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Identification and multi-dimensional validation of mitochondrial permeability transition-driven necrosis-related model to assess the prognosis and immunotherapy value in breast cancer

  • 0Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
European Journal of Medical Research +

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February 19, 2025

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February 19, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies mitochondrial permeability transition (MPT)-driven necrosis (MPTdn) related genes in breast cancer. A novel risk model using BCL2A1, SCUBE2, NPY1R, and CLIC6 shows predictive efficacy for patient outcomes.

Area Of Science

  • Oncology
  • Cell Biology
  • Genomics

Background

  • Breast cancer is a widespread malignancy globally.
  • Mitochondrial permeability transition (MPT)-driven necrosis is a cell death pathway involving mitochondrial disruption.
  • The specific role of MPT-driven necrosis in breast cancer pathogenesis is not well understood.

Purpose Of The Study

  • To investigate the role of MPT-driven necrosis (MPTdn) in breast cancer.
  • To identify MPTdn-related genes and develop a predictive risk model.
  • To explore potential biomarkers and therapeutic targets for breast cancer.

Main Methods

  • Genome-wide analysis of MPTdn-related genes using TCGA and GEO datasets.
  • Consensus clustering to define MPTdn molecular subtypes.
  • Development and validation of a prognostic risk model based on differentially expressed genes.
  • Immune, clinical, and drug sensitivity correlation analyses.
  • Validation of candidate genes at protein and mRNA levels.

Main Results

  • Identified 39 MPTdn-related genes with significant alterations in breast cancer.
  • Developed a 4-gene risk model (BCL2A1, SCUBE2, NPY1R, CLIC6) with strong predictive power for overall survival.
  • Low-risk group associated with better survival and higher immune infiltration.
  • External datasets confirmed the model's stability and predictive efficacy.
  • BCL2A1, SCUBE2, NPY1R, and CLIC6 showed lower expression in tumor tissues and correlated with non-recurrence.

Conclusions

  • A novel MPTdn-based risk model demonstrates excellent predictive efficacy in breast cancer.
  • BCL2A1, SCUBE2, NPY1R, and CLIC6 are identified as potential biomarkers for breast cancer.
  • These findings provide a foundation for developing new therapeutic strategies targeting MPTdn in breast cancer.

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