Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for...

Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biomarkers and prognostic models. Programmed cell death (PCD) pathways serve as a critical factor in tumor development and treatment response. However, the relationship between the diverse patterns of PCD and NAC in BRCA remains unclear. We integrated machine learning and multiple bioinformatics tools to explore the association between 19 PCD patterns and the prognosis of NAC within a cohort of 921 BRCA patients treated with NAC from seven multicenter cohorts. A prognostic risk model based on PCD-related genes (PRGs) was constructed and evaluated using a combination of 117 machine learning algorithms. Immune infiltration analysis, mutation analysis, pharmacological analysis, and single-cell RNA sequencing (scRNA-seq) were conducted to explore the genomic profile and clinical significance of these model genes in BRCA. Immunohistochemistry (IHC) was employed to validate the expression of select model genes (UGCG, BTG22, TNFRSF21, and MYB) in BRCA tissues. We constructed a PRGs prognostic risk model by using a signature comprising 20 PCD-related DEGs to forecast the clinical outcomes of NAC in BRCA patients. The prognostic model demonstrated excellent predictive accuracy, with a high concordance index (C-index) of 0.772, and was validated across multiple independent datasets. Our results demonstrated a strong association between the developed model and the survival prognosis, clinical pathological features, immune infiltration, tumor microenvironment (TME), gene mutations, and drug sensitivity of NAC for BRCA patients. Moreover, IHC studies further demonstrated that the expression of certain model genes in BRCA tissues was significantly associated with the efficacy of NAC and emerged as an autonomous predictor of outcomes influencing the outcome of patients. We are the first to integrate machine learning and bulk and scRNA-seq to decode various cell death mechanisms for the prognosis of NAC in BRCA. The developed unique prognostic model, based on PRGs, provides a novel and comprehensive strategy for predicting the NAC outcomes of BRCA patients. This model not only aids in understanding the mechanisms underlying NAC efficacy but also offers insights into personalized treatment strategies, potentially improving patient outcomes.