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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
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Proteogenomic convergence for understanding cancer pathways and networks
Emily S Boja1, Henry Rodriguez1
1Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, 31 Center Drive, MSC 2580, 20892 Bethesda, MD, USA.
Clinical Proteomics
|July 5, 2014
Summary
Cancer research is moving beyond single pathways to understand complex molecular signaling. New proteomic and genomic tools enable a systems-level view of cancer biology, linking genotype to phenotype.
Area of Science:
- Molecular biology
- Cancer research
- Proteomics
- Genomics
Background:
- Traditional cancer research focused on individual signaling pathways, but these models are insufficient for complex biological systems.
- Cancer involves intricate crosstalk between signaling pathways, necessitating a more holistic approach.
- Recent advances in high-throughput sequencing and proteomic technologies offer new avenues for cancer research.
Purpose of the Study:
- To highlight the shift in cancer research from linear pathway analysis to a systems-level understanding.
- To emphasize the importance of integrating multi-dimensional omics data (genomics, transcriptomics, proteomics) for a comprehensive view of cancer.
- To underscore the move towards protein target verification and network analysis in cancer biology.
Main Methods:
- High-throughput deep sequencing of human genomes.
- Proteomic technologies for comprehensive human proteome characterization.
- Multiplexed quantitative proteomic assays for protein and post-translational modification (PTM) analysis.
- Integration of genomics, transcriptomics, and proteomics data.
Main Results:
- Proteomic profiling provides an initial landscape of differentially expressed proteins.
- Multiplexed assays enable verification of protein targets and analysis of interactions, isoforms, and PTMs.
- Multi-dimensional omics data integration allows assessment of genomic/transcriptomic aberrations' effects on signaling pathways.
- Systems-level analysis maps protein fluctuations in response to stimuli for better cancer biology understanding.
Conclusions:
- Cancer research is transitioning to a systems biology approach, integrating multi-omics data.
- Understanding protein networks and signaling crosstalk is crucial for deciphering complex cancer mechanisms.
- The integration of genotype, proteotype, and phenotype data offers a more complete picture of cancer.
- Advanced proteomic techniques are vital for target verification and understanding dynamic cellular processes in cancer.
Keywords:
Mathematical and computational modelingOmics integrationPost-translational modificationProtein identification and quantitationProtein networksProteogenomicsSignaling pathwaysSystems biologyTargeted proteomicsMore Related Videos
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