Multi-modal information plays a pivotal role in the segmentation of brain tumors. However, previous studies have largely overlooked the distinctive characteristics of individual modalities, which are correlated with the target tumor region due to distinct imaging principles. In this paper, we harness the distinctive traits of individual modalities and introduce a brain tumor segmentation model called specific modality guided brain tumor segmentation model (SMG-BTS). Our SMG-BTS adopts a three-branch encoder-decoder architecture. The main branch utilizes full modalities fused at input-level, while the two affiliated branches operate in parallel to provide guidance to the main branch in acquiring a robust representation. We propose a specific modality spatial guidance (SMSG) module to guide the process of feature extraction. Spatial information is obtained from selected modalities and utilized to enhance features extracted from the main branch. A shared-specific feature enhancement(SSFE) module is proposed to enhance the shared features across modalities and utilizes modality-specific features to further supplement specific information of modalities. Experimental results on the BraTS2021 benchmark dataset demonstrate the effectiveness of our proposed SMG-BTS over state-of-the-art brain tumor segmentation methods.