Predictive Insights: Unmasking Breast Cancer Biomarkers through machine learning and Systems Biology

Abstract

Breast cancer is a complex and heterogeneous disease in nature with quite high rates
of metastasis and recurrence that cause significant morbidity and mortality. Despite the
improved treatment options with new medical therapies, a proper understanding of the molecular mechanism in breast cancer development and its progression is of utmost necessity. Hence, we conducted a comprehensive analysis on transcriptomic profiling combined with SHAP feature importance calculation in an attempt to find potential molecular targets. Among the 9 machine learning models generated, random forest model displayed an accuracy value of 0.96 for breast cancer prediction. KRT17, KRT5 and FABP5 were the commonly resulted prognostic biomarkers during the DGE and feature selection approaches. Furthermore, gene enrichment and functional annotations of key genes reveals the importance of these key genes in breast cancer progression. The survival analysis confirms the risk associate with key genes in breast cancer patients. Therefore, this finding show the effectiveness of machine learning combine with DGE in Biomarkers discovery and experimental validation of these genes would be a promising approach to eliminate the clinical complications during the breast cancer treatment.