGs were enriched in arachidonic acid metabolism pathway, while Hypo-HGs were primarily associated with the cytokine-cytokine receptor interaction pathway. IL 6, MMP3, MMP9, SPP1 were identified as hub genes based on the PPI network that combined 7 ranked methods included in cytoHubba, and verification was performed in human tissues. Our integrated analysis identified many novel genetic lesions in ESCC and provides a crucial molecular foundation to improve our understanding of ESCC. Hub genes, including IL 6, MMP3, MMP9 and SPP1, could be considered for use as aberrant methylation-based biomarkers to facilitate the accurate diagnosis and therapy of ESCC.", "claims": [{"id": "claim-method", "statement": "Differential expression between normal and ESCC tissue samples was identified using the online tool GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) across experimental conditions in the GEO series. 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"GO:0016310∼phosphorylation", "6", "31.58", "3.21E-03"], ["", "GOTERM_BP_FAT", "GO:0046777∼protein amino acid autophosphorylation", "3", "15.79", "5.59E-03"], ["", "GOTERM_BP_FAT", "GO:0006793∼phosphorus metabolic process", "6", "31.58", "7.41E-03"], ["", "GOTERM_CC_FAT", "GO:0012505∼endomembrane system", "4", "21.05", "4.12E-02"], ["", "GOTERM_CC_FAT", "GO:0044459∼plasma membrane part", "6", "31.58", "5.79E-02"], ["", "GOTERM_MF_FAT", "GO:0004672∼protein kinase activity", "6", "31.58", "8.44E-04"], ["", "GOTERM_MF_FAT", "GO:0004713∼protein tyrosine kinase activity", "4", "21.05", "1.22E-03"], ["", "GOTERM_MF_FAT", "GO:0004714∼transmembrane receptor protein tyrosine kinase activity", "3", "15.79", "3.39E-03"], ["", "GOTERM_MF_FAT", "GO:0005021∼vascular endothelial growth factor receptor activity", "2", "10.53", "1.04E-02"], ["", "GOTERM_MF_FAT", "GO:0005524∼ATP binding", "6", "31.58", "3.64E-02"], [""], ["Hypo-HGs", "GOTERM_BP_FAT", "GO:0030574∼collagen catabolic process", "4", 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analysis", "row_count": 5, "column_count": 6}, "table": {"label": "Table 6", "caption": "KEGG pathway analysis of MDEGs related to ESCC.", "rows": [["Category", "Term", "Count", "%", "PValue"], ["Hyper-LGs", "KEGG_PATHWAY", "hsa00590:Arachidonic acid metabolism", "2", "10.53", "9.49E-02"], [""], ["Hypo-HGs", "KEGG_PATHWAY", "hsa04620:Toll-like receptor signaling pathway", "3", "16.67", "1.58E-02"], ["", "KEGG_PATHWAY", "hsa04060:Cytokine-cytokine receptor interaction", "3", "16.67", "9.06E-02"]], "row_count": 5, "column_count": 6, "numeric_cells": 12, "header_rows": 1, "locator": "section[12]/table-wrap[1]#tbl0030"}}, {"id": "asset:table:pmid:32825936:7", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:83cbc1cf95ec13a6228f8f9126a3164b7ef99a497cbd900c991df71c89f7985a", "bytes": 574, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": 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The multiple t-test was used to detect statistically significant genes, and the Benjamin & Hochberg false discovery rate method is selected."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "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": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: ppi_network."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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