A ten N6-methyladenosine-related long non-coding RNAs signature predicts prognosis of triple-negative breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33934391", "kind": "science-record", "revision": 6, "content_hash": "sha256:2e6d619d71acac38f911e45262e4b08ffc7dbafac12144936294c229fbd1097d", "created_at": "2026-11-01T15:41:34Z", "updated_at": "2026-11-01T21:25:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A ten N6-methyladenosine-related long non-coding RNAs signature predicts prognosis of triple-negative breast cancer.", "doi": "10.1002/jcla.23779", "pmid": "33934391", "authors": ["Jie Wu", "Yan Cai", "Gaiping Zhao", "Maolan Li"], "journal": "Journal of clinical laboratory analysis", "year": 2021, "abstract": "BACKGROUND: Patients with triple-negative breast cancer (TNBC) face a major challenge of the poor prognosis, and N6-methyladenosine-(m6A) mediated regulation in cancer has been proposed. Therefore, this study aimed to explore the prognostic roles of m6A-related long non-coding RNAs (LncRNAs) in TNBC.\nMETHODS: Clinical information and expression data of TNBC samples were collected from TCGA and GEO databases. Pearson correlation, univariate, and multivariate Cox regression analysis were employed to identify independent prognostic m6A-related LncRNAs to construct the prognostic score (PS) risk model. Receiver operating characteristic (ROC) curve was used to evaluate the performance of PS risk model. A competing endogenous RNA (ceRNA) network was established for the functional analysis on targeted mRNAs.\nRESULTS: We identified 10 independent prognostic m6A-related LncRNAs (SAMD12-AS1, BVES-AS1, LINC00593, MIR205HG, LINC00571, ANKRD10-IT1, CIRBP-AS1, SUCLG2-AS1, BLACAT1, and HOXB-AS1) and established a PS risk model accordingly. Relevant results suggested that TNBC patients with lower PS had better overall survival status, and ROC curves proved that the PS model had better prognostic abilities with the AUC of 0.997 and 0.864 in TCGA and GSE76250 datasets, respectively. Recurrence and PS model status were defined as independent prognostic factors of TNBC. These ten LncRNAs were all differentially expressed in high-risk TNBC compared with controls. The ceRNA network revealed the regulatory axes for nine key LncRNAs, and mRNAs in the network were identified to function in pathways of cell communication, signaling transduction and cancer.\nCONCLUSION: Our findings proposed a ten-m6A-related LncRNAs as potential biomarkers to predict the prognostic risk of TNBC.", "claims": [{"id": "claim-method", "statement": "Integrating with the clinical information, the univariate Cox regression analysis in R package “survival” version 2.41‐1 was implemented to filter out prognostic m6A‐related LncRNAs, 23 and then, the multivariate Cox regression analysis was used to identify independent prognostic m6A‐related LncRNAs. Expression data of RNA‐seq and clinical information of 153 TNBC samples (ER‐, PR‐ and HER‐2) was gathered from TCGA database, and GSE76250 dataset was screened out from NCBI‐GEO according to the following criteria: (1) has information to classify tumor subtypes; (2) has entity tumor sample tissues; (3) the sample size is over 100; and (4) more LncRNAs can be annotated from the testing platform.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33934391", "locator": "sections[4] \"Screening for independent prognostic m6A‐related LncRNAs\" paragraph 2", "quote": "Integrating with the clinical information, the univariate Cox regression analysis in R package “survival” version 2.41‐1 was implemented to filter out prognostic m6A‐related LncRNAs, 23 and then, the multivariate Cox regression analysis was used to identify independent prognostic m6A‐related LncRNAs."}, {"asset": "source:pmid:33934391", "locator": "sections[3] \"Data acquisition and identification of m6A‐related LncRNAs\" paragraph 2", "quote": "Expression data of RNA‐seq and clinical information of 153 TNBC samples (ER‐, PR‐ and HER‐2) was gathered from TCGA database, and GSE76250 dataset was screened out from NCBI‐GEO according to the following criteria: (1) has information to classify tumor subtypes; (2) has entity tumor sample tissues; (3) the sample size is over 100; and (4) more LncRNAs can be annotated from the testing platform."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In this study, the data from TCGA database are available from https://gdc‐portal.nci.nih.gov/, while the GSE76250 dataset can be found in NCBI‐GEO (http://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33934391", "locator": "sections[19] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "In this study, the data from TCGA database are available from https://gdc‐portal.nci.nih.gov/, while the GSE76250 dataset can be found in NCBI‐GEO (http://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:33934391"], "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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