Identifying Tumorigenesis and Prognosis-Related Genes of Lung Adenocarcinoma: Based on Weighted Gene Coexpression Network Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32149105", "kind": "science-record", "revision": 3, "content_hash": "sha256:0de35cf1406323fb2521157b9395759029ebff6be1726ec5de8100b32a9c67d6", "created_at": "2026-11-01T15:40:57Z", "updated_at": "2026-11-01T21:22:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying Tumorigenesis and Prognosis-Related Genes of Lung Adenocarcinoma: Based on Weighted Gene Coexpression Network Analysis.", "doi": "10.1155/2020/4169691", "pmid": "32149105", "authors": ["Ming Yi", "Tianye Li", "Shuang Qin", "Shengnan Yu", "Qian Chu", "Anping Li", "Kongming Wu"], "journal": "BioMed research international", "year": 2020, "abstract": "Lung adenocarcinoma is the most frequently diagnosed subtype of nonsmall cell lung cancer. The molecular mechanisms of the initiation and progression of lung adenocarcinoma remain to be further determined. This study aimed to screen genes related to the progression of lung adenocarcinoma. By weighted gene coexpression network analysis (WGCNA), we constructed a free-scale gene coexpression network to evaluate the correlations between multiple gene sets and patients' clinical traits, then further identify predictive biomarkers. GSE11969 was obtained from the Gene Expression Omnibus (GEO) database which contained the gene expression data of 90 lung adenocarcinoma patients. Data of the Cancer Genome Atlas (TCGA) were employed as the validation cohort. After the average linkage hierarchical clustering, a total of 9 modules were generated. In the clinical significant module (R = 0.44, P < 0.0001), we identified 29 network hub genes. Subsequent verification in the TCGA database showed that 11 hub genes (ANLN, CDCA5, FLJ21924, LMNB1, MAD2L1, RACGAP1, RFC4, SNRPD1, TOP2A, TTK, and ZWINT) were significantly associated with poor survival data of lung adenocarcinomas. Besides, the results of receiver operating characteristic curves indicated that the mRNA levels of this group of genes exhibited high specificity and sensitivity to distinguish malignant lesions from nonmalignant tissues. Apart from mRNA levels, we found that the protein abundances of these 11 genes were remarkably upregulated in lung adenocarcinomas compared with normal tissues. In conclusion, by the WGCNA method, a panel of 11 genes were identified as predictive biomarkers for tumorigenesis and poor prognosis of lung adenocarcinomas.", "claims": [{"id": "claim-method", "statement": "By weighted gene coexpression network analysis (WGCNA), we constructed a free-scale gene coexpression network to evaluate the correlations between multiple gene sets and patients' clinical traits, then further identify predictive biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32149105", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "By weighted gene coexpression network analysis (WGCNA), we constructed a free-scale gene coexpression network to evaluate the correlations between multiple gene sets and patients' clinical traits, then further identify predictive biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Previously reported gene expression data (GSE11969) were used to support this study and are available at the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo). 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