Mapping relationships between glutathione and SLC25 transporters in cancers using hybrid machine learning models

Abstract

AbstractAlterations of metabolism are a well appreciated hallmark of many cancers, including changes in mitochondrial and glutathione (GSH) metabolism. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Here, we apply hybrid machine learning (ML) models and leverage cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential candidates involved in mGSH metabolism and membrane transport in cancers. These models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to GSH metabolism in cancers. Additionally, SLC25A24 and SLC25A37 along with the poorly characterized members of the SLC25 family, SLC25A43 and SLC25A50, are predicted to be associated. Similarities in potential substrate binding regions are found between SLC25A39 and the candidates SLC25A24 and A43. These findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets.