Identification of a novel CpG methylation signature to predict prognosis in lung squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32924987", "kind": "science-record", "revision": 3, "content_hash": "sha256:d19380b43ccfc5b697a432a795cf9730b8fa9ecbfb0762fd27a141da979b44ce", "created_at": "2026-11-01T17:34:05Z", "updated_at": "2026-11-01T21:23:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a novel CpG methylation signature to predict prognosis in lung squamous cell carcinoma.", "doi": "10.3233/CBM-201564", "pmid": "32924987", "authors": ["Nan Lee", "Xuelian Xia", "Hui Meng", "Weiliang Zhu", "Xiankai Wang", "Tianyuan Zhang", "Chanyuan Zhang", "Jian Zhang", "Peng Luo"], "journal": "Cancer biomarkers : section A of Disease markers", "year": 2021, "abstract": "BACKGROUND: DNA methylation plays a vital role in modulating genomic function and warrants evaluation as a biomarker for the diagnosis and treatment of lung squamous cell carcinoma (LUSC).\nOBJECTIVE: In this study, we aimed to identify effective potential biomarkers for predicting prognosis and drug sensitivity in LUSC.\nMETHODS: A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO).\nRESULTS: A methylated region consisting of 3 sites (cg06675147, cg07064331, cg20429172) was selected. Patients were divided into a high-risk group and a low-risk group in the training dataset. High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82-4.07, P< 0.001) compared with low-risk patients. The accuracy of the prognostic signature was validated in the test and validation cohorts (TCGA, n= 94; GSE56044, n= 23). Gene set variation analysis (GSVA) showed that activity in the cell cycle/mitotic, ERBB, and ERK/MAPK pathways was higher in the high-risk compared with the low-risk group, which may lead to differences in OS.Interestingly, we observed that patients in the high-risk group were more sensitive to gemcitabine and docetaxel than the low-risk group, which is consistent with results of the GSVA.\nCONCLUSION: We report novel methylation sites that could be used as powerful tools for predicting risk factors for poorer survival in patients with LUSC.", "claims": [{"id": "claim-method", "statement": "A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32924987", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32924987", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82–4.07, P< 0.001) compared with low-risk patients.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82–4.07, P< 0.001) compared with low-risk patients."}, "evidence": [{"asset": "source:pmid:32924987", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82–4.07, P< 0.001) compared with low-risk patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82–4.07, P< 0.001) compared with low-risk patients.", "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:32924987"], "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:GSE56044"], "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:GSE56044"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32924987", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3233/CBM-201564", "sha256": "sha256:5f07833a38aef783a4127ca32e880f8e1abe0fbb16cb94b843819da0e9af3def", "bytes": 1650, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3233/CBM-201564", "version": null, "note": "Full article fetched from PMC; 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