Hub Genes and Long Noncoding RNAs That Regulates It Associated with the Prognosis of Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36238478", "kind": "science-record", "revision": 5, "content_hash": "sha256:9a8268dfdefd330d8bfee5d0660fa5bdee1e1538ed5493bce7ade81433259f6c", "created_at": "2026-11-01T18:15:37Z", "updated_at": "2026-11-01T22:50:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Hub Genes and Long Noncoding RNAs That Regulates It Associated with the Prognosis of Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis.", "doi": "10.1155/2022/6027058", "pmid": "36238478", "authors": ["Jun Lu", "Ruichao Li", "Minghao Fang", "Shun Ke"], "journal": "Computational and mathematical methods in medicine", "year": 2022, "abstract": "Objective: Through bioinformatics analysis methods, the public databases GEO and TCGA were used to research mRNA and squamous cell carcinoma of the esophagus, construct a lncRNA-mRNA network, and screen hub genes and lncRNAs related to prognosis.\nMethod: Download esophageal squamous cell carcinoma-related mRNA and lncRNA datasets GEO and TCGA public datasets, as well as clinical data, use bioinformatic tools to perform gene differential expression analysis on the datasets to obtain differentially expressing mRNA (DEmRNA) and lncRNA (DElncRNA), and plot volcano plots and cluster heatmaps. The differential intersection of differentially expressed DEmRNA and DElncRNA was extracted by Venn diagram and imported into CytoScape software, a regulatory network visualization software, to construct a lncRNA-mRNA network and use cytoHubba and MCODE plug-ins to screen hub genes and key lncRNAs. The DEmRNA in the network was imported into the Gene and Protein Interaction Retrieval Database (STRING), gene-encoded protein-protein interactions (PPI) network maps were created, and the genes in the PPI network maps were submitted to GO functional annotation and pathway enrichment analysis using Kyoto Encyclopedia of Gene Genomes (KEGG) (KEGG). The link between hub gene and prognosis was studied using the clinical data collected by TCGA.\nResult: Retrieve the datasets GSE23400 and GSE38129 from the GEO database and the esophageal squamous cell carcinoma-related mRNAs from TCGA databases and then obtain intersection. Differentially regulated genes revealed a correlation of 326 (up) with 191 (down) in terms of the differential intersection; for this study, we need to collect the GSE130078 dataset from GEO, as well as the lncRNAs from TCGA databases that are connected to esophageal squamous cell cancer. There were 184 differentially up- and downregulated genes in the differential intersection. A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3). The extracellular matrix, which is important in protein digestion and absorption, was shown to be the primary site of functional enrichment, as shown by GO/KEGG analysis. Squamous cell carcinoma of the mouth and throat is associated with a poor prognosis because of a change in the extracellular matrix structure caused by specific long noncoding RNA (lncRNA) regulatory upregulation.\nConclusion: For the purpose of predicting the prognosis of cancer of the esophagus, researchers studied the esophageal squamous cell carcinoma-related hub genes and important noncoding RNAs (ncRNAs).", "claims": [{"id": "claim-method", "statement": "Use R language limma or edgeR package [12] to calculate differential expression, and draw Venn diagram to take differential intersection.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36238478", "locator": "sections[7] \"2.5. Statistical Methods\" paragraph 2", "quote": "Use R language limma or edgeR package [12] to calculate differential expression, and draw Venn diagram to take differential intersection."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The labeled dataset used to support the findings of this study is available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36238478", "locator": "sections[15] \"Data Availability\" paragraph 2", "quote": "The labeled dataset used to support the findings of this study is available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36238478", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3).", "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:36238478"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE130078"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE130078"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36238478", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/6027058", "sha256": 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