Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning
Abstract Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08915-1 |
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| author | Jun Wang Yuan Cong Bo Tang Juan Liu Ke Pu |
| author_facet | Jun Wang Yuan Cong Bo Tang Juan Liu Ke Pu |
| author_sort | Jun Wang |
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| description | Abstract Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a machine learning-based prognostic model. Our microbiome analysis revealed significant differences between CRC and normal tissues. Multi-omics clustering identified two major CRC subtypes, CS1 and CS2, with distinct molecular characteristics and survival outcomes. We developed the Multi-Omics Integrative Clustering and Machine Learning Score (MCMLS) model, which demonstrated strong prognostic value in predicting patient survival and outperformed existing models. The MCMLS low-score group exhibited higher immune cell infiltration, increased metabolic pathway activity, and potentially better immunotherapy response. In contrast, the MCMLS high-score group showed higher mutation burden, fibroblast infiltration, and enrichment of cell adhesion and migration pathways. Bacterial analysis revealed differentially abundant bacteria associated with prognosis. Importantly, MCMLS consistently predicted immunotherapy response across six independent datasets. Our findings highlight the complex interplay between the gut microbiome, tumor microenvironment, and immune landscape in CRC, providing valuable insights for improving patient stratification and personalized treatment strategies. |
| format | Article |
| id | doaj-art-cb94f9db75cf450b99a52ca27a3ba4c3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-cb94f9db75cf450b99a52ca27a3ba4c32025-08-20T03:46:03ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-08915-1Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learningJun Wang0Yuan Cong1Bo Tang2Juan Liu3Ke Pu4Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical CollegeDepartment of Gastroenterology, Affiliated Hospital of North Sichuan Medical CollegeSchool of Medicine, University of Electronic Science and Technology of ChinaDepartment of Gastroenterology, Affiliated Hospital of North Sichuan Medical CollegeDepartment of Gastroenterology, Affiliated Hospital of North Sichuan Medical CollegeAbstract Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a machine learning-based prognostic model. Our microbiome analysis revealed significant differences between CRC and normal tissues. Multi-omics clustering identified two major CRC subtypes, CS1 and CS2, with distinct molecular characteristics and survival outcomes. We developed the Multi-Omics Integrative Clustering and Machine Learning Score (MCMLS) model, which demonstrated strong prognostic value in predicting patient survival and outperformed existing models. The MCMLS low-score group exhibited higher immune cell infiltration, increased metabolic pathway activity, and potentially better immunotherapy response. In contrast, the MCMLS high-score group showed higher mutation burden, fibroblast infiltration, and enrichment of cell adhesion and migration pathways. Bacterial analysis revealed differentially abundant bacteria associated with prognosis. Importantly, MCMLS consistently predicted immunotherapy response across six independent datasets. Our findings highlight the complex interplay between the gut microbiome, tumor microenvironment, and immune landscape in CRC, providing valuable insights for improving patient stratification and personalized treatment strategies.https://doi.org/10.1038/s41598-025-08915-1Colorectal cancerMulti-omics data integrationGut microbiota compositionMachine learning-based prognostic modelImmunotherapy response prediction |
| spellingShingle | Jun Wang Yuan Cong Bo Tang Juan Liu Ke Pu Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning Scientific Reports Colorectal cancer Multi-omics data integration Gut microbiota composition Machine learning-based prognostic model Immunotherapy response prediction |
| title | Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning |
| title_full | Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning |
| title_fullStr | Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning |
| title_full_unstemmed | Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning |
| title_short | Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning |
| title_sort | integrative analysis of multi omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning |
| topic | Colorectal cancer Multi-omics data integration Gut microbiota composition Machine learning-based prognostic model Immunotherapy response prediction |
| url | https://doi.org/10.1038/s41598-025-08915-1 |
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