Accurate segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance images (DCE-MRI) is critical for early diagnosis of breast cancer. However, this task remains challenging due to the wide range of tumor sizes, shapes, and appearances. Additionally, the complexity is further compounded by the high dimensionality and ill-posed artifacts present in DCE-MRI data. Furthermore, accurately modeling features in DCE-MRI sequences presents a challenge that hinders the effective representation of essential tumor characteristics. Therefore, this paper introduces a novel Temporal-Spatial Enhanced Network (TSESNet) for breast tumor segmentation in DCE-MRI. TSESNet leverages the spatial and temporal dependencies of DCE-MRI to provide a comprehensive representation of tumor features. To address sequence modeling challenges, we propose a Temporal-Spatial Contrastive Loss (TSCLoss) that maximizes the distance between different classes and minimizes the distance within the same class, thereby improving the separation between tumors and the background. Moreover, we design a novel Temporal Series Feature Fusion (TSFF) module that effectively integrates temporal MRI features from multiple time points, enhancing the model's ability to handle temporal sequences and improving overall performance. Finally, we introduce a simple and effective Tumor-Aware (TA) module that enriches feature representation to accommodate tumors of various sizes. We conducted comprehensive experiments to validate the proposed method and demonstrate its superior performance compared to recent state-of-the-art segmentation methods on two breast cancer DCE-MRI datasets.