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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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Multiscale protein networks systematically identify aberrant protein interactions and oncogenic regulators in seven

Won-Min Song1,2,3, Abdulkadir Elmas1,2,3, Richard Farias1,4

  • 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1425 Madison Avenue, New York, NY, 10029, USA.

Journal of Hematology & Oncology
|December 16, 2023
PubMed

Insights

This study uses proteomic network analysis across seven cancers to identify key cancer pathways and regulators. It reveals conserved protein modules and validates new therapeutic targets like RSL1D1, DDX21, and SMC2.

Area of Science:

  • Proteomics
  • Systems Biology
  • Cancer Research

Background:

  • Global proteomic data from mass spectrometry (MS) can elucidate cancer functional models.
  • Integrative proteomic network analyses were performed on 687 cases across 7 cancer types.

Discussion:

  • Protein co-expression network analysis identified disease-associated pathways.
  • Comparison with transcriptome networks revealed proteome-specific cancer subnetworks.
  • Cross-cancer analysis identified conserved protein modules and central functional axes: ERAD and N-acetyltransferase activity.

Key Insights:

  • Developed interpretable network models of cancer proteomes.
  • Identified novel pan-cancer protein regulators (RSL1D1, DDX21, SMC2) for disease-associated pathways.
  • Experimental validation confirmed the role of predicted regulators in various cancer and fetal cells.

Outlook:

  • Proteomic network models offer potential for discovering oncogenic regulators.
  • This approach can elucidate cancer mechanisms and identify new therapeutic targets.
  • Further research can leverage these models for precision oncology.

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