ArticleGenome medicine2022
CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research.
Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 37 citations in OpenAlex.
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- Molecular principles underlying aggressive cancers.Signal transduction and targeted therapy · 2025Review
- Drug discovery for chemotherapeutic resistance based on pathway-responsive gene sets and its application in breast cancer.Frontiers in bioinformatics · 2025Article
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- Network-based identification of key proteins and repositioning of drugs for non-small cell lung cancer.Cancer reports (Hoboken, N.J.) · 2024Article
- Benchmarking enrichment analysis methods with the disease pathway network.Briefings in bioinformatics · 2024Article
- ssMutPA: single-sample mutation-based pathway analysis approach for cancer precision medicine.GigaScience · 2024Article
- CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation.Briefings in bioinformatics · 2023Article
- Unraveling the mechanism of ethyl acetate extract fromFrontiers in immunology · 2023Article
- CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research.Genome medicine · 2022Article
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundPathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated.
methodsTo overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes.
resultsAnalysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy.
conclusionsCollectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.