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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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SRPS: Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery.

Linhai Xie1,2, Pei Jiang1,2, Cheng Chang1

  • 1State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.

Genomics, Proteomics & Bioinformatics
|June 10, 2025
PubMed
Summary

A new algorithm, Survival Reinforced Patient Stratification (SRPS), improves molecular subtyping for precision medicine. It identifies key proteins like PPIC, aiding in discovering new cancer drivers for better patient stratification.

Keywords:
Biomarker discoveryPeptidyl-prolyl cis-trans isomerase CProteomicsSubtypingTransfer learning

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Precision Medicine

Background:

  • Omics-based molecular subtyping is crucial for proteomics-driven precision medicine (PDPM).
  • Maintaining subtype consistency across diverse cohorts is challenging due to biological heterogeneity and technical variability.
  • Robust patient stratification requires algorithms that preserve molecular features and prognostic associations.

Purpose of the Study:

  • To develop a novel subtyping algorithm, Survival Reinforced Patient Stratification (SRPS), for adapting molecular subtypes across cohorts.
  • To ensure preserved prognostic significance and distinct molecular characteristics of subtypes.
  • To demonstrate interpretable machine learning for biological discovery in PDPM.

Main Methods:

  • Proposed Survival Reinforced Patient Stratification (SRPS) algorithm for cross-cohort subtyping.
  • Benchmarking SRPS on simulated and real-world datasets.
  • Utilized subtype significance scores to identify key proteins for stratification.

Main Results:

  • SRPS demonstrated a 12% increase in classification accuracy compared to existing methods.
  • The algorithm achieved superior prognostic discrimination across datasets.
  • Identified Peptidylprolyl Isomerase C (PPIC) as a top-ranked protein for stratifying hepatocellular carcinoma (HCC) patients with poor prognosis.
  • Experimentally validated PPIC as a pro-cancer protein in HCC.

Conclusions:

  • SRPS effectively adapts molecular subtypes across different cohorts while maintaining prognostic and molecular integrity.
  • The algorithm facilitates interpretable machine learning-guided biological discovery.
  • PPIC is a potential therapeutic target and biomarker for HCC, underscoring the utility of SRPS in PDPM research.