A gene expression signature‐based nomogram model in prediction of breast cancer bone metastases. (21st December 2018)

• Record Type: Journal Article
• Authors: Zhao, Chenglong; Lou, Yan; Wang, Yao; Wang, Dongsheng; Tang, Liang; Gao, Xin; Zhang, Kun; Xu, Wei; Liu, Tielong; Xiao, Jianru
• Is Part Of: Cancer medicine. Volume 8:Number 1(2019:Jan.)
• Page Start: 200
• Page End: 208
• Publication Date: 2018-12-21
• Subjects: bone metastases -- breast cancer -- GSEA -- nomogram -- prognosis; 616.994005
• DOI: 10.1002/cam4.1932
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Breast cancer is prone to form bone metastases and subsequent skeletal‐related events (SREs) dramatically decrease patients' quality of life and survival. Prediction and early management of bone lesions are valuable; however, proper prognostic models are inadequate. In the current study, we reviewed a total of 572 breast cancer patients in three microarray data sets including 191 bone metastases and 381 metastases‐free. Gene set enrichment analysis (GSEA) indicated less aggressive and low‐grade features of patients with bone metastases compared with metastases‐free ones, while luminal subtypes are more prone to form bone metastases. Five bone metastases‐related genes (KRT23, REEP1, SPIB, ALDH3B2, and GLDC) were identified and subjected to construct a gene expression signature‐based nomogram (GESBN) model. The model performed well in both training and testing sets for evaluation of breast cancer bone metastases (BCBM). Clinically, the model may help in prediction of early bone metastases, prevention and management of SREs, and even help to prolong survivals for patients with BCBM. The five‐gene GESBN model showed some implications as molecular diagnostic markers and therapeutic targets. Furthermore, our study also provided a way for analysis of tumor organ‐specific metastases. To the best of our knowledge, this is the first published model focused on tumor organ‐specific metastases. Abstract : (a) Breast cancer bone metastases (BCBM) and subsequent skeletal‐related events (SREs) dramatically decrease patients' quality of life and survival. In the current study, we constructed a five‐gene expression signature‐based nomogram (GESBN) model for early prediction of bone metastases, prevention and management of SREs, and even help to prolong survivals for patients with BCBM. To the best of our knowledge, this is the first published model focusing on tumor organ specific metastases. And also the first attempt for construction such models in the field of bone oncology. (b) The five genes were weighted by the multivariate Cox regression model and further subjected to the nomogram scoring model. Nomograms have been developed and shown to be more accurate than the conventional staging systems for predicting prognosis in some cancers. (c) Our model has great importance for potential clinical evaluation. Based on our model, primary breast cancer patients could be classified as high or low incidence of bone metastasis. Based on the nomogram, even the potential time of occurrence of bone met could be predicted. These information are of great value in management of both primary and bone metastatic breast cancers.