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Four response groups emerge, with two strongly affected by anti-glycolytic agents and two relatively blunted, highlighting molecular-state analysis as a route to identify personalized therapy targets.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/enhancing-cancer-treatment-and-understanding-through-clustering-of-gene-responses-to-categorical-stressors/382298/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/enhancing-cancer-treatment-and-understanding-through-clustering-of-gene-responses-to-categorical-stressors/382298.png","ImageObject",300,407,{"name":42,"@type":43},"Noah","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-24",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main therapeutic rationale behind targeting glycolysis in cancer?","Question",{"text":63,"@type":64},"Cancer cells rely on glycolysis for energy and biosynthetic intermediates, so inhibiting glycolytic pathways may selectively impair growth and proliferation.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were gene responses grouped in this study?",{"text":68,"@type":64},"Gene expression fold changes from Gene Expression Omnibus experiments were clustered after dimensionality reduction, then evaluated using indices such as 1 − PAC, concordance, and mean silhouette.",{"name":70,"@type":61,"acceptedAnswer":71},"What do the four identified response groups suggest?",{"text":72,"@type":64},"Some groups show strong sensitivity to anti-glycolytic drugs with higher mitotic inhibition and apoptosis, while others remain relatively unaffected, indicating heterogeneity in molecular 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nature.com/scientificreports)  \nOPEN  \nEnhancing cancer treatment and understanding  \nthrough clustering of gene responses to categorical stressors  \nChristopher El Hadi1*, George Hilal2 & Rita Aoun2  \nCancer 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 ofsquamous 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.  \nIn the 1920s, Otto Warburg and his colleagues observed a notable uptake of glucose in tumors in comparison to that observed in the surrounding tissues. Subsequent investigation revealed glucose fermentation, resulting in lactate production even in the presence of oxygen, a phenomenon termed \"aerobic glycolysis\"1,2. This paradoxical prevalence of glycolysis in cancer cells is referred to as the \"Warburg effect.\" The catabolism of glucose to lactate yields a relatively low amount of energy, thereby necessitating high rates of glucose consumption to meet the energetic and anabolic demands of cancer cells3. In addition to providing energy, glycolysis generates metabolic intermediates that enable de novo synthesis of nucleotides, amino acids, lipids, and NADPH, all of which are essential for rapid cell proliferation4. Cancer cells thus possess a distinct metabolic profile that permits a high rate of proliferation and resistance to apoptosis signals5.  \nInhibition of glycolytic pathways could be a promising selective approach in cancer research for developing targeted anticancer agents. Chen6 and Abdel-Wahab7 and colleagues have evaluated various inhibitory drug modalities that forestall glucose utilization. These drugs have been tested on cancer cells, followed by gene expression evaluation to identify more potent agents with a lethal metabolic impact on cancer cells. Currently, many glycolytic inhibitors are undergoing preclinical and clinical studies, demonstrating promising results6,8,9.  \nWhile many studies have described the effects of glycolysis inhibitory drugs on various cancers, there has been no effort to identify a common thread among the outcomes of these treatments. This study aims to fill that gap by examining how cancer cells respond to stressful environments, specifically those induced by glycolysis inhibitory drugs. By making use of fundamental cancer characteristics such as the molecules the drugs target, along with cancer “iClusters” extracted using an integrative clustering technique proposed by Shen et al.10, 11, we hope to uncover hidden patterns that summarize cancer cell behavior under such conditions. The iClusters represent a new way of classifying all cancers based on molecular signatures rather than histology12. In their 2018 publication, Hoadly et al. expanded on this concept by analyzing approximately 33 tumor types or 10,000 samples and identifying 28 distinct iCl","cbCairP8pzATU7Hg","https://ap.wps.com/l/cbCairP8pzATU7Hg","pdf",3589713,"English","# Results\n## Clustering evaluation and selected k\n## Data matrices and filtering\n## Consensus partitioning and CDF/PCA visualization","[{\"question\":\"What is the main therapeutic rationale behind targeting glycolysis in cancer?\",\"answer\":\"Cancer cells rely on glycolysis for energy and biosynthetic intermediates, so inhibiting glycolytic pathways may selectively impair growth and proliferation.\"},{\"question\":\"How were gene responses grouped in this study?\",\"answer\":\"Gene expression fold changes from Gene Expression Omnibus experiments were clustered after dimensionality reduction, then evaluated using indices such as 1 − PAC, concordance, and mean silhouette.\"},{\"question\":\"What do the four identified response groups suggest?\",\"answer\":\"Some groups show strong sensitivity to anti-glycolytic drugs with higher mitotic inhibition and apoptosis, while others remain relatively unaffected, indicating heterogeneity in molecular responses.\"}]","Enhancing cancer treatment and understanding through clustering of gene responses to categorical stressors | PDF",1790249928,25]