Identification and validation of a robust autophagy-related molecular model for predicting the prognosis of breast cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34185683", "kind": "science-record", "revision": 6, "content_hash": "sha256:f64ee5cf9a6ab755f1a3e2f8db99dd64a4ab0990de0cf5cd80e1b0a0374368fc", "created_at": "2026-11-01T15:41:40Z", "updated_at": "2026-11-01T21:25:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a robust autophagy-related molecular model for predicting the prognosis of breast cancer patients.", "doi": "10.18632/aging.203187", "pmid": "34185683", "authors": ["Jian-Ying Ma", "Qin Liu", "Gang Liu", "Shasha Peng", "Gaosong Wu"], "journal": "Aging", "year": 2021, "abstract": "Despite a relatively low mortality rate, high recurrence rates represent a significant problem for breast cancer (BC) patients. Autophagy affects the development, progression, and prognosis of various cancers, including BC. The aim of the present study was to identify candidate autophagy-related genes (ARGs) and construct a molecular-clinicopathological signature to predict recurrence risk in BC. A 10-ARG-based signature was established in a training cohort (GEO-BC dataset GSE25066) with LASSO Cox regression and assessed in an independent validation cohort (GEO-BC GSE22219). Significant differences in recurrence-free survival were observed for high- and low-risk patients segregated based on their signature-based risk score. Time-dependent receiver operating characteristic (tdROC) analysis of signature performance demonstrated satisfactory accuracy and predictive power in both the training and validation cohorts. Moreover, we developed a nomogram to predict 3- and 5-year recurrence-free survival by combining the autophagy-related risk score and clinicopathological data. Both the tdROC and calibration curves indicated high discriminating ability for the nomogram. This study indicates that our ARG-based signature is an independent prognostic classifier for recurrence-free survival in BC. In addition, individualized survival risk assessment and treatment decisions might be effectively improved by implementing the proposed nomogram.", "claims": [{"id": "claim-method", "statement": "Then, we performed LASSO Cox regression analysis and identified, among those, 10 recurrence-related genes (ATF4, BAK1, BCL2, BIRC5, CCL2, DDIT3, HIF1A, PRKAB1, RPS6KB1, and TM9SF1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34185683", "locator": "sections[3] \"Development and validation of an autophagy-related prognostic gene signature for breast cancer\" paragraph 5", "quote": "Then, we performed LASSO Cox regression analysis and identified, among those, 10 recurrence-related genes (ATF4, BAK1, BCL2, BIRC5, CCL2, DDIT3, HIF1A, PRKAB1, RPS6KB1, and TM9SF1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE25066 and GSE22219 datasets analyzed in the current study are available from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). 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Additional information about results of this work is available from the corresponding author upon reasonable request."}], "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:34185683"], "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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