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Microbial imbalance (dysbiosis) is associated with multiple conditions, including cancer, where pathogenic bacteria can promote inflammation and tumor formation. Advances in high-throughput sequencing, metagenomics, and machine learning improve risk prediction through better cancer categorization from microbiological data. Next-generation sequencing enables profiling of cultivable and non-cultivable bacteria, identifying microbial signatures and metabolic pathways linked to cancer. 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Using New Technologies to Analyze Gut Microbiota and Predict Cancer Risk. Cells 2024, 13, 1987. [https://](https://)[ ](https://)[doi.org/10.3390/cells13231987](doi.org/10.3390/cells13231987)  \nAcademic Editor: Bernhard Ryffel  \nReceived: 14 November 2024  \nRevised: 29 November 2024  \nAccepted: 30 November 2024  \nPublished: 1 December 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Student Research Committee, Semnan University of Medical Sciences, Semnan 35147-99442, Iran; [hemmati.m.amin@gmail.com](hemmati.m.amin@gmail.com) (M.A.H.); [behinaforouzanmehr1377@gmail.com](behinaforouzanmehr1377@gmail.com) (B.F.)  \n2 Department of Basic Science, Faculty of Pharmacy and Pharmaceutical Science, Tehran Medical Science, Islamic Azad University, Tehran 19395-1495, Iran; [mmomeni1976@gmail.com](mmomeni1976@gmail.com)  \n[3](3 Faculty of Medicine)[ Faculty of Medicine](3 Faculty of Medicine), [Semnan University of Medical Sciences](Semnan University of Medical Sciences), [Semnan 35147-99442](Semnan 35147-99442), [Iran](Iran); [shimaasl1378@gmail.com](shimaasl1378@gmail.com) (S.A.); [sinamohammadi911@yahoo.com](sinamohammadi911@yahoo.com) (S.M.)  \n4 Department of Immunology, Semnan University of Medical Sciences, Semnan 35147-99442, Iran; [dhaghmorad@gmail.com](dhaghmorad@gmail.com)  \n5 Department of Clinical Science, University of Bergen, 5020 Bergen, Norway  \n6 Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7028 Trondheim, Norway  \n7 Department of Biosciences and Nutrition, Karolinska Institutet, 14183 Huddinge, Sweden  \n8 Cancer Research Center, Faculty of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran  \n9 Department of Bacteriology and Virology, Faculty of Medicine, Semnan University of Medical Sciences, Semnan 35147-99442, Iran  \n* [Correspondence: valentyn.oksenych@uib.no](Correspondence: valentyn.oksenych@uib.no) (V.O.); [m.eslami@semums.ac.ir](m.eslami@semums.ac.ir) (M.E.); Tel.: +98-9144078609 (M.E.)  \nAbstract: The gut microbiota significantly impacts human health, influencing metabolism, immunological responses, and disease prevention. Dysbiosis, or microbial imbalance, is linked to various diseases, including cancer. It is crucial to preserve a healthy microbiome since pathogenic bacteria, such as Escherichia coli and Fusobacterium nucleatum, can cause inflammation and cancer. These pathways can lead to the formation of tumors. Recent advancements in high-throughput sequencing, metagenomics, and machine learning have revolutionized our understanding of the role of gut microbiota in cancer risk prediction. Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. Additionally, the investigation of the microbiome has been transformed by next-generation sequencing (NGS), which has made it possible to fully profile both cultivable and non-cultivable bacteria and to understand their roles in connection with cancer. Among the uses of NGS are the detection of microbial fingerprints connected to treatment results and the investigation of metabolic pathways implicated in the development of cancer. The combination of NGS with machine learning opens up new possibilities for creating customiz","cbCaivSfhWJI3rhv","https://ap.wps.com/l/cbCaivSfhWJI3rhv","pdf",1707595,24,"English","# Introduction\n## Using New Technologies to Analyze Gut Microbiota\n# Abstract\n# Keywords","[{\"question\":\"How does gut dysbiosis relate to cancer risk?\",\"answer\":\"Dysbiosis—microbial imbalance—is linked to diseases including cancer, and pathogenic bacteria can drive inflammation that contributes to tumor development.\"},{\"question\":\"What technologies are emphasized for analyzing gut microbiota in cancer prediction?\",\"answer\":\"High-throughput sequencing methods such as metagenomics and next-generation sequencing (NGS) are highlighted, along with machine learning for improved risk prediction and cancer categorization.\"},{\"question\":\"How does combining NGS with machine learning support personalized cancer prevention or treatment?\",\"answer\":\"Machine learning leverages microbiological data derived from NGS to create diagnostic tools and treatments tailored to an individual’s gut microbiome profile, even when data complexity is a challenge.\"}]","Using New Technologies to Analyze Gut Microbiota and Predict Cancer Risk - Review | PDF",1789984677]