Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1–2 breast cancer

• Published in: Scientific reports Vol. 12; no. 1; pp. 687 - 11
• Main Authors: Luo, Na, Wen, Ying, Zou, Qiongyan, Ouyang, Dengjie, Chen, Qitong, Zeng, Liyun, He, Hongye, Anwar, Munawar, Qu, Limeng, Ji, Jingfen, Yi, Wenjun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1347; 631/67/1857; Area Under Curve; Axilla; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Epidermal growth factor; ErbB-2 protein; Female; Forecasting; Humanities and Social Sciences; Humans; Infiltration; Logistic Models; Lymph nodes; Lymphatic Metastasis - diagnosis; Lymphatic Metastasis - genetics; Lymphatic system; Macrophages; Metastases; Metastasis; Middle Aged; Models, Theoretical; multidisciplinary; Neoplasm Staging; Prediction models; Prognosis; Receptor, ErbB-2; Risk; RNA, Messenger; Science; Science (multidisciplinary); Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-04495-y

The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1–2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR−/HER2−) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1–2 BC patients, particularly given that it can be used to adjust surgical options in the future.