Machine Learning-Based Classification of Breast Cancer using TCGA Transcriptomics Data

Abstract: Breast cancer (BRCA) remains one of the most prevalent and challenging malignancies worldwide, necessitating advanced approaches for diagnosis and therapeutic intervention. In this study, we utilized transcriptomics data from The Cancer Genome Atlas (TCGA-BRCA) to classify tumor versus normal breast tissue samples using machine learning techniques. RNA-seq data normalized using RSEM was employed to assess differential gene expression profiles. The dataset underwent preprocessing steps, including removing genes with zero expression and log2 transformation of normalized values to ensure data quality and consistency. Three machine learning algorithms-Logistic Regression (LR), Neural Networks (NN), and Random Forest (RF)-were implemented to classify tumor and normal samples. The dataset was randomly split into training (70%) and testing (30%) subsets for cross-validation over 100 iterations. Classifier performance was evaluated using the Area Under Curve-Receiver Operating Characteristic Curve (AUC-ROC). Logistic regression provided interpretable insights into the key features, while neural networks and random forests offered robust, nonlinear classification capabilities. To further refine the models, we identified a minimal set of informative features capable of discriminating tumors from normal samples with high accuracy. The results underscore the potential of machine learning in leveraging high-dimensional gene expression data for precise classification, offering a foundation for further exploration into biomarkers and personalized therapeutic strategies in breast cancer. Keywords: Breast cancer, The Cancer Genome Atlas, RNA sequencing and Machine learning.