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A Prognostic Risk Model for Breast Cancer Integrating Migrasome and Tumor Microenvironment Features to Predict
1Department of General Surgery, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Cancer Research and Treatment
|July 29, 2026
Summary
We developed a prognostic model for breast cancer (BRCA) using migrasome and tumor microenvironment genes. This model predicts patient risk, immune status, and treatment response, aiding in understanding BRCA heterogeneity.
Area of Science:
- Oncology
- Genomics
- Immunology
Background:
- Breast cancer (BRCA) is a complex disease with unclear prognostic factors.
- The role of migrasomes and tumor microenvironment-related genes (MTMERGs) in BRCA prognosis is not well understood.
Purpose of the Study:
- To develop and validate a prognostic risk model for breast cancer (BRCA) utilizing MTMERGs.
- To investigate the association between the prognostic model, tumor immune microenvironment, and therapeutic responses in BRCA.
Main Methods:
- A six-gene prognostic signature was built using differential expression and Cox regression analysis.
- Model validation involved Kaplan-Meier, time-dependent ROC curves, and Decision Curve Analysis (DCA).
- Immune infiltration, function, immunotherapy sensitivity, TMB, and drug sensitivity were assessed using various computational tools.
Main Results:
- The six-MTMERG model stratified BRCA patients into high- and low-risk groups with distinct survival outcomes.
- High-risk patients showed an immunosuppressive microenvironment, higher tumor mutation burden (TMB), and poorer prognosis.
- Low-risk patients had an active immune profile and predicted better response to immune checkpoint inhibitors.
Conclusions:
- A validated prognostic model based on MTMERGs was developed for breast cancer (BRCA).
- The model effectively stratifies patients by clinical outcomes, immune profiles, and predicted therapeutic responses.
- This prognostic tool offers insights into BRCA heterogeneity, warranting further clinical validation.
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