Enhancing Cancer Treatment and Understanding Through Clustering of Gene Responses to Categorical Stressors

Abstract

Abstract Cancer cells have unique metabolic activity in the glycolysis pathway compared to normal cells, which allows them to sustain their growth and proliferation. Therefore, inhibiting glycolytic pathways may provide a promising therapeutic approach to cancer treatment. In this first-of-its-kind study, we analyzed the genetic responses of cancer cells to stressors, particularly drugs that target the glycolysis pathway. Gene expression data for experiments on different types of cancer cells were retrieved from the Gene Expression Omnibus and expression fold-change was then clustered after dimensionality reduction. We identified four response clusters, the first and third are affected the most by anti-glycolytic drugs, consisting mainly of squamous and mesenchymal tissues, showing higher mitotic inhibition and apoptosis. Drugs acting on several glycolytic targets at once resulted in such responses. The second and fourth clusters were relatively unaffected by the treatments, succumbing the least to glycolysis inhibitors. These clusters are mainly gynecological and hormone-sensitive, with drugs acting on hexokinases mainly inducing this response. This study highlights the importance of analyzing the molecular states of cancer cells to identify potential targets for personalized cancer treatments and to improve our understanding of the disease.