A Novel Tool for Multi-Omics Network Integration and Visualization: A Study of Glioma Heterogeneity

Abstract

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. In this study, we developed a framework comprising two steps for variable selection based on sparse network estimation from various omics. Subsequently, we introduced 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. Applying this method to glioma data, with patients grouped according to the newest glioma classification guidelines, led to the selection of variables as potential candidates for novel glioma-type-specific biomarkers.