Transcriptomic Analysis of Gallic Acid's Apoptotic Effect on Cervical Cancer Cells

Abstrak

This study investigates gallic acid's (GA) anticancer effects on HeLa cells by analyzing gene expression changes over time. An analysis was performed using transcriptomic data from HeLa cells treated with 50 µg/mL of GA at four different time points (2, 4, 6, and 9 hours) sourced from the GEO database. Predictive machine learning model for GA's IC₅₀ value was developed, and molecular docking with catalase was performed. Analysis revealed 4013 differentially expressed genes, narrowed down to 288 genes that were consistently regulated across time points, with TNF identified as the key hub gene, showing significant fold increases. The predictive model estimated an IC₅₀ value of 46.376,71 nM for GA, and the following docking indicated favorable binding interactions with catalase. These in-silico findings suggest GA's potential as an anticancer agent by promoting TNF-mediated apoptosis in HeLa cells, warranting further in-vitro validation and exploration of its therapeutic potential. Translational potential of GA in cervical cancer remains speculative as there are absence of robust human pharmacokinetic data for GA.