Establishing nomograms for predicting disease-free survival and overall survival in patients with breast cancer.

ABSTRACT

BACKGROUND: Prognostic factors-based nomograms have been utilised to detect the likelihood of the specific cancer events. We have focused on the roles of aldehyde dehydrogenase 1 (ALDH1) and p-AKT in predicting the prognosis of BC patients. This study was designed to establish nomograms based on the integration of aldehyde dehydrogenase 1 (ALDH1) and p-AKT in predicting the disease-free survival (DFS) and overall survival (OS) of breast cancer (BC) patients. METHODS: Demographic and clinical data were obtained from BC patients admitted to our hospital between September 2015 and August 2016. Univariate and multivariate Cox regression analyses were utilised to analyse the risk factors of recurrence and mortality. The nomograms for predicting the DFS and OS were established using the screened risk factors. Stratified analysis was performed with the cut-off value of exp (pi) of 4.0-fold in DFS and OS, respectively. RESULTS: Multivariate Cox regression analysis indicated that ALDH, p-AKT and pathological stage III were independent risk factors for the recurrence among BC patients. ALDH1, p-AKT, pathological stage III and ER-/PR-/HER2- were independent risk factors for the mortality among BC patients. The established nomograms based on these factors were effective for predicting the DFS and OS with good agreement to the calibration curve and acceptable area under the receiver operating characteristic (ROC) curve. Finally, stratified analyses showed patients with a low pi showed significant decrease in the DFS and OS compared with those of high risk. CONCLUSION: We established nomograms for predicting the DFS and OS of BC patients based on ALDH1, p-AKT and pathological stages. The ER-/PR-/HER2- may be utilised to predict the OS rather than DFS in the BC patients.

Many breast cancer patients show poor response after treatment due to recurrence and metastasis. Therefore, early prediction of the disease-free survival and overall survival is crucial to the treatment outcome and clinical decision-making. In this study, we established nomograms with the demographic and clinical data from breast cancer patients admitted to our hospital between September 2015 and August 2016. Univariate and multivariate Cox regression analyses showed that some important proteins and signalling pathways were risk factors for decreased disease-free survival and overall survival of breast cancer patients. On this basis, we established an effective nomogram for predicting the disease-free survival and overall survival of these patients based on these factors. This study offers new options in the predicting the treatment outcome of breast cancer patients.