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Related Experiment Video

Updated: Jul 1, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Multi-scale geometric network analysis identifies melanoma immunotherapy response gene modules.

Kevin A Murgas1, Rena Elkin2, Nadeem Riaz3

  • 1Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.

Scientific Reports
|March 14, 2024
PubMed
Summary

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This summary is machine-generated.

Researchers used network analysis to understand melanoma

Area of Science:

  • Oncology
  • Immunotherapy
  • Systems Biology
  • Bioinformatics

Background:

  • Melanoma immunotherapy response mechanisms are not fully understood.
  • Identifying molecular markers for treatment efficacy is crucial.

Purpose of the Study:

  • To investigate melanoma immunotherapy response at the molecular level.
  • To identify gene modules associated with treatment response using a network approach.

Main Methods:

  • Utilized gene expression data from melanoma patients before and after nivolumab treatment.
  • Modeled gene expression changes in a correlation network.
  • Applied dynamic Ollivier-Ricci curvature to identify critical gene modules.

Main Results:

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Last Updated: Jul 1, 2025

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  • Identified six distinct gene modules involved in immunotherapy response.
  • One module, linked to the nuclear factor kappa-B (NFkB) pathway, correlated with improved patient survival.
  • This module also predicted a positive clinical response to immunotherapy.

Conclusions:

  • Dynamic Ollivier-Ricci curvature is effective for identifying gene modules in cancer.
  • The NFkB pathway module shows potential as a biomarker for melanoma immunotherapy response and patient survival.