Construction of a Breast Cancer Predictive Nomogram Based on Diverse Cell Death Methods and Reveal Tumor...

Area of Science:

• Oncology
• Genetics
• Molecular Biology

Background:

• Breast cancer (BC) prognosis and treatment can be improved by understanding genetic factors influencing cell death.
• Predictive models integrating diverse gene functions are crucial for personalized oncology.

Purpose of the Study:

• To develop a robust predictive model and nomogram for breast cancer prognosis using genes associated with various cell death mechanisms.
• To validate the model's prognostic value and explore its correlation with the tumor microenvironment (TME) and treatment sensitivity.

Main Methods:

• Utilized the LASSO Cox method to construct a prognostic model based on twelve selected genes.
• Classified breast cancer patients into high-risk and low-risk subgroups.
• Developed and validated a nomogram for outcome prediction.
• Performed enrichment analyses and assessed TME scores.
• Evaluated drug sensitivity in relation to risk subgroups.

Main Results:

• A twelve-gene prognostic model and nomogram were successfully developed and validated, distinguishing high-risk from low-risk BC patients with significant survival differences.
• The low-risk subgroup demonstrated superior survival and a higher tumor microenvironment (TME) score.
• Distinct drug sensitivities were observed: high-risk patients responded better to lapatinib, BI‑2536, OSI‑027, and SB505124, while low-risk patients showed better sensitivity to axitinib, epirubicin, fulvestrant, and olaparib.
• CD24 overexpression was found to promote BC cell proliferation and migration while inhibiting apoptosis.

Conclusions:

• The developed gene-based predictive model and nomogram serve as reliable tools for breast cancer prognosis and personalized treatment selection.
• Risk stratification based on cell death-associated genes provides insights into TME characteristics and differential drug responses.
• Findings contribute to advancing personalized medicine strategies in breast cancer management.