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Related Experiment Video

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Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
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Molecular subtype identification and prognosis stratification based on lysosome-related genes in breast cancer.

Xiaozhen Liu1, Kewang Sun1, Hongjian Yang2

  • 1General Surgery, Cancer Center, Department of Breast Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.

Heliyon
|February 29, 2024
PubMed
Summary
This summary is machine-generated.

This study identifies key lysosome-related genes (LRGs) to predict breast cancer prognosis and treatment response. A novel LRG-based risk model aids in personalized treatment strategies for better patient outcomes.

Keywords:
Breast cancerConsensus clusteringLysosomesNomogramPrognosis

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Area of Science:

  • Oncology
  • Genetics
  • Molecular Biology

Background:

  • Lysosomes play a role in breast cancer development and recurrence.
  • The specific association between lysosome-related genes (LRGs) and breast cancer is not fully understood.
  • Investigating LRGs can reveal insights into breast cancer prognosis and treatment response.

Purpose of the Study:

  • To explore the prognostic and predictive value of LRGs in breast cancer.
  • To identify novel LRG-based biomarkers for breast cancer.
  • To develop a risk model for predicting patient outcomes and treatment response.

Main Methods:

  • Utilized TCGA and GEO databases for breast cancer gene expression and clinical data.
  • Performed consensus clustering and Lasso Cox regression to identify prognosis-related LRGs.
  • Constructed and validated a prognostic risk model and a nomogram incorporating LRGs and clinical features.

Main Results:

  • Identified 176 differentially expressed LRGs associated with breast cancer prognosis.
  • Developed a 7-LRG risk model that stratifies patients into high- and low-risk groups with distinct prognoses.
  • The low-risk group showed better survival, enhanced immunotherapy response, and lower chemotherapy sensitivity.
  • The risk score correlated with immune cell infiltration and served as an independent prognostic factor.
  • A nomogram combining risk score and clinical factors demonstrated excellent overall survival prediction.

Conclusions:

  • A novel LRG-derived risk feature was successfully constructed for breast cancer.
  • This LRG-based risk model shows strong performance in predicting prognosis when integrated with clinical pathological features.
  • The findings support the use of LRGs in personalized breast cancer management and treatment selection.