Decoding the latent molecular interactions between aspartame and hepatocellular carcinoma: A multi-omics and machine learning integration

Objective To understand the latent molecular interactions between aspartame and hepatocellular carcinoma (HCC), this study analyzes the critical functions of genes involved in redox imbalance that may facilitate malignancy. Materials and Methods We employed a hybrid computational strategy – combining transcriptomic differential expression, weighted gene co-expression network analysis, and an ensemble of 12 machine learning techniques – to isolate central oxidative stress (OS) genes involved in the aspartame-HCC axis. The binding capacities of aspartame toward identified target molecules were subsequently authenticated through network toxicology and molecular docking analyses. Results In total, 42 shared targets were discovered within the aspartame-HCC axis. Utilizing machine learning, four pivotal OS-related genes – specifically rho family guanosine triphosphatase (GTPase) 3 (RND3), glutathione S-transferase zeta 1 (GSTZ1), aurora kinase A (AURKA), and ADP-ribosylation factor 1 (ARF1) – were prioritized. Among these, ARF1 and AURKA showed significant overexpression, whereas GSTZ1 and RND3 exhibited substantial downregulation in hepatic malignancy ( P < 0.001). Furthermore, the structural interactions between aspartame and these central proteins were validated via docking, revealing strong affinities with energies between −7.1 and −5.0 kcal/mol. Conclusion This study highlights that aspartame may be associated with HCC progression via potential interactions with specific OS genes and the associated signaling pathways.