From multi-omics to predictive biomarker: AI in tumor microenvironment
In recent years, tumors have emerged as a major global health threat. An increasing number of studies indicate that the production, development, metastasis, and elimination of tumor cells are closely related to the tumor microenvironment (TME). Advances in artificial intelligence (AI) algorithms, pa...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1514977/full |
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| author | Luo Hai Luo Hai Ziming Jiang Haoxuan Zhang Yingli Sun Yingli Sun Yingli Sun |
| author_facet | Luo Hai Luo Hai Ziming Jiang Haoxuan Zhang Yingli Sun Yingli Sun Yingli Sun |
| author_sort | Luo Hai |
| collection | DOAJ |
| description | In recent years, tumors have emerged as a major global health threat. An increasing number of studies indicate that the production, development, metastasis, and elimination of tumor cells are closely related to the tumor microenvironment (TME). Advances in artificial intelligence (AI) algorithms, particularly in large language models, have rapidly propelled research in the medical field. This review focuses on the current state and strategies of applying AI algorithms to tumor metabolism studies and explores expression differences between tumor cells and normal cells. The analysis is conducted from the perspectives of metabolomics and interactions within the TME, further examining the roles of various cytokines. This review describes the potential approaches through which AI algorithms can facilitate tumor metabolic studies, which offers a valuable perspective for a deeper understanding of the pathological mechanisms of tumors. |
| format | Article |
| id | doaj-art-0ae9848caf05415c942b79a1f7eb2c05 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-0ae9848caf05415c942b79a1f7eb2c052024-12-23T06:39:15ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-12-011510.3389/fimmu.2024.15149771514977From multi-omics to predictive biomarker: AI in tumor microenvironmentLuo Hai0Luo Hai1Ziming Jiang2Haoxuan Zhang3Yingli Sun4Yingli Sun5Yingli Sun6Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, ChinaShenzhen Key Laboratory of Epigenetics and Precision Medicine for Cancers, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, ChinaDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaCentral Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, ChinaShenzhen Key Laboratory of Epigenetics and Precision Medicine for Cancers, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, ChinaKey Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, ChinaIn recent years, tumors have emerged as a major global health threat. An increasing number of studies indicate that the production, development, metastasis, and elimination of tumor cells are closely related to the tumor microenvironment (TME). Advances in artificial intelligence (AI) algorithms, particularly in large language models, have rapidly propelled research in the medical field. This review focuses on the current state and strategies of applying AI algorithms to tumor metabolism studies and explores expression differences between tumor cells and normal cells. The analysis is conducted from the perspectives of metabolomics and interactions within the TME, further examining the roles of various cytokines. This review describes the potential approaches through which AI algorithms can facilitate tumor metabolic studies, which offers a valuable perspective for a deeper understanding of the pathological mechanisms of tumors.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1514977/fulltumor cellstumor microenvironmentmetabolomicsinteractionsartificial intelligence |
| spellingShingle | Luo Hai Luo Hai Ziming Jiang Haoxuan Zhang Yingli Sun Yingli Sun Yingli Sun From multi-omics to predictive biomarker: AI in tumor microenvironment Frontiers in Immunology tumor cells tumor microenvironment metabolomics interactions artificial intelligence |
| title | From multi-omics to predictive biomarker: AI in tumor microenvironment |
| title_full | From multi-omics to predictive biomarker: AI in tumor microenvironment |
| title_fullStr | From multi-omics to predictive biomarker: AI in tumor microenvironment |
| title_full_unstemmed | From multi-omics to predictive biomarker: AI in tumor microenvironment |
| title_short | From multi-omics to predictive biomarker: AI in tumor microenvironment |
| title_sort | from multi omics to predictive biomarker ai in tumor microenvironment |
| topic | tumor cells tumor microenvironment metabolomics interactions artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1514977/full |
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