Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33717664", "kind": "science-record", "revision": 6, "content_hash": "sha256:4cd827eedd9ddfcabe4b5aa0141df7cd75aa5677cd8a87474a9344cfd50e4448", "created_at": "2026-11-01T15:41:28Z", "updated_at": "2026-11-01T21:24:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis.", "doi": "10.7717/peerj.10682", "pmid": "33717664", "authors": ["Chunyang Li", "Haopeng Yu", "Yajing Sun", "Xiaoxi Zeng", "Wei Zhang"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy.\nMETHODS: GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the \"least absolute shrinkage and selection operator\" (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm.\nRESULTS: Twelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946.\nCONCLUSIONS: These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.", "claims": [{"id": "claim-method", "statement": "We calculated log10 transformation of the P-value in the logistic regression between the MEs and clinical trait.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33717664", "locator": "sections[6] \"Identification of clinically significant modules\" paragraph 2", "quote": "We calculated log10 transformation of the P-value in the logistic regression between the MEs and clinical trait."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Availability Statement\nThe following information was supplied regarding data availability:\nR code and training set, pre-derivation analysis, and testing set raw data are available in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33717664", "locator": "sections[29] \"Data Availability Statement\" paragraphs 1-3", "quote": "Data Availability Statement\nThe following information was supplied regarding data availability:\nR code and training set, pre-derivation analysis, and testing set raw data are available in the Supplemental Files."}], "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:33717664"], "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:GSE13911"], "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:GSE13911"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33717664", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.10682", "sha256": 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