Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

• Published in: Journal of cellular and molecular medicine 2019-02, Vol.23 (2), p.1439-1447
• Main Authors: Xie, Xinhua, Wang, Jianwei, Shi, Dingbo, Zou, Yutian, Xiong, Zhenchong, Li, Xing, Zhou, Jianhua, Tang, Hailin, Xie, Xiaoming
• Format: Article
• Language: English
• Subjects: Adult; Biomarkers, Tumor - genetics; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Calibration; Disease-Free Survival; Female; Gene expression; Gene Expression Regulation, Neoplastic - genetics; Health risks; Humans; Kaplan-Meier Estimate; Metastases; Metastasis; mRNAs signature; Neoplasm Metastasis - genetics; Neoplasm Metastasis - pathology; Nomograms; Original; Prognosis; Risk assessment; Risk Factors; Risk groups; RNA, Messenger - genetics; sisease‐free survival; Survival; Transcriptome - genetics; Tumors
• ISSN: 1582-1838; 1582-4934
• DOI: 10.1111/jcmm.14049

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P