Abstract Triple Negative Breast Cancer is a clinically aggressive and molecularly heterogeneous subtype of breast cancer that currently lacks effective targeted therapies. Recognising biologically distinct subtypes within this disease is crucial for enhancing diagnosis, prognosis, and therapeutic approaches. This study introduces a comprehensive analytical framework that integrates unsupervised clustering, differential gene expression analysis, pathway enrichment, and explainable machine learning to delineate robust molecular subtypes of Triple Negative Breast Cancer and their corresponding biological mechanisms. Consensus clustering is used to divide patients into different subgroups by analyzing publicly available gene expression datasets. Pathway enrichment analysis is used to find subtype-specific gene signatures and figure out what they do. To improve interpretability and translational relevance, a model-agnostic explainable artificial intelligence approach is used to measure how much key genes and pathways help with subtype classification. The prognostic significance of the genes is further studied to demonstrate the clinical applicability of the identified biomarkers. The suggested framework works well with many different machine learning models and makes it possible to find biologically meaningful biomarkers linked to therapeutic resistance and the ability to spread cancer. These findings elucidate the molecular heterogeneity of Triple Negative Breast Cancer and endorse the advancement of more accurate and interpretable biomarker-driven clinical strategies.
Mohammad et al. (Mon,) studied this question.