The prediction models for postoperative overall survival and disease‐free survival in patients with breast cancer

Abstract

The goal of this study is to establish a method for predicting overall survival (OS) and disease‐free survival (DFS) in breast cancer patients after surgical operation. The gene expression profiles of cancer tissues from the patients, who underwent complete surgical resection of breast cancer and were subsequently monitored for postoperative survival, were analyzed using cDNA microarrays. We detected seven and three probes/genes associated with the postoperative OS and DFS, respectively, from our discovery cohort data. By incorporating these genes associated with the postoperative survival into MammaPrint genes, often used to predict prognosis of patients with early‐stage breast cancer, we constructed postoperative OS and DFS prediction models from the discovery cohort data using a Cox proportional hazard model. The predictive ability of the models was evaluated in another independent cohort using Kaplan–Meier (KM) curves and the area under the receiver operating characteristic curve (AUC). The KM curves showed a statistically significant difference between the predicted high‐ and low‐risk groups in both OS (log‐rank trend test P = 0.0033) and DFS (log‐rank trend test P = 0.00030). The models also achieved high AUC scores of 0.71 in OS and of 0.60 in DFS. Furthermore, our models had improved KM curves when compared to the models using MammaPrint genes (OS: P = 0.0058, DFS: P = 0.00054). Similar results were observed when our model was tested in publicly available datasets. These observations indicate that there is still room for improvement in the current methods of predicting postoperative OS and DFS in breast cancer.

Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data

“…The adjusted models were finally evaluated on an independent validation cohort using AUC as the discriminative accuracy of these risk prediction models. In general, these risk prediction models on cross-validation of the discovery cohort achieved higher AUCs than the adjust models on the validation cohort 18,40 . The difference of the AUCs is due to overfitting of the model construction criterion.…”

MiR-16-5p suppresses breast cancer proliferation by targeting ANLN

“…Breast cancer (BC), one of the most common malignancies in women worldwide, had approximately 268,600 new cases and 41,760 deaths in 2019 due to continuously increasing incidence in recent years, accounting for approximately 30% of new tumorigenesis cases and 15% of tumor-related deaths [ 1 , 2 ]. Although the diagnosis and therapy of BC have made great progress, the 5-year survival rate of BC patients remains still low [ 3 ]. At present, the molecular mechanism of BC is still unclear, so it is crucial to identify novel molecular biomarkers that are relevant to the development and prognosis of BC and interpret the underlying molecular mechanisms.…”

Bivariate lifetime models in presence of cure fraction: a comparative study with many different copula functions

“…In this subsection it is assumed the use of the proposed models in the data analysis of a data set related to a cohort, where 97 patients underwent surgical treatment for breast cancer followed up for a period ranging from the year 2000 to 2011. More details of this study can be found in Shigemizu et al [69]. For this bivariate lifetime application it was considered the disease-free survival time (DFS) and the overall survival time (OS), denoted respectively by 1 and 2 .…”