ArticleInternational journal of women's health2021
An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer.
Article in International journal of women's health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Visual analytics framework for survival analysis and biomarker discovery from gene expression data.PloS one · 2026Article
- Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.Discover oncology · 2025Article
- Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.Frontiers in bioinformatics · 2025Article
- Construction and validation of an aging-related gene signature for prognosis prediction of patients with breast cancer.Cancer reports (Hoboken, N.J.) · 2023Article
- MLSP: A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer.Computational and structural biotechnology journal · 2022Article
- An Aging and Senescence-Related Gene Signature for Prognosis Prediction in Clear Cell Renal Cell Carcinoma.Frontiers in genetics · 2022Article
- A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Early-Stage Breast Cancer.Journal of inflammation research · 2022Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAging, 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.
methodsRNA-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.
resultsA six ARG-based signature, including
conclusionA novel ARG-based risk signature was developed for patients with BC, which can be used for individual prognosis prediction and promoting personalized treatment.
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