BackgroundBreast cancer is the most common type of cancer in females. Aberrant expression of microRNA-21 (miR-21) has previously been reported in breast cancer tissue. The aim of this study was to investigate expression levels of serum miR-21 in breast cancer patients and evaluate its prognostic value in Chinese females.MethodsReal-time quantitative (RQ)-PCR was used to analyze miR-21 expression in archived serum, tumor tissue, and adjacent normal tissue from 549 participants (326 with breast cancer, 223 without breast cancer). We also analyzed associations between serum miR-21 expression and breast cancer subtypes and patient prognosis. Recurrence and survival were analyzed by using the multivariate Cox proportional hazards model.ResultsExpression of miR-21 was significantly higher in breast cancer tissues compared with normal adjacent breast tissues (P<0.001). The 2-ΔΔCt values for serum miR-21 in breast cancer patients versus healthy controls were 9.12±3.43 and 2.96±0.73, respectively. Multivariate Cox proportional hazards model suggested that serum miR-21 expression was an independent poor prognostic factor for both recurrence (hazard ratio [HR]= 2.942; 95% confidence interval [CI]=1.420-8.325; P=0.008) and disease-free survival (HR=2.732; 95% CI=1.038-7.273, P=0.003) in breast cancer.ConclusionsIncreased serum miR-21 expression level was correlated with poor prognosis of breast cancer patients, indicating that serum miR-21 may be a novel prognostic marker for recurrence and survival of breast cancer patients before resection.

Since satellite network traffic is self-similar and long-range-dependent (LRD), after analyzing current network traffic forecasting models, a satellite network traffic combined forecasting model that is based on the decomposition fruit fly optimization algorithm -extreme learning machine (FOA-ELM) is proposed. This forecasting model decomposes LRD network traffic into multiple short-range dependent (SRD) components via empirical mode decomposition (EMD), applies the FOA-ELM forecasting model to the decomposed high-frequency components, and applies the ELM forecasting model to lowfrequency components. The simulation results show that the forecasting model can improve the forecasting accuracy and forecasting speed, reduce the complexity, and achieve effective and efficient forecasting of satellite network traffic. INDEX TERMS Self-similarity, forecasting model, empirical mode decomposition, fruit fly optimization, extreme learning machine.