Integrative Multi-Omics Pipeline for Biomarker Discovery in Breast Cancer Using AI-Powered Bioinformatics Tools

Abstract

As breast cancer is a heterogeneous disease with diverse molecular subtypes/disease behaviors and outcomes, it is a major challenge to identify early detection and personalized treatment. Based on the above, we describe in this study an integrative pipeline based on AI that can be used to identify the robust Clinically relevant biomarkers for Breast cancer. Using advanced bioinformatics tools and machine learning algorithms (e.g. random forest, LASSO regression, support vector machines, SVMs) together with genomic, transcriptomic and proteomic datasets obtained from the TCGA and CPTAC, the pipeline is used. It integrates feature selection, data normalization, cross platform harmonization and predictive modeling to discover major features that predict disease progression and prognosis of the patients. The selected biomarkers are validated by function enrichment analysis and protein protein interaction (PPI) network. Using this, we produce a multi-omics signature with the ability to classify tasks (AUC > 0.90) and correlates strongly with clinical outcomes. Potentially, this integrative framework demonstrates the role of the AI approach in the search for biomarkers to use for such actionable diagnostic approaches for personalized therapeutic strategies in breast cancer management.