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Signalling pathway impact analysis based on the strength of interaction between genes
Zhenshen Bao1, Xianbin Li1, Xiangzhen Zan2
1Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou, Zhejiang, People's Republic of China.
IET Systems Biology
|July 23, 2016
Summary
New methods like mutual information (MSPIA) improve cancer pathway identification by measuring gene interaction strength, outperforming traditional signalling pathway impact analysis (SPIA) in cancer datasets.
Area of Science:
- Bioinformatics
- Computational Biology
- Cancer Genomics
Background:
- Signalling pathway analysis identifies cancer-related pathways using differentially expressed genes (DEGs).
- Traditional methods simplify gene interactions to activation (+1) or suppression (-1), neglecting interaction strength.
- This simplification limits the accuracy of pathway analysis in complex biological systems.
Purpose of the Study:
- To introduce and evaluate novel methods for quantifying gene interaction strength in signalling pathways.
- To compare the performance of new methods against existing signalling pathway impact analysis (SPIA).
- To identify more robust cancer-related pathways using enhanced interaction metrics.
Main Methods:
- Developed and applied SPIA based on Pearson correlation coefficient (PSPIA) to measure gene pair interaction strength.
- Developed and applied SPIA based on mutual information (MSPIA) for a more nuanced assessment of gene interactions.
- Analyzed colorectal, lung, and pancreatic cancer datasets using PSPIA and MSPIA.
Main Results:
- Both PSPIA and MSPIA identified a greater number of candidate cancer-related pathways compared to standard SPIA.
- MSPIA demonstrated superior performance over PSPIA in identifying significant cancer pathways.
- The enhanced methods provide a more comprehensive analysis of signalling pathways in cancer.
Conclusions:
- Novel methods incorporating gene interaction strength, particularly MSPIA, offer improved sensitivity for detecting cancer-related pathways.
- These advanced approaches overcome limitations of traditional binary gene interaction models.
- The findings suggest a more accurate and detailed understanding of cancer signalling networks is achievable.
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