The identification of hub genes associated with pure ground glass nodules using weighted gene co-expression network analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38858671", "kind": "science-record", "revision": 5, "content_hash": "sha256:dce2163f7df0ff6a08027f786d86dfd9d2f93dbf9e2bbf61b28f76324344c441", "created_at": "2026-11-01T19:20:48Z", "updated_at": "2026-11-01T22:54:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The identification of hub genes associated with pure ground glass nodules using weighted gene co-expression network analysis.", "doi": "10.1186/s12890-024-03072-z", "pmid": "38858671", "authors": ["Yuan Cheng", "Zuoqing Song"], "journal": "BMC pulmonary medicine", "year": 2024, "abstract": "BACKGROUND: Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis.\nMETHODS: To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients' tumor tissues compared to controls.\nRESULTS: A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients' tumor tissues compared to controls.\nCONCLUSIONS: To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.", "claims": [{"id": "claim-method", "statement": "Based on generalized linear models, Limma [10] is a method for screening differential expression data from microarrays, here we use limma (version 3.40.6) on SangerBOX to identify DEGs between the control and pGGN groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38858671", "locator": "sections[4] \"DEGs identification\" paragraph 2", "quote": "Based on generalized linear models, Limma [10] is a method for screening differential expression data from microarrays, here we use limma (version 3.40.6) on SangerBOX to identify DEGs between the control and pGGN groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mRNA expression dataset used in our study was downloaded from the Gene Expression Omnibus (GEO) under accession number GSE193725 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE, accessed on 22 March 2023).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38858671", "locator": "sections[24] \"Data availability\" paragraph 2", "quote": "The mRNA expression dataset used in our study was downloaded from the Gene Expression Omnibus (GEO) under accession number GSE193725 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE, accessed on 22 March 2023)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified six overlapped hub genes and found that CCT3 and RPL8 were more highly expressed in pGGN tissues than in adjacent tissues, supporting their potential as biomarkers for pGGN.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38858671", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients’ tumor tissues compared to controls."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified six overlapped hub genes and found that CCT3 and RPL8 were more highly expressed in pGGN tissues than in adjacent tissues, supporting their potential as biomarkers for pGGN.", "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:38858671"], "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:GSE193725"], "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:GSE193725"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38858671", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12890-024-03072-z", "sha256": "sha256:9315b50fb98a6eb93a0912d064c86c46ae8720bdaff35940c41d30a2dbb9f78c", "bytes": 31334, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12890-024-03072-z", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11165796", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE193725", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE193725", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE193725", "version": null, "note": "The mRNA expression dataset used in our study was downloaded from the Gene Expression Omnibus (GEO) under accession number GSE193725 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? 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