Unveiling the role of PANoptosis-related genes in breast cancer: an integrated study by multi-omics analysis and machine learning algorithms.

BACKGROUND

The heterogeneity of breast cancer (BC) necessitates the identification of novel subtypes and prognostic models to enhance patient stratification and treatment strategies. This study aims to identify novel BC subtypes based on PANoptosis-related genes (PRGs) and construct a robust prognostic model to guide individualized treatment strategies.

METHODS

The transcriptome data along with clinical data of BC patients were sourced from the TCGA and GEO databases. Consensus clustering was performed on 12 PRGs to ascertain potential BC subtypes, and variances in survival, infiltration of immune cells, and functional pathways among them were examined. A prognostic model was generated through 101 combinations of machine learning algorithms and validated across multiple cohorts. The response of patients towards immunotherapy were analyzed using multiple frameworks.

RESULTS

Consensus clustering of 12 PRGs identified two distinct BC subtypes, with subtype B exhibiting significantly lower overall survival (OS) rates compared to subtype A. Immune cell infiltration analysis revealed higher immune activity in subtype A. Functional pathway analysis revealed that subtype A exhibited a significant enrichment in immune-related pathways, while subtype B was associated with cell cycle and metabolic processes. An integrated machine learning framework integrating CoxBoost and Random Survival Forest (RSF) algorithms was developed, demonstrating high predictive performance across multiple cohorts. A nomogram combining age and risk score was constructed, showing excellent predictive performance. Immune landscape analysis revealed that the high-risk group exhibited a suppressive tumor immune microenvironment (TIME). Immunotherapy response prediction suggested that low-risk patients were more likely to benefit from PD-1 and CTLA-4 inhibitors.

CONCLUSIONS

Our study provides a comprehensive framework for BC subtype classification and prognostic prediction, offering valuable insights for personalized treatment strategies.