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Biased random walk model for the prioritization of drug resistance associated proteins

Hao Guo1,2, Jiaqiang Dong1, Sijun Hu1

  • 1State Key Laboratory of Cancer Biology and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, P. R. China.

Scientific Reports
|June 4, 2015
PubMed

Insights

Identifying key proteins linked to cancer drug resistance is crucial. ProteinRank effectively prioritizes these molecules from multi-omics data, aiding in overcoming treatment failure in gastric cancer.

Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Multi-drug resistance significantly contributes to cancer treatment failure.
  • Identifying molecular drivers of drug resistance from complex multi-omics data presents a major challenge.

Purpose of the Study:

  • To introduce and validate ProteinRank, a novel strategy for prioritizing drug-resistance associated proteins in cancer.
  • To identify key proteins involved in Adriamycin and Vincristine resistance in gastric cancer.

Main Methods:

  • Differential protein expression analysis using iTRAQ and LC-MS/MS in resistant vs. parental gastric cancer cells.
  • Network analysis using ProteinRank to rank proteins based on their association with known drug resistance proteins.
  • Cross-validation and functional studies to confirm the role of top-ranked proteins.

Main Results:

  • ProteinRank successfully prioritized drug-resistance associated proteins, outperforming methods that did not utilize mass spectrometry data.
  • Validation confirmed altered expression/activity of several top-ranked proteins, including PIM3 and CAV1.
  • Silencing PIM3 or CAV1 expression reversed drug resistance in gastric cancer cells.

Conclusions:

  • ProteinRank is an effective tool for identifying critical proteins related to drug resistance in gastric cancer.
  • The findings provide valuable insights and potential therapeutic targets for overcoming cancer drug resistance.

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