Discovering key regulatory mechanisms from single-factor and multi-factor regulations in glioblastoma utilizing multi-dimensional data

Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data

In human health, a fundamental challenge is the identification of disease-related genes. Bladder cancer (BC) is a worldwide malignant tumor, which has resulted in 170,000 deaths in 2010 up from 114,000 in 1990. Moreover, with the emergence of multi-omics data, more comprehensive analysis of human diseases become possible. In this study, we propose a multi-step approach for the identification of BC-related genes by using integrative Heterogeneous Network Modeling of Multi-Omics data (iHNMMO). The heterogeneous network model properly and comprehensively reflects the multiple kinds of relationships between genes in the multi-omics data of BC, including general relationships, unique relationships under BC condition, correlational relationships within each omics and regulatory relationships between different omics. Besides, a network-based propagation algorithm with resistance is utilized to quantize the relationships between genes and BC precisely. The results of comprehensive performance evaluation suggest that iHNMMO significantly outperforms other approaches. Moreover, further analysis suggests that the top ranked genes may be functionally implicated in BC, which also confirms the superiority of iHNMMO. In summary, this study shows that disease-related genes can be better identified through reasonable integration of multi-omics data.