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

Abstract

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 < 0.001), disease-free survival (DFS) (HR 4.69, 2.93-7.50; P < 0.001) and overall survival (HR 4.06, 2.56-6.45; P < 0.001) than patients with low-risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical-related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4-mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.

Keywords: breast cancer; mRNAs signature; prognosis; sisease-free survival.

Figure 1

Study design for the identification of BC survival‐related 4‐mRNA signature

Figure 2

Construction of a 4‐mRNA signature. (A) Fifty‐six mRNAs selected by univariate analysis. Volcano plot illustrating a comparison of mRNAs expression in BC with metastasis‐free and metastasis. (B) Hierarchical clustering shows the collinearity of 26 candidate mRNAs. (C) LASSO algorithms was used to identify and evaluate the 4‐mRNA signature in the training cohort. Four mRNAs were selected and used to develop a 4‐mRNA signature to predict patients prognosis. (D) LASSO coefficient profiles of the 26 metastasis and prognostic‐related mRNAs based on the training data (GSE20685)

Figure 3

Analysis of the 4‐mRNA signature in the training cohort. The distribution of patients’ risk score and metastasis (A), disease (C) or death status (E); Kaplan‐Meier survival curves of MFS (B), DFS (D) and OS (F) between high‐risk and low‐risk patients in GSE20685

Figure 4

Validation of the 4‐mRNA signature in two independent validation cohorts. Kaplan‐Meier survival curves of DFS between high‐risk and low‐risk patients in (A) GSE21653 and (B) GSE31448

Figure 5

mRNA nomogram to predict the risk of disease in patients with BC. (A) mRNA nomogram to predict disease‐free survival. Calibration curves of the nomogram to predict disease‐free survival at 10 years in (B) GSE20685, (C) GSE21653 and (D) GSE31448. The actual disease‐free survival is plotted on the y‐axis; nomogram predicted probability is plotted on the x‐axis. DFS: disease‐free survival