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CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research
Haizhou Liu1, Mengqin Yuan1, Ramkrishna Mitra2
1Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Avenue, Nanjing, 211106, Jiangsu Province, China.
Genome Medicine
|October 13, 2022
Summary
CTpathway enhances pathway enrichment analysis by integrating pathway crosstalk and molecular interactions. This novel method accurately identifies cancer risk pathways, outperforming existing approaches for both bulk and single-cell RNA-seq data.
Area of Science:
- Bioinformatics
- Computational Biology
- Cancer Genomics
Background:
- Pathway enrichment analysis (PEA) is crucial for understanding gene functions and identifying disease-risk pathways.
- Existing PEA methods often fail to integrate critical pathway features like crosstalk, molecular interactions, and network topology.
- This limitation leads to many potential cancer risk pathways remaining uninvestigated.
Purpose of the Study:
- To develop an advanced PEA method, CTpathway, that overcomes the limitations of existing approaches.
- To integrate pathway crosstalk, molecular interactions, and network topology into a comprehensive analysis framework.
- To accurately identify and investigate cancer risk pathways using a novel crosstalk-based approach.
Main Methods:
- Developed CTpathway, a novel crosstalk-based PEA method.
- Constructed a global pathway crosstalk map (GPCM) with over 440,000 edges by integrating multiple pathway resources, transcription factor-gene regulations, and protein-protein interactions.
- Assigned gene risk scores within the GPCM by integrating gene differential expression and crosstalk effects to identify enriched risk pathways.
Main Results:
- CTpathway demonstrated superior performance in identifying cancer risk pathways across >8300 expression profiles from ten cancer types and blood samples.
- The method achieved higher accuracy, reproducibility, and speed compared to state-of-the-art methods.
- CTpathway successfully identified known and novel critical pathways for various cancer types, including early-stage cancers, and is applicable to both bulk and single-cell RNA-seq data.
Conclusions:
- CTpathway is a fast, accurate, and stable PEA method specifically designed for cancer research.
- The method effectively identifies cancer risk pathways, offering significant advantages over existing tools.
- CTpathway provides an interactive web server and a stand-alone program for broader accessibility in the research community.
