ArticleBiomedicines2021
Pathway-Based Personalized Analysis of Pan-Cancer Transcriptomic Data.
Article in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Delineation of molecular subtypes based on pathway activation signatures reveals intrinsic tumor heterogeneity and pan-cancer shared features.Frontiers in oncology · 2026Article
- Construct prognostic models of multiple myeloma with pathway information incorporated.PLoS computational biology · 2024Article
- ssMutPA: single-sample mutation-based pathway analysis approach for cancer precision medicine.GigaScience · 2024Article
- PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis.Frontiers in bioinformatics · 2024Article
- DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features.Bioinformatics (Oxford, England) · 2022Article
- PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The occurrence of cancer is closely related to the deregulation of certain pathways. Based on pathway deregulation scores (PDS) inferred by the Pathifier algorithm, we analyzed transcriptomic data of 13 different cancer types in The Cancer Genome Atlas database to identify cancer-specific deregulated pathways and prognostic pathways. The results showed that the individual-specific pathway deregulation scores can clearly distinguish different cancer types and their tumor-adjacent tissues. In addition, the cancer-specific deregulated pathways and prognostic pathways of different cancer types had high heterogeneity, and the identified cancer prognostic pathways have been reported to be closely related to the corresponding cancers. Furthermore, we also found that cancers with more deregulation pathways tend to be malignant and have worse prognoses. Finally, a Cox proportional Hazards model was constructed based on the prognostic pathways; this model successfully predicted survival and prognosis based on data from cancer samples. In addition, the performance of the breast cancer prognostic model was validated with an independent data set in the METABRIC database. Therefore, the prognostic pathways we identified have the potential to become targets for the treatment of cancer.
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Registered trials
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