Identification of aberrantly methylated differentially expressed genes and associated pathways in endometrial cancer using integrated bioinformatic analysis.

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In this study, GSE17025 and GSE40032 were obtained from Gene Expression Omnibus (GEO). \"limma\" package and Venn diagram tool were used to identify hub genes. FunRich was used for functional analysis. Retrieval of Interacting Genes Database (STRING) was used to analyze protein-protein interaction (PPI) complex. Cancer Genome Atlas (TCGA), GEPIA, immunohistochemistry staining, and ROC curve analysis were carried out for validation. Univariate and multivariate regression analyses were performed to predict the risk score. Compound muscle action potential (CMap) was used to find potential drugs. GSEA was also done. We retrieved seven oncogenes which were upregulated and hypomethylated and 12 tumor suppressor genes (TSGs) which were downregulated and hypermethylated. The upregulated and hypomethylated genes were strikingly enriched in term \"immune response\" while the downregulated and hypermethylated genes were mainly focused on term \"aromatic compound catabolic process.\" TCGA and GEPIA were used to screen out EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1. Among them, ESPL1 and ROR2 were identified by Cox regression analysis and were used to construct prognostic risk model. The result showed that ESPL1 was a negative independent prognostic factor. Cmap identified aminoglutethimide, luteolin, sulfadimethoxine, and maprotiline had correlation with EC. GSEA results showed that \"hedgehog signaling pathway\" was enriched. This research inferred potential aberrantly methylated DEGs and dysregulated pathways may participate in EC development and firstly reported eight hub genes, including EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1 that could be used to predict EC prognosis. Aminoglutethimide and luteolin may be used to fight against EC.", "claims": [{"id": "claim-method", "statement": "Univariate Cox proportional hazards regression analyses were performed for the above eight DEGs, including ESPL1, NDN, ROR2, and PLCD1 (Table 1). Multivariate Cox proportional hazards regression analysis was further performed on the above genes, which screened ROR2 and ESPL1 (Figure S1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32170852", "locator": "sections[19] \"Prognostic signature\" paragraph 2", "quote": "Univariate Cox proportional hazards regression analyses were performed for the above eight DEGs, including ESPL1, NDN, ROR2, and PLCD1 (Table 1). Multivariate Cox proportional hazards regression analysis was further performed on the above genes, which screened ROR2 and ESPL1 (Figure S1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All the data in this study are available from GEO database and TCGA database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32170852", "locator": "sections[28] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "All the data in this study are available from GEO database and TCGA database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In ROC curve analysis, the AUC value for 5‐year survival showed the highest specificity and sensitivity when the risk score was 0.633 (95% confidence interval[CI] 0.52‐0.74) (Figure 9B).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "In ROC curve analysis, the AUC value for 5‐year survival showed the highest specificity and sensitivity when the risk score was 0.633 (95% confidence interval[CI] 0.52‐0.74) (Figure 9B)."}, "evidence": [{"asset": "source:pmid:32170852", "locator": "sections[19] \"Prognostic signature\" paragraph 2", "quote": "In ROC curve analysis, the AUC value for 5‐year survival showed the highest specificity and sensitivity when the risk score was 0.633 (95% confidence interval[CI] 0.52‐0.74) (Figure 9B)."}, {"asset": "source:pmid:32170852", "locator": "sections[19] \"Prognostic signature\" paragraph 2", "quote": "The risk score for predicting overall survival was calculated as follows: Risk score = 0.336* ESPL1‐0.101* ROR2."}, {"asset": "source:pmid:32170852", "locator": "sections[11] \"Gene set enrichment analysis (GSEA)\" paragraph 2", "quote": "From TCGA, 546 EC samples were divided into two different groups based on their median expression levels."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In ROC curve analysis, the AUC value for 5‐year survival showed the highest specificity and sensitivity when the risk score was 0.633 (95% confidence interval[CI] 0.52‐0.74) (Figure 9B).", "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:32170852"], "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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