Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis.

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In the present study, we applied an integrated bioinformatics analysis to identify the aberrantly methylated and differentially expressed genes (DEGS), and their related pathways in cervical cancer. Data of gene expression microarrays (GSE9750) and gene methylation microarrays (GSE46306) were gained from Gene Expression Omnibus (GEO) databases. Hub genes were identified by 'limma' packages and Venn diagram tool. Functional analysis was conducted by FunRich. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein-protein interaction (PPI) information. Gene Expression Profiling Interactive Analysis (GEPIA), immunohistochemistry staining, and ROC curve analysis were conducted for validation. Gene Set Enrichment Analysis (GSEA) was also performed to identify potential functions.We retrieved two upregulated-hypomethylated oncogenes and eight downregulated-hypermethylated tumor suppressor genes (TSGs) for functional analysis. Hypomethylated and highly expressed genes (Hypo-HGs) were significantly enriched in cell cycle and autophagy, and hypermethylated and lowly expressed genes (Hyper-LGs) in estrogen receptor pathway and Wnt/β-catenin signaling pathway. Estrogen receptor 1 (ESR1), Erythrocyte membrane protein band 4.1 like 3 (EPB41L3), Endothelin receptor B (EDNRB), Inhibitor of DNA binding 4 (ID4) and placenta-specific 8 (PLAC8) were hub genes. Kaplan-Meier method was used to evaluate survival data of each identified gene. Lower expression levels of ESR1 and EPB41L3 were correlated with a shorter survival time. GSEA results showed that 'cell adhesion molecules' was the most enriched item. This research inferred the candidate genes and pathways that might be used in the diagnosis, treatment, and prognosis of cervical cancer.", "claims": [{"id": "claim-method", "statement": "To explore the DEGs and DMGs, we selected the ‘limma’ packages [29] to process in GSE9750 and GSE46306 datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32368784", "locator": "sections[4] \"Identification of DEGs and DMGs\" paragraph 2", "quote": "To explore the DEGs and DMGs, we selected the ‘limma’ packages [29] to process in GSE9750 and GSE46306 datasets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used or analyzed during the current study are available from the GEO (https://www.ncbi.nlm.nih.gov) and Cancer Genome Atlas database (https://cancergenome.nih.gov).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32368784", "locator": "sections[25] \"Data Availability\" paragraph 2", "quote": "The datasets used or analyzed during the current study are available from the GEO (https://www.ncbi.nlm.nih.gov) and Cancer Genome Atlas database (https://cancergenome.nih.gov)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:32368784"], "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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