Thanks to deep convolutional neural networks (CNNs), Brain Tumor Segmentation (BTS) has made great progresses, while most existing methods are parsed into several stages and cannot be trained end-to-end. In this paper, we propose a novel and light noise suppression network (ENS-Unet) to achieve end-to-end learning without elaborate pre-processing and post-processing. Specifically, Slice-based Normalization is first proposed to enhance the model adaptability, where the impacts of different data distributions between training and test samples are restrained. Additionally, aiming at suppressing noises, Noise Suppression U-net (NS-Unet) is designed to obtain better image representation. Finally, to cope with the unbalanced problem of training data, Batch-based Loss, deriving from the statistical distribution within mini-batch, is adopted to adjust the weights of classes. Extensive experiments on BraTS 2013 and 2015 datasets demonstrate that the proposed method achieves very competitive results with the state-of-the-art approaches.