A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer

Abstract

Background: The prognosis of lymph node-negative triple-negative breast cancer (TNBC) is still worse than that of other subtypes despite adjuvant chemotherapy. Reliable prognostic biomarkers are required to identify lymph node-negative TNBC patients at a high risk of distant metastasis and optimize individual treatment.

Methods: We analyzed the RNA sequencing data of primary tumor tissue and the clinicopathological data of 202 lymph node-negative TNBC patients. The cohort was randomly divided into training and validation sets. Least absolute shrinkage and selection operator Cox regression and multivariate Cox regression were used to construct the prognostic model.

Results: A clinical prognostic model, seven-gene signature, and combined model were constructed using the training set and validated using the validation set. The seven-gene signature was established based on the genomic variables associated with distant metastasis after shrinkage correction. The difference in the risk of distant metastasis between the low- and high-risk groups was statistically significant using the seven-gene signature (training set: P < 0.001; validation set: P = 0.039). The combined model showed significance in the training set (P < 0.001) and trended toward significance in the validation set (P = 0.071). The seven-gene signature showed improved prognostic accuracy relative to the clinical signature in the training data (AUC value of 4-year ROC, 0.879 vs. 0.699, P = 0.046). Moreover, the composite clinical and gene signature also showed improved prognostic accuracy relative to the clinical signature (AUC value of 4-year ROC: 0.888 vs. 0.699, P = 0.029; AUC value of 5-year ROC: 0.882 vs. 0.693, P = 0.038). A nomogram model was constructed with the seven-gene signature, patient age, and tumor size.

Conclusions: The proposed signature may improve the risk stratification of lymph node-negative TNBC patients. High-risk lymph node-negative TNBC patients may benefit from treatment escalation.

Keywords: distant metastasis; modeling; prognostic biomarker; transcriptomics; triple-negative breast cancer.

Figure 1

Flowchart of study design. TNBC, triple-negative breast cancer; FC, fold change; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.

Figure 2

Volcano plot for differentially expressed mRNAs between patients with and without distant metastasis. In total, 71 differentially expressed mRNAs were screened out with log₂(fold change) > 1 or < -1 and P < 0.05. Significantly upregulated and downregulated mRNAs are shown as red and blue dots, respectively.

Figure 3

Time-dependent receiver operating characteristic (ROC), Kaplan–Meier survival analysis, and risk score analysis for the seven-gene signature in the training set and validation set of the lymph node-negative triple-negative breast cancer (TNBC) cohort. AUC, area under the curve. (A) Time-dependent ROC curves of the seven-gene signature for 3-, 4-, and 5-year distant metastasis-free survival (DMFS). (B) Kaplan–Meier plots of the seven-gene signature illustrating that the patients in the high-risk group showed poorer DMFS than those in the low-risk group. (C) Distribution of genomic risk score, DMFS status of patients, and heat map of seven differentially expressed mRNA expression profiles.

Figure 4

Time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival analysis for the clinical model and combined model in the training set and validation set of the lymph node-negative triple-negative breast cancer (TNBC) cohort. AUC, area under the curve. (A) Time-dependent ROC curves of the clinical model for 3-, 4-, and 5-year distant metastasis-free survival (DMFS). (B) Time-dependent ROC curves of the combined model for 3-, 4-, and 5-year DMFS. (C) Kaplan–Meier plots of the combined model illustrating that the patients in the high-risk group showed poorer DMFS than those in the low-risk group.

Figure 5

A predictive nomogram was established in the training set. AUC, area under the curve. (A) The nomogram was built by the seven-gene risk score and clinical characteristics, including age and tumor size. (B) The time-dependent receiver operating characteristic (ROC) curves of the seven-gene model, clinical model, and combined model for 4- and 5-year distant metastasis-free survival (DMFS). The combined model was better than the clinical model for predicting 4-year (P = 0.029) and 5-year (P = 0.038) DMFS. (C) Calibration plots of the nomogram for 4-year DMFS. (D) Decision curve analysis (DCA) of the seven-gene model, clinical model, and combined model for 4-year DMFS.