A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34247575", "kind": "science-record", "revision": 7, "content_hash": "sha256:2ff91bc41547b8647df10b98e29d7d67a85a57dda49d0a222637bb02f0c7098b", "created_at": "2026-11-01T15:41:42Z", "updated_at": "2026-11-01T22:04:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma.", "doi": "10.1186/s12885-021-08539-4", "pmid": "34247575", "authors": ["Xuelong Wang", "Bin Zhou", "Yuxin Xia", "Jianxin Zuo", "Yanchao Liu", "Xin Bi", "Xiong Luo", "Chengwei Zhang"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.\nMETHOD: The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.\nRESULT: We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.\nCONCLUSIONS: The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.", "claims": [{"id": "claim-method", "statement": "For each methylation site, we test the difference in methylation level between tumor and normal tissues to select differentially methylated site (DMS) by T-test with p < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34247575", "locator": "sections[4] \"Identification of differentially methylated sites\" paragraph 2", "quote": "For each methylation site, we test the difference in methylation level between tumor and normal tissues to select differentially methylated site (DMS) by T-test with p < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34247575", "locator": "sections[26] \"notes\" paragraph 1", "quote": "All data generated or analyzed during this study are included in this published article."}], "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:34247575"], "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. 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