BIOINFORMATICS ANALYSIS OF GENE EXPRESSION PATTERNS IN BREAST CANCER

Abstract

Breast cancer is one of the leading causes of cancer-related mortality among women worldwide and is characterized by substantial molecular heterogeneity. The present study aimed to analyse gene expression patterns associated with breast cancer using bioinformatics approaches and to identify potential molecular biomarkers related to different breast cancer subtypes. A publicly available microarray dataset, Breast_GSE45827, consisting of 151 samples and more than 54,000 gene expression features, was utilized for computational analysis. The dataset included normal breast tissue samples and multiple breast cancer subtypes, including basal, HER2-positive, luminal A, luminal B, and cell line samples. Data preprocessing and normalization procedures were performed to improve dataset quality and comparability among samples. Exploratory data analysis, differential gene expression analysis, Principal Component Analysis (PCA), and hierarchical clustering were subsequently conducted to investigate molecular expression patterns and subtype-specific variations. Several significantly dysregulated genes associated with tumour progression, cell proliferation, immune response, and metastasis were identified. PCA and clustering analyses demonstrated distinct molecular signatures among breast cancer subtypes, particularly within basal and HER2-positive groups. The analysis also identified potential biomarker genes that may contribute to breast cancer diagnosis, prognosis, and personalized therapeutic strategies. The findings highlighted the effectiveness of bioinformatics methods in analysing large-scale genomic datasets and identifying clinically relevant molecular targets in breast cancer. The present study contributes to the understanding of breast cancer molecular mechanisms and supports the potential application of computational biology approaches in biomarker
discovery and cancer genomics research.