Open AccessA gene expression signature‐based nomogram model in prediction of breast cancer bone metastases
Author(s) -
Zhao Chenglong,
Lou Yan,
Wang Yao,
Wang Dongsheng,
Tang Liang,
Gao Xin,
Zhang Kun,
Xu Wei,
Liu Tielong,
Xiao Jianru
Publication year - 2019
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.1932
Subject(s) - nomogram , breast cancer , medicine , bone metastasis , gene signature , oncology , cancer , pathology , gene , gene expression , biology , biochemistry
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.