COMPARATIVE ANALYSIS OF GENE EXPRESSION PROFILES ACROSS BREAST CANCER STAGES USING NEXT GENERATION SEQUENCING DATA

Abstract

Background  

Breast cancer is still the most common cancer among women worldwide, and its incidence is on the rise, especially in Pakistan and other similar nations. It is a complex illness that is impacted by lifestyle, environmental, and hereditary variables. Based on molecular traits, there are several subtypes, including as triple-negative, HER2-positive, luminal A, and luminal B. Usually, imaging, histological investigation, and tumor markers are used to confirm the diagnosis. Recent advancements in genomic technologies, particularly RNA Sequencing (RNA-Seq) and Next Generation Sequencing (NGS), have enabled researchers to investigate the transcriptomic characteristics of tumors. Nonetheless, there is a scarcity of comparative studies examining gene expression profiles at various stages of Breast Cancer. Studying gene expression patternsat each stage can aid in the identification of crucial biomarkers and potential therapeutic targets.

Objective  

This study aims to explore Breast Cancer by analyzing the network of Differentially Expressed Genes (DEGs) between normal and tumor tissues. It focuses on identifying the molecular pathways that are disrupted in the breast cancer and integrates these insights to provide a comprehensive understanding for the identification of potential tumor markers . The goal is to identify diagnostic biomarkers for early detection and therapeutic targets to enhance treatment outcomes for patients suffering from this aggressive cancer.

Methods  

This research involved the evaluation of datasets sourced from the Gene Expression Omnibus (GEO) database to identify Differentially Expressed Genes (DEGs) associated with Breast Cancer through bioinformatics analysis. DEGs were selected based on a p-value of less than 0.05 and a log fold change (logFC) of ±1.5. Subsequently, these DEGs were input into a ring database to create a protein-protein interaction (PPI) network, which revealed five hub genes. The expression levels of these significant genes were then validated using the GEPIA tool.

Results  

Through gene network analysis of datasets, we uncovered hundreds of DEGs, with a substantial overlap between datasets. We identified 387 Deferentially Expressed Genes in Invasive Ductal Carcinoma (IDC) and 261 in Ductal carcinoma in situ (DCIS), total of which is 648 DEGS, of which only 227 were upregulated and 160 were downregulated in DCIS samples while 115 were upregulated and 146 were downregulated in IDC samples. Functional enrichment and protein-protein interaction analyses revealed the biological processes and networks affected in Breast Cancer, highlighting the role of upregulated hub genes (BRCA1, TOP2A, FOXM1, TYMS, and BIRC5) through the comparison of expression levels between tumor and normal samples.

Conclusion  

This retrospective and comparative study, which examines the comparison of gene expressions between stages of Breast Cancer and normal Breast cells, has greatly enhanced our comprehension of the molecular landscape by  identifying Differentially Expressed Genes (DEGs) and disrupted molecular pathways between normal and tumor samples. Ultimately, our research uncovers diagnostic biomarkers that lay the groundwork for future investigations focused on enhancing prognosis and treatment approaches for Breast Cancer patients.