WeChat Mini Program
Old Version Features

Development and Validation of a 10-Gene Signature for Predicting Recurrence Risk in HR+/HER2- Early Breast Cancer Undergoing Chemo-Endocrine Therapy

Xiaoyan Wu, Xunxi Lu, Wenchuan Zhang,Xiaorong Zhong,Hong Bu, Zhang

Breast (Edinburgh, Scotland)(2025)

Department of Pathology

Cited 0|Views1
Abstract
BACKGROUND:While existing multi-gene assays aid adjuvant treatment decisions, no gene signature has identified HR+/HER2- early breast cancer (EBC) patients at high recurrence risk post-chemo-endocrine therapy (C-ET). METHODS:Clinical data and RNA sequencing information from 1457 HR+/HER2- breast cancer patients were collected from West China Hospital, the GEO database, and the TCGA database. Using univariate Cox regression, gene set enrichment analysis, and LASSO regression, ten key genes associated with recurrence were identified. A comprehensive prognostic model was developed by combining the 10-gene risk score with clinicopathological features, and a nomogram was created to predict 3-, 5-, and 7-year recurrence-free survival (RFS). The model's performance was evaluated using AUC and decision curve analysis (DCA). RESULTS:The 10-gene risk score was significantly associated with recurrence risk of HR+/HER2- EBC after C-ET and effectively distinguished between high-risk and low-risk patients (training: HR: 6.37, P < 0.001; validation: HR: 4.51, P < 0.001). It maintained consistent stratification efficacy across different treatment regimens, clinical stages, and grades. Compared to existing multi-gene signatures (21-gene, 70-gene, EndoPredict, PAM50, GGI), HR+/HER2- EBC patients identified as high-risk by the 10-gene risk score exhibited a higher 10-year cumulative recurrence rate following C-ET. In multivariate Cox regression analysis, the 10-gene risk score remained an independent prognostic factor in both the training and validation sets. The comprehensive model, integrating the 10-gene score and clinicopathological features, showed high predictive accuracy (AUC: 0.734, 0.778, 0.792 for 3, 5, 7 years in training; 0.691, 0.715, 0.709 in validation). CONCLUSION:The 10-gene risk score can serve as a tool to predict recurrence risk in HR+/HER2- EBC patients following C-ET, assisting clinicians in developing personalized treatment plans for high-risk patients and ultimately improving patient prognosis.
More
Translated text
Key words
HR+/HER2- early breast cancer,10-Gene risk score,Recurrence risk prediction,Chemo-endocrine therapy,Prognostic model
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined