No abstract

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Network theory provides useful tools to study the underlying laws governing complex diseases. Within this framework, comparisons of network topologies, including the node, edge, and community, between different disease states can highlight key factors within these dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the “core network module” that quantifies the significant phenotypic variation. Then, based on this core network module, key factors including functional protein-protein interactions, pathways, and drive mutations are predicted by the topological-functional connection score and structural modeling. We applied the approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.