Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer
ObjectiveThe metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1565090/full |
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| author | Jian Yue Jian Yue Huiying Fang Qian Yang Qian Yang Rui Feng Guosheng Ren |
| author_facet | Jian Yue Jian Yue Huiying Fang Qian Yang Qian Yang Rui Feng Guosheng Ren |
| author_sort | Jian Yue |
| collection | DOAJ |
| description | ObjectiveThe metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately identify key molecules that may become potential therapeutic targets for CRC.MethodsThis study integrates bulk transcriptome and single-cell transcriptome to analyze and identify key MAAD-related genes from multiple levels. Subsequently, numerous machine learning methods were incorporated to construct MAAD-related prognostic models, and the infiltration of immune cells, tumor heterogeneity, tumor mutation burden, and potential pathway changes under different modes were analyzed. Finally, key molecules were identified for experimental validation.ResultsWe successfully constructed prognostic models and Nomograms based on key MAAD-related molecules. There was a notable survival benefit observed for low-risk patients when contrasted with their high-risk counterparts. In addition, the high-risk group had a poorer response to immunotherapy and stronger tumor heterogeneity compared with the low-risk group. Further research found that by knocking down the MAAD-related gene. LSM8, the malignant characteristics of colorectal cancer cell lines were significantly alleviated, suggesting that LSM8 may become a potential therapeutic target.ConclusionThe MAAD-related gene LSM8 is likely involved in the progression of CRC and could be a hopeful target for therapeutic intervention. |
| format | Article |
| id | doaj-art-8e43b6ef0e174f5b8bc2af0b5c1e0901 |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-8e43b6ef0e174f5b8bc2af0b5c1e09012025-08-20T02:42:08ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-03-011510.3389/fonc.2025.15650901565090Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancerJian Yue0Jian Yue1Huiying Fang2Qian Yang3Qian Yang4Rui Feng5Guosheng Ren6Department of Breast and Thyroid Surgery, Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Breast Surgery, Gaozhou People’s Hospital, Gaozhou, Guangdong, ChinaDepartment of Breast Cancer Center, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, Chongqing, ChinaDepartment of Breast and Thyroid Surgery, Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaInstitute for Brain Science and Disease, Key Laboratory of Major Brain Disease and Aging Research (Ministry of Education), Chongqing Medical University, Chongqing, ChinaDepartment of Breast and Thyroid Surgery, Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Breast and Thyroid Surgery, Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaObjectiveThe metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately identify key molecules that may become potential therapeutic targets for CRC.MethodsThis study integrates bulk transcriptome and single-cell transcriptome to analyze and identify key MAAD-related genes from multiple levels. Subsequently, numerous machine learning methods were incorporated to construct MAAD-related prognostic models, and the infiltration of immune cells, tumor heterogeneity, tumor mutation burden, and potential pathway changes under different modes were analyzed. Finally, key molecules were identified for experimental validation.ResultsWe successfully constructed prognostic models and Nomograms based on key MAAD-related molecules. There was a notable survival benefit observed for low-risk patients when contrasted with their high-risk counterparts. In addition, the high-risk group had a poorer response to immunotherapy and stronger tumor heterogeneity compared with the low-risk group. Further research found that by knocking down the MAAD-related gene. LSM8, the malignant characteristics of colorectal cancer cell lines were significantly alleviated, suggesting that LSM8 may become a potential therapeutic target.ConclusionThe MAAD-related gene LSM8 is likely involved in the progression of CRC and could be a hopeful target for therapeutic intervention.https://www.frontiersin.org/articles/10.3389/fonc.2025.1565090/fullmetabolismbioinformaticsRNAseqLSM8colorectal cancer |
| spellingShingle | Jian Yue Jian Yue Huiying Fang Qian Yang Qian Yang Rui Feng Guosheng Ren Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer Frontiers in Oncology metabolism bioinformatics RNAseq LSM8 colorectal cancer |
| title | Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer |
| title_full | Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer |
| title_fullStr | Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer |
| title_full_unstemmed | Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer |
| title_short | Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer |
| title_sort | integrating multi omics and machine learning methods reveals the metabolism of amino acids and derivatives related signature in colorectal cancer |
| topic | metabolism bioinformatics RNAseq LSM8 colorectal cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1565090/full |
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