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Updated: Feb 27, 2026
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and
Sungjin Kwon1, Hyosil Kim2, Hyun Seok Kim1,2
1Graduate Programs for Nanomedical Science, Yonsei University, Seoul, Republic of Korea.
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.
Insights
A new R package, MCODER, enables simultaneous analysis of large multiomics datasets for network biology. It identified elevated collagen-integrin complexes in ovarian and colorectal tumors, suggesting potential therapeutic targets.
Area of Science:
- Computational biology
- Network analysis
- Cancer research
Background:
- Multiomics assays identify biological network entities.
- Molecular Complex Detection (MCODE) is a key algorithm for network subclusters.
- Existing MCODE implementation on Cytoscape has limitations for large-scale, simultaneous analyses.
Purpose of the Study:
- To implement the MCODE algorithm in R, creating a package named MCODER.
- To enable simultaneous analysis of multiple, large-scale omics datasets within the R environment.
- To apply MCODER for identifying therapeutically relevant protein-protein interactions in cancer subtypes.
Main Methods:
- Developed the MCODER package by implementing the MCODE algorithm in R.
- Utilized MCODER for analyzing multiple omics data sets concurrently.
- Applied MCODER to detect protein-protein interactions in molecular subtypes of ovarian and colorectal tumors.
Main Results:
- MCODER facilitates efficient analysis of multiple omics datasets.
- Identified elevated collagen-integrin protein complex production in an epithelial-mesenchymal transition subtype common to both ovarian and colorectal cancers.
- This finding highlights a potential therapeutic vulnerability in this specific cancer subtype.
Conclusions:
- MCODER provides a flexible and efficient R-based solution for network biology analyses.
- The collagen-integrin complex is a promising target for pharmacological intervention in specific molecular subtypes of ovarian and colorectal tumors.
- This study demonstrates the utility of MCODER in uncovering biologically significant molecular interactions in cancer.
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