Abstract
Breast cancer is an
alarming global health concern, including a vast and varied set of illnesses
with different molecular characteristics. The fusion of sophisticated
computational methodologies with extensive biological datasets has emerged as
an effective strategy for unravelling complex patterns in cancer oncology. This
research delves into breast cancer staging, classification, and diagnosis by
leveraging the comprehensive dataset provided by the The Cancer Genome Atlas
(TCGA). By integrating advanced machine learning algorithms with bioinformatics
analysis, it introduces a cutting-edge methodology for identifying complex
molecular signatures associated with different subtypes and stages of breast
cancer. This study utilizes TCGA gene expression data to detect and categorize
breast cancer through the application of machine learning and systems biology
techniques. Researchers identified differentially expressed genes in breast
cancer and analyzed them using signaling pathways, protein-protein interactions,
and regulatory networks to uncover potential therapeutic targets. The study
also highlights the roles of specific proteins (MYH2, MYL1, MYL2, MYH7) and
microRNAs (such as hsa-let-7d-5p) that are the potential biomarkers in cancer
progression founded on several analyses. In terms of diagnostic accuracy for
cancer staging, the random forest method achieved 97.19%, while the XGBoost
algorithm attained 95.23%. Bioinformatics and machine learning meet in this
study to find potential biomarkers that influence the progression of breast
cancer. The combination of sophisticated analytical methods and extensive
genomic datasets presents a promising path for expanding our understanding and
enhancing clinical outcomes in identifying and categorizing this intricate illness.