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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
Published on: August 19, 2025
The network organization of cancer-associated protein complexes in human tissues
Jing Zhao1, Sang Hoon Lee, Mikael Huss
1Department of Mathematics, Logistical Engineering University, Chongqing, China. zhaojanne@gmail.com
Abstract:
Differential gene expression profiles for detecting disease genes have been studied intensively in systems biology. However, it is known that various biological functions achieved by proteins follow from the ability of the protein to form complexes by physically binding to each other. In other words, the functional units are often protein complexes rather than individual proteins. Thus, we seek to replace the perspective of disease-related genes by disease-related complexes, exemplifying with data on 39 human solid tissue cancers and their original normal tissues. To obtain the differential abundance levels of protein complexes, we apply an optimization algorithm to genome-wide differential expression data. From the differential abundance of complexes, we extract tissue- and cancer-selective complexes, and investigate their relevance to cancer. The method is supported by a clustering tendency of bipartite cancer-complex relationships, as well as a more concrete and realistic approach to disease-related proteomics.
Insights
This study shifts focus from disease genes to disease-related protein complexes. Analyzing cancer data, it identifies tissue-specific complexes, offering a new proteomics approach for disease understanding.
Area of Science:
- Systems biology
- Proteomics
- Cancer research
Background:
- Traditional disease gene studies overlook protein complex functions.
- Biological functions are often executed by interacting protein complexes, not individual proteins.
- Understanding disease requires a shift from gene-centric to complex-centric perspectives.
Purpose of the Study:
- To propose and validate a novel method for identifying disease-related protein complexes.
- To analyze differential abundance of protein complexes in human solid tissue cancers.
- To investigate the relevance of tissue- and cancer-selective complexes in oncogenesis.
Main Methods:
- Applied an optimization algorithm to genome-wide differential expression data.
- Calculated differential abundance levels for protein complexes.
- Extracted tissue- and cancer-selective complexes based on abundance data.
Main Results:
- Identified specific protein complexes associated with different human solid tissue cancers.
- Demonstrated a clustering tendency in cancer-complex relationships.
- Provided a more realistic proteomics approach for disease-related studies.
Conclusions:
- Protein complexes are crucial functional units in disease.
- The developed method effectively identifies disease-relevant complexes.
- This complex-centric approach enhances our understanding of cancer proteomics.
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