Construction of a breast cancer predictive nomogram based on diverse cell death methods and reveal tumor microenvironment characterization

Abstract

AbstractObjectiveTo develop a robust predictive model and nomogram for breast cancer (BC) linked to genes associated with diverse cell death methods.MethodsThe prognostic model was constructed using the LASSO Cox method. Model performance was assessed using K-M analysis, ROC curves, and independent prognostic analysis. Subsequently, we constructed a nomogram and analyzed differences in tumor microenvironment and drug sensitivity between different subgroups. The enrichment of differentially expressed genes between different subgroups was assessed. Additionally, we overexpressed CD24 in BC cell lines to assess its impact on cellular proliferation using CCK8 assays, migration through scratch and transwell assays, and apoptosis via flow cytometry.ResultsA prognostic model comprising twelve genes (CREB3L1, SFRP1, SHARPIN, AIFM1, IL-18, CD24, EDA2R, CRIP1, XBP1, BCL2A1, NKX3-1, and NME5) was constructed. BC patients were categorized into different subgroups, with the low-risk subgroup demonstrating superior survival. Additionally, we constructed a nomogram. The nomogram was validated as a reliable independent predictor of outcome. The enrichment analyses imply a connection between patient risk and immune response. The low-risk subgroup had a higher TME score. Patients in the high-risk group had improved responses to lapatinib, BI-2536, OSI-027, and SB505124, while those in the low-risk subgroup showed improved sensitivity to axitinib, epirubicin, fulvestrant, and olaparib. Furthermore, CD24 overexpression was found to promote proliferation and migration, while inhibiting apoptosis.ConclusionThese findings contribute to the individualization of treatment and aid in uncovering the tumor microenvironment characterization for BC patients.