Integrative 3′ Untranslated Region-Based Model to Identify Patients with Low Risk of Axillary Lymph Node Metastasis in Operable Triple-Negative Breast Cancer

Author:

Wang Lei123,Hu Xin123,Wang Peng4,Shao Zhi-Ming1235

Affiliation:

1. Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China

2. Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China

3. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China

4. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China

5. Institutes of Biomedical Sciences, Fudan University, Shanghai, People's Republic of China

Abstract

Abstract Background Sentinel lymph node biopsy is the standard surgical staging approach for operable triple-negative breast cancer (TNBC) with clinically negative axillae. In this study, we sought to develop a model to predict TNBC patients with negative nodal involvement, who would benefit from the exemption of the axillary staging surgery. Materials and Methods We evaluated 3′ untranslated region (3′UTR) profiles using microarray data of TNBC from two Gene Expression Omnibus datasets. Samples from GSE31519 were divided into training set (n = 164) and validation set (n = 163), and GSE76275 was used to construct testing set (n = 164). We built a six-member 3′UTR panel (ADD2, COL1A1, APOL2, IL21R, PKP2, and EIF4G3) using an elastic net model to estimate the risk of lymph node metastasis (LNM). Receiver operating characteristic and logistic analyses were used to assess the association between the panel and LNM status. Results The six-member 3′UTR-panel showed a high distinguishing power with an area under the curve of 0.712, 0.729, and 0.708 in the training, validation, and testing sets, respectively. After adjustment by tumor size, the 3′UTR panel retained significant predictive power in the training, validation, and testing sets (odds ratio = 4.93, 4.58, and 3.59, respectively; p < .05 for all). A combinatorial analysis of the 3′UTR panel and tumor size yielded an accuracy of 97.2%, 100%, and 100% in training, validation, and testing set, respectively. Conclusion This study established an integrative 3′UTR-based model as a promising predictor for nodal negativity in operable TNBC. Although a prospective study is needed to validate the model, our results may permit a no axillary surgery option for selected patients. Implications for Practice Currently, sentinel lymph node biopsy is the standard approach for surgical staging in breast cancer patients with negative axillae. Prediction estimation for lymph node metastasis of breast cancer relies on clinicopathological characteristics, which is unreliable, especially in triple-negative breast cancer (TNBC)—a highly heterogeneous disease. The authors developed and validated an effective prediction model for the lymph node status of patients with TNBC, which integrates 3′UTR markers and tumor size. This is the first 3′UTR-based model that will help identify TNBC patients with low risk of nodal involvement who are most likely to benefit from exemption axillary surgery.

Funder

National Natural Science Foundation of China

Shanghai Committee of Science and Technology Funds

Ministry of Science and Technology of China

National Key R&D Program of China

Research Fund for the Doctoral Program of Higher Education of China

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3