Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER

Abstract

Current multiomics assay platforms facilitate systematic identification of functional entities that are mappable in a biological network, and computational methods that are better able to detect densely connected clusters of signals within a biological network are considered increasingly important. One of the most famous algorithms for detecting network subclusters is Molecular Complex Detection (MCODE). MCODE, however, is limited in simultaneous analyses of multiple, large-scale data sets, since it runs on the Cytoscape platform, which requires extensive computational resources and has limited coding flexibility. In the present study, we implemented the MCODE algorithm in R programming language and developed a related package, which we called MCODER. We found the MCODER package to be particularly useful in analyzing multiple omics data sets simultaneously within the R framework. Thus, we applied MCODER to detect pharmacologically tractable protein-protein interactions selectively elevated in molecular subtypes of ovarian and colorectal tumors. In doing so, we found that a single molecular subtype representing epithelial-mesenchymal transition in both cancer types exhibited enhanced production of the collagen-integrin protein complex. These results suggest that tumors of this molecular subtype could be susceptible to pharmacological inhibition of integrin signaling.

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

“…Cytoscape version 3.4.0 software (12) was used to visualize the molecular interaction networks of the DEGs. The APP plug-in, Molecular Complex Detection (MCODE) (13), was used to arrange the network topology to cluster densely connected genes. After the PPI networks was constructed, its key modules were searched by using the MCODE application.…”

Identification of molecular subtypes in lung adenocarcinoma based on DNA methylation and gene expression profiling—a bioinformatic analysis

“…To obtain the best results, the minimum required interaction score was set to 0.4. The PPI network was visualized with Cytoscape 3.9.0 (18) and the MCODE plug-in was used to build subnetworks using the following parameters: a MCODE score >5, degree cutoff =2, node score cutoff =0.2, node density cutoff =0.1, k-score =2, and maximum depth =100 (19).…”