Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32596300", "kind": "science-record", "revision": 7, "content_hash": "sha256:665bbf344bc76324635e16a7b5d9446591dba6020b940cf21179d10ab3c051f9", "created_at": "2026-11-01T15:41:04Z", "updated_at": "2026-11-01T22:01:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis.", "doi": "10.1155/2020/3416807", "pmid": "32596300", "authors": ["Xuting Xu", "Limin Xu", "Huilian Huang", "Jing Li", "Shunli Dong", "Lili Jin", "Zhihong Ma", "Liqin Li"], "journal": "BioMed research international", "year": 2020, "abstract": "Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan-Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the \"DNA replication,\" \"Cell cycle,\" and \"P53 signaling pathway\" pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan-Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p = 0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer.", "claims": [{"id": "claim-method", "statement": "Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis (http://www.kmplot.com) with the TCGA database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32596300", "locator": "sections[6] \"2.4. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE30219"], "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:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32596300", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2020/3416807", "sha256": 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