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pmid:34003815
Identification of a DNA Methylation-Based Prognostic Signature for Patients with Triple-Negative Breast Cancer.
Yinqi Gao · Xuelong Wang · Shihui Li · Zhiqiang Zhang · Xuefei Li · Fangcai Lin
Medical science monitor : international medical journal of experimental and clinical research · 2021 doi:10.12659/MSM.930025
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34003815", "kind": "science-record", "revision": 7, "content_hash": "sha256:eaf50082784ae130c7f5b82fa22cda06328ac28c9ba6acfc7435d00d8a5a08db", "created_at": "2026-11-01T15:41:35Z", "updated_at": "2026-11-01T22:03:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a DNA Methylation-Based Prognostic Signature for Patients with Triple-Negative Breast Cancer.", "doi": "10.12659/MSM.930025", "pmid": "34003815", "authors": ["Yinqi Gao", "Xuelong Wang", "Shihui Li", "Zhiqiang Zhang", "Xuefei Li", "Fangcai Lin"], "journal": "Medical science monitor : international medical journal of experimental and clinical research", "year": 2021, "abstract": "BACKGROUND Aberrant DNA methylation is an important biological regulatory mechanism in malignant tumors. However, it remains underutilized for establishing prognostic models for triple-negative breast cancer (TNBC). MATERIAL AND METHODS Methylation data and expression data downloaded from The Cancer Genome Atlas (TCGA) were used to identify differentially methylated sites (DMSs). The prognosis-related DMSs were selected by univariate Cox regression analysis. Functional enrichment was analyzed using DAVID. A protein-protein interaction (PPI) network was constructed using STRING. Finally, a methylation-based prognostic signature was constructed using LASSO method and further validated in 2 validation cohorts. RESULTS Firstly, we identified 743 DMSs corresponding to 332 genes, including 357 hypermethylated sites and 386 hypomethylated sites. Furthermore, we selected 103 prognosis-related DMSs by univariate Cox regression. Using a LASSO algorithm, we established a 5-DMSs prognostic signature in TCGA-TNBC cohort, which could classify TNBC patients with significant survival difference (log-rank p=4.97E-03). Patients in the high-risk group had shorter overall survival than patients in the low-risk group. The excellent performance was validated in GSE78754 (HR=2.42, 95%CI: 1.27-4.59, log-rank P=0.0055). Moreover, for disease-free survival, the prognostic performance was verified in GSE141441 (HR=2.09, 95%CI: 1.28-3.44, log-rank P=0.0027). Multivariate Cox regression analysis indicated that the 5-DMSs signature could serve as an independent risk factor. CONCLUSIONS We constructed a 5-DMSs signature with excellent performance for the prediction of disease-free survival and overall survival, providing a guide for clinicians in directing personalized therapeutic regimen selection of TNBC patients.", "claims": [{"id": "claim-method", "statement": "To better investigate the performance of those DMSs in predicting prognosis, a prognostic model was built by LASSO method using the glmnet R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34003815", "locator": "sections[7] \"Construction of Prognostic Signature\" paragraph 2", "quote": "To better investigate the performance of those DMSs in predicting prognosis, a prognostic model was built by LASSO method using the glmnet R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used and analyzed during the current study are available from The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34003815", "locator": "sections[19] \"Footnotes\" paragraph 2", "quote": "The data used and analyzed during the current study are available from The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/)."}], "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:34003815"], "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:GSE141441"], "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:GSE141441"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34003815", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.12659/MSM.930025", "sha256": 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