Introduction: Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. Areas Covered:The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.Expert Opinion: In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity. Article highlights:• Diseases were originally thought to be the result of a single gene mutation, but advances in DNA sequencing have proven otherwise.• Methods for classifying genetic variants are evolving, and they all show that genetic variants need to be studied in a more robust manner.• Complex diseases occur when the right environmental factors and SNPs exist. The latter are accumulating without understanding their significance, requiring integration into multiomic studies.• Gene networks are dominated by universal laws, proving they are credible to consider as the new "units of study" instead of single genes.• Network and polygenic studies allow for more accurate prediction and prognosis of cancer risk, treatment-useful cancer subtyping, and the discovery of interesting cancer therapies.• Comprehensive network studies integrating all types of data (e.g. transcriptomics, blood serum omics, and environmental agents) are the future of medical care.
Background Breast cancer (BC) is the most frequently diagnosed cancer in women. Altering glucose metabolism and its effects on cancer progression and treatment resistance is an emerging interest in BC research. For instance, combining chemotherapy with glucose-lowering drugs (2-deoxyglucose (2-DG), metformin (MET)) or glucose starvation (GS) has shown better outcomes than with chemotherapy alone. However, the genes and molecular mechanisms that govern the action of these glucose deprivation conditions have not been fully elucidated. Here, we investigated the differentially expressed genes in MCF-7 and MDA-MB-231 BC cell lines upon treatment with glucose-lowering drugs (2-DG, MET) and GS using microarray analysis to study the difference in biological functions between the glucose challenges and their effect on the vulnerability of BC cells. Methods MDA-MB-231 and MCF-7 cells were treated with 20 mM MET or 4 mM 2-DG for 48 h. GS was performed by gradually decreasing the glucose concentration in the culture medium to 0 g/L, in which the cells remained with fetal bovine serum for one week. Expression profiling was carried out using Affymetrix Human Clariom S microarrays. Differentially expressed genes were obtained from the Transcriptome Analysis Console and enriched using DAVID and R packages. Results Our results showed that MDA-MB-231 cells were more responsive to glucose deprivation than MCF-7 cells. Endoplasmic reticulum stress response and cell cycle inhibition were detected after all three glucose deprivations in MDA-MB-231 cells and only under the metformin and GS conditions in MCF-7 cells. Induction of apoptosis and inhibition of DNA replication were observed with all three treatments in MDA-MB-231 cells and metformin-treated MCF-7 cells. Upregulation of cellular response to reactive oxygen species and inhibition of DNA repair mechanisms resulted after metformin and GS administration in MDA-MB-231 cell lines and metformin-treated MCF-7 cells. Autophagy was induced after 2-DG treatment in MDA-MB-231 cells and after metformin in MCF-7 cells. Finally, inhibition of DNA methylation were observed only with GS in MDA-MB-231 cells. Conclusion The procedure used to process cancer cells and analyze their expression data distinguishes our study from others. GS had the greatest effect on breast cancer cells compared to 2-DG and MET. Combining MET and GS could restrain both cell lines, making them more vulnerable to conventional chemotherapy.
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.
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.
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.
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