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.

An immune‐related prognostic signature for predicting breast cancer recurrence

“…48 Several previous studies have reported prognostic signatures for BC. [49][50][51][52][53][54] Compared with the models, our prognostic model in this article has following advantages. Firstly, our prognostic model was build using several statistical analysis tools to ensure its rigor and accuracy.…”

Potential five‑mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma

“…A 24-mRNA signature model was also constructed for early-relapse prediction of patients with hepatocellular carcinoma, which may help facilitate disease management for individual patients (48). A metastasis-related 4-mRNA gene signature model has been demonstrated to effectively classify patients with breast cancer into high-and low-risk groups (49). In addition, Cui et al (38) developed a 6-mRNA signature model that may potentially improve the risk stratification of patients with head and neck squamous cell carcinoma (50).…”

Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer

“…Therefore, we used Cox model analysis to construct a gene signature that includes several genes to enhance prognostic prediction efficiency and sensitivity to TNBC. It has been widely confirmed that combined genetic models are superior to previous single gene markers in disease prediction and diagnoses [32].…”