Abstract
Gene expression profiling is a powerful tool that has been extensively used to investigate the underlying biology and etiology of diseases, including cancer. Microarray gene expression analysis enables simultaneous measurement of thousands of mRNA levels. Sophisticated computational approaches have evolved in parallel with the rapid progress in bioassay technologies, enabling more effective analysis of the large and complex datasets that these technologies produce. In this study, we utilized systems biology approaches to examine gene expression profiles across different grades of breast cancer progression. We conducted a meta-analysis of publicly available microarray data to elucidate the molecular mechanisms underlying breast cancer grade classification. Our results suggest that while grade index is commonly used for evaluating cancer progression status in the clinic, the complexity of molecular mechanisms, histological characteristics, and other factors related to patient outcomes raises doubts about the utility of breast cancer grades as a foundation for formulating treatment protocols. Our study underscores the importance of advancing personalized strategies for breast cancer classification and management. More research is crucial to refine diagnostic tools and treatment modalities, aiming for greater precision and tailored care in patient outcomes.