An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34785957", "kind": "science-record", "revision": 3, "content_hash": "sha256:5d805e13b5f414124ddcb941ff0ddbd2fc18e70135ea6de9de19d42520c09061", "created_at": "2026-11-01T17:36:29Z", "updated_at": "2026-11-01T21:26:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer.", "doi": "10.2147/IJWH.S334756", "pmid": "34785957", "authors": ["Jing Yuan", "Fangfang Duan", "Wenyu Zhai", "Chenge Song", "Li Wang", "Wen Xia", "Xin Hua", "Zhongyu Yuan", "Xiwen Bi", "Jiajia Huang"], "journal": "International journal of women's health", "year": 2021, "abstract": "BACKGROUND: Aging, an inevitable process characterized by functional decline over time, is a significant risk factor for various tumors. However, little is known about aging-related genes (ARGs) in breast cancer (BC). We aimed to explore the potential prognostic role of ARGs and to develop an ARG-based prognosis signature for BC.\nMETHODS: RNA-sequencing expression profiles and corresponding clinicopathological data of female patients with BC were obtained from public databases in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). An ARG-based risk signature was constructed in the TCGA cohort based on results of least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, and its prognostic value was further validated in the GSE20685 cohort.\nRESULTS: A six ARG-based signature, including CLU, DGAT1, MXI1, NFKBI, PIK3CA and PLAU, was developed in the TCGA cohort and significantly stratified patients into low- and high-risk groups. Patients in the former group showed significantly better prognosis than those in the latter. Multivariate Cox regression analysis demonstrated that the ARG risk score was an independent prognostic factor for BC. A predictive nomogram integrating the ARG risk score and three identified factors (age, N- and M-classification) was established in the TCGA cohort and validated in the GSE20685 cohort. Calibration plots showed good consistency between predicted survival probabilities and actual observations.\nCONCLUSION: A novel ARG-based risk signature was developed for patients with BC, which can be used for individual prognosis prediction and promoting personalized treatment.", "claims": [{"id": "claim-method", "statement": "A least absolute shrinkage and selection operator (LASSO) regression analysis was then performed to narrow down the ARGs and determine the best weighting coefficient of each one.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34785957", "locator": "sections[4] \"Construction and Validation of the Prognostic ARG Signature\" paragraph 2", "quote": "A least absolute shrinkage and selection operator (LASSO) regression analysis was then performed to narrow down the ARGs and determine the best weighting coefficient of each one."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34785957", "locator": "sections[16] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets used and/or analyzed during current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The Cox regression analysis showed that the ARG risk signatures remained significantly related to OS after adjustment of other clinicopathological factors in the training cohort (HR = 3.17, 95% CI = 2.35–4.28, P < 0.001; Figure 3F).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The Cox regression analysis showed that the ARG risk signatures remained significantly related to OS after adjustment of other clinicopathological factors in the training cohort (HR = 3.17, 95% CI = 2.35–4.28, P < 0.001; Figure 3F)."}, "evidence": [{"asset": "source:pmid:34785957", "locator": "sections[10] \"Independent Prognostic Value of the ARG Signature in the Training Cohort\" paragraph 2", "quote": "The Cox regression analysis showed that the ARG risk signatures remained significantly related to OS after adjustment of other clinicopathological factors in the training cohort (HR = 3.17, 95% CI = 2.35–4.28, P < 0.001; Figure 3F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The Cox regression analysis showed that the ARG risk signatures remained significantly related to OS after adjustment of other clinicopathological factors in the training cohort (HR = 3.17, 95% CI = 2.35–4.28, P < 0.001; Figure 3F).", "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:34785957"], "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:GSE20685"], "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:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34785957", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJWH.S334756", "sha256": 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