Enhancing cancer treatment and understanding through clustering of gene responses to categorical stressors

Abstract

Cancer cells have a unique metabolic activity in the glycolysis pathway compared to normal cells, which allows them to maintain their growth and proliferation. Therefore, inhibition of glycolytic pathways may be a promising therapeutic approach for cancer treatment. In this novel study, we analyzed the genetic responses of cancer cells to stressors, particularly to drugs that target the glycolysis pathway. Gene expression data for experiments on different cancer cell types were extracted from the Gene Expression Omnibus and the expression fold change was then clustered after dimensionality reduction. We identified four groups of responses: the first and third were most affected by anti-glycolytic drugs, especially those acting on multiple pathways at once, and consisted mainly of squamous and mesenchymal tissues, showing higher mitotic inhibition and apoptosis. The second and fourth groups were relatively unaffected by treatment, comprising mainly gynecologic and hormone-sensitive groups, succumbing least to glycolysis inhibitors. Hexokinase-targeted drugs mainly showed this blunted effect on cancer cells. This study highlights the importance of analyzing the molecular states of cancer cells to identify potential targets for personalized cancer therapies and to improve our understanding of the disease.