Overview
This project focused on identifying the biological pathways and cellular functions affected by estradiol treatment in MCF7 breast cancer cell lines.
By applying functional enrichment analysis, the study aimed to interpret how hormone exposure influences gene expression at a systems biology level.
Objectives
- Perform functional enrichment analysis on differentially expressed genes (DEGs) obtained from MCF7 cells after estradiol treatment.
- Identify Gene Ontology (GO) categories and biological processes significantly associated with hormone-induced transcriptional changes.
- Discuss potential cellular and molecular effects linked to estrogen receptor signaling and cell proliferation.
Methodology
Data Input:
The DEG list was obtained from publicly available transcriptomic datasets comparing control and estradiol-treated MCF7 cells.Functional Enrichment:
- Used g:Profiler to map DEGs to biological pathways and GO terms.
- Evaluated the top enriched categories within the Biological Process (BP) and Molecular Function (MF) domains.
- Adjusted for multiple testing using Benjamini–Hochberg FDR correction to ensure statistical reliability.
Biological Interpretation:
- The most enriched processes included cell cycle regulation, DNA replication, and response to hormone stimulus.
- Results suggested that estradiol promotes proliferative and metabolic pathways through estrogen receptor-mediated transcriptional activity.
Tools and Technologies
- Language: Python
- Tools: g:Profiler, Ensembl BioMart, GEO Database
- Analysis Techniques: Gene Ontology (GO) Enrichment, Multiple Testing Correction, Pathway Interpretation
Reflections
This project enhanced my understanding of how computational analysis bridges molecular biology and data science.
It also strengthened my ability to extract biological meaning from high-dimensional gene expression data, reinforcing my interest in explainable and interpretable AI for life sciences.