Gliomas are highly heterogeneous tumors with generally poor prognoses. Leveraging multi-omics data and network analysis holds great promise in uncovering crucial signatures and molecular relationships that elucidate glioma heterogeneity. However, the complexity of the problem and the high dimensionality of the data increase the challenges of integrating information across various biological levels. This study develops a comprehensive framework aimed at identifying potential glioma-type-specific biomarkers through innovative variable selection and integrated network visualization. We designed a two-step framework for variable selection using sparse network estimation across various omics datasets. This framework incorporates MINGLE (Multi-omics Integrated Network for GraphicaL Exploration), a novel methodology designed to merge distinct multi-omics information into a single network, enabling the identification of underlying relations through an innovative integrated visualization. The analysis was conducted using glioma omics datasets, with patients grouped based on the latest glioma classification guidelines. Our investigation of the glioma data led to the identification of variables potentially serving as glioma-type-specific biomarkers. The integration of multi-omics data into a single network through MINGLE facilitated the discovery of molecular relationships that reflect glioma heterogeneity, supporting the biological interpretation. Scripts and files for reproducing the analysis or adapting it to other applications, are available in R software.