Gliomas, especially high-grade gliomas, have a high mortality rate. O6-Methylguanine-DNA Methyltransferase (MGMT) status is crucial for gliomas treatment and prognosis. Traditional diagnosis relies on invasive tissue analysis, which is often infeasible for high-risk patients. While machine learning and deep learning methods using multi-sequence Magnetic Resonance Imaging (MRI) images and radiomics provides a non-invasive alternative, existing methods suffer from low accuracy and poor generalization due to challenges in extracting features from the integrated multi-sequence representation. To address this issue, we propose a Multi-modal feature extraction and Global-aware feature Graph-based deep learning network (MGG-Net), integrating convolutional neural network (CNN) and graph convolutional network (GCN) for multi-modal and multi-scale feature learning. Specifically, MGG-Net consists of multiple CNN-GCN stages, responsible for processing MRI image features and radiomic features at different scales. CNN blocks are used to extract fine-grained and sequence-specific local features from each MRI sequence. These features are then fed into a GCN, which models long-range dependencies and extracts high-level global representations. Finally, the fused multi-scale features extracted are used for classification. Experimental results demonstrate that MGG-Net outperforms previous approaches, effectively leveraging multi-scale and multi-modal information for improved MGMT status classification.