Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring
Abstract Colorectal cancer (CRC) is the second leading cause of cancer‐related mortality in the United States when considering both men and women. Colonoscopy remains the gold standard for CRC diagnosis but is invasive, costly, and requires extensive bowel preparation and sedation. Recent advancemen...
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| Format: | Article |
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Wiley
2025-06-01
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| Series: | iMetaOmics |
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| Online Access: | https://doi.org/10.1002/imo2.70003 |
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| author | Rui Xu Hyein Jung Fouad Choueiry Shiqi Zhang Rachel Pearlman Heather Hampel Ning Jin Jieli Li Jiangjiang Zhu |
| author_facet | Rui Xu Hyein Jung Fouad Choueiry Shiqi Zhang Rachel Pearlman Heather Hampel Ning Jin Jieli Li Jiangjiang Zhu |
| author_sort | Rui Xu |
| collection | DOAJ |
| description | Abstract Colorectal cancer (CRC) is the second leading cause of cancer‐related mortality in the United States when considering both men and women. Colonoscopy remains the gold standard for CRC diagnosis but is invasive, costly, and requires extensive bowel preparation and sedation. Recent advancements in high throughput “omics” technologies may offer less invasive methods for CRC diagnosis through biomarker discovery. This study introduces a novel bioinformatics pipeline, PLS‐ANN‐DA (PANDA), combining partial least squares discriminant analysis (PLS‐DA) with an advanced artificial neural network (ANN) to improve CRC diagnosis and monitor disease progression. We analyzed metabolic alterations in CRC using a metabolomics data set of 626 CRC cases and 402 healthy controls (HC). Meanwhile, complementary transcriptomic data were also analyzed and integrated to further understand CRC metabolic dysregulations. By integrating metabolomics and transcriptomics analyses and establishing the biomarker discovery pipeline PANDA, significant metabolic pathway alterations were identified between CRC patients and healthy controls, with notable upregulation of multiple pathways in CRC. Meanwhile, we observed a downregulation of specific pathways, including purine metabolism and the tricarboxylic acid (TCA) cycle, associated with advanced tumor stages. The PANDA pipeline showed promising outcomes by effectively differentiating CRC from healthy states and providing insight into metabolic shifts occurring in advanced CRC stages. Genetic mutation‐associated metabolic changes were also discovered. Overall, this method has the potential for noninvasive CRC diagnostics and may serve as a valuable tool for understanding metabolic changes in cancer progression. |
| format | Article |
| id | doaj-art-30bc7f0b246a41f092d26a1081e2490f |
| institution | OA Journals |
| issn | 2996-9506 2996-9514 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
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| series | iMetaOmics |
| spelling | doaj-art-30bc7f0b246a41f092d26a1081e2490f2025-08-20T02:03:23ZengWileyiMetaOmics2996-95062996-95142025-06-0122n/an/a10.1002/imo2.70003Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoringRui Xu0Hyein Jung1Fouad Choueiry2Shiqi Zhang3Rachel Pearlman4Heather Hampel5Ning Jin6Jieli Li7Jiangjiang Zhu8Human Nutrition Program, Department of Human Sciences The Ohio State University Columbus Ohio USADepartment of Chemistry and Biochemistry The Ohio State University Columbus Ohio USAHuman Nutrition Program, Department of Human Sciences The Ohio State University Columbus Ohio USAHuman Nutrition Program, Department of Human Sciences The Ohio State University Columbus Ohio USAComprehensive Cancer Center The Ohio State University Columbus Ohio USADepartment of Medical Oncology & Therapeutics Research City of Hope National Cancer Center Duarte Ohio USAComprehensive Cancer Center The Ohio State University Columbus Ohio USADepartment of Pathology The Ohio State University Columbus Ohio USAHuman Nutrition Program, Department of Human Sciences The Ohio State University Columbus Ohio USAAbstract Colorectal cancer (CRC) is the second leading cause of cancer‐related mortality in the United States when considering both men and women. Colonoscopy remains the gold standard for CRC diagnosis but is invasive, costly, and requires extensive bowel preparation and sedation. Recent advancements in high throughput “omics” technologies may offer less invasive methods for CRC diagnosis through biomarker discovery. This study introduces a novel bioinformatics pipeline, PLS‐ANN‐DA (PANDA), combining partial least squares discriminant analysis (PLS‐DA) with an advanced artificial neural network (ANN) to improve CRC diagnosis and monitor disease progression. We analyzed metabolic alterations in CRC using a metabolomics data set of 626 CRC cases and 402 healthy controls (HC). Meanwhile, complementary transcriptomic data were also analyzed and integrated to further understand CRC metabolic dysregulations. By integrating metabolomics and transcriptomics analyses and establishing the biomarker discovery pipeline PANDA, significant metabolic pathway alterations were identified between CRC patients and healthy controls, with notable upregulation of multiple pathways in CRC. Meanwhile, we observed a downregulation of specific pathways, including purine metabolism and the tricarboxylic acid (TCA) cycle, associated with advanced tumor stages. The PANDA pipeline showed promising outcomes by effectively differentiating CRC from healthy states and providing insight into metabolic shifts occurring in advanced CRC stages. Genetic mutation‐associated metabolic changes were also discovered. Overall, this method has the potential for noninvasive CRC diagnostics and may serve as a valuable tool for understanding metabolic changes in cancer progression.https://doi.org/10.1002/imo2.70003artificial neural networkcolorectal cancermetabolomicsmulti‐omicspartial least squares discriminant analysistranscriptomics |
| spellingShingle | Rui Xu Hyein Jung Fouad Choueiry Shiqi Zhang Rachel Pearlman Heather Hampel Ning Jin Jieli Li Jiangjiang Zhu Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring iMetaOmics artificial neural network colorectal cancer metabolomics multi‐omics partial least squares discriminant analysis transcriptomics |
| title | Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring |
| title_full | Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring |
| title_fullStr | Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring |
| title_full_unstemmed | Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring |
| title_short | Novel machine‐learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring |
| title_sort | novel machine learning bioinformatics reveal distinct metabolic alterations for enhanced colorectal cancer diagnosis and monitoring |
| topic | artificial neural network colorectal cancer metabolomics multi‐omics partial least squares discriminant analysis transcriptomics |
| url | https://doi.org/10.1002/imo2.70003 |
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