[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124128-en":3,"doc-seo-124128-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124128,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data","Machine learning-based proteomics analysis identifies protein biomarkers for colorectal cancer by integrating machine learning with protein network interpretation. The study leverages a large proteome profile collection from healthy individuals and colorectal cancer patients to find markers with strong predictive ability. Three classifiers—LASSO, XGBoost, and LightGBM—are trained with grid-search hyperparameter tuning. Model interpretability uses SHapley Additive exPlanations to quantify each protein’s contribution, highlighting TFF3, LCN2, and CEACAM5. Protein quantitative trait loci analysis supports roles for TFF1, CEACAM5, and SELE, linking to PI3K/Akt and MAPK signaling pathways.","TYPE Original Research PUBLISHED 07 January 2025 DOI 10.3389/fonc.2024.1505675  \nOPEN ACCESS  \nEDITED BY  \nNemat Ali,  \nKing Saud University, Saudi Arabia  \nREVIEWED BY  \nMusalula Sinkala,  \nUniversity of Cape Town, South Africa Kang Qin,  \nUniversity of Texas MD Anderson Cancer Center, United States  \n*CORRESPONDENCE  \nAnimesh Acharjee  \n [a.acharjee@bham.ac.uk](a.acharjee@bham.ac.uk)  \nRECEIVED 03 October 2024  \nACCEPTED 02 December 2024  \nPUBLISHED 07 January 2025  \nCITATION  \nRadhakrishnan SK, Nath D, Russ D, Merodio LB, Lad P, Daisi FK and Acharjee A (2025)  \nMachine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.  \nFront. Oncol. 14:1505675 .  \ndoi: 10.3389/fonc.2024.1505675  \nCOPYRIGHT  \n© 2025 Radhakrishnan, Nath, Russ, Merodio, Lad, Daisi and Acharjee. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data  \nSwarnima Kollampallath Radhakrishnan 1, Dipanwita Nath 1, Dominic Russ 1,2,3, Laura Bravo Merodio 1,2,3, Priyani Lad 1, Folakemi Kola Daisi 1 and Animesh Acharjee 1,2,3*  \n1College of Medicine and Health, School of Medical Sciences, Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom, 2 Institute of Translational Medicine, University Hospitals Birmingham National Health Service (NHS) Foundation Trust, Birmingham, United Kingdom, 3Centre for Health Data Research, University of Birmingham, Birmingham, United Kingdom  \nColorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein’s contribution to the model ’s predictions using SHapley Additive exPlanations values. Three classifiers—LASSO, XGBoost, and LightGBM were evaluated for predictive performance along with hyperparameter tuning of each model using grid search, with LASSO achieving the highest AUC of 75% in the UK Biobank dataset and the AUCs for LightGBM and XGBoost are 69 . 61% and 71 .42%, respectively. Using SHapley Additive exPlanations values, TFF3, LCN2, and CEACAM5 were found to be key biomarkers associated with cell adhesion and inflammation. Protein quantitative trait loci analyze studies provided further evidence for the involvement of TFF1, CEACAM5, and SELE in colorectal cancer, with possible connections to the PI3K/ Akt and MAPK signaling pathways. By offering insights into colorectal cancer diagnostics and targeted therapeutics, our findings set the stage for further biomarker validation.  \nKEYWORDS  \ncolorectal cancer, proteins, UK Biobank, biomarkers, SHAP, translational research, machine learning, decision tree  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nGlobally, colorectal cancer (CRC) is the fourth most common cancer to be diagnosed and the third most common cause of death from cancer (1, 2) . Each year, around 250,000 cases of CRC are identified in ","cbCaic3yXThtDovD","https://ap.wps.com/l/cbCaic3yXThtDovD","pdf",2603262,1,21,"English","en",105,"# Introduction\n## Colorectal cancer burden and risk factors\n## Screening and treatment context\n## Need for proteomic biomarker discovery\n## Proteomics and molecular network challenges","[{\"question\":\"Which machine learning models were used to identify colorectal cancer proteomic biomarkers?\",\"answer\":\"The study evaluated LASSO, XGBoost, and LightGBM, with hyperparameter tuning via grid search to optimize predictive performance.\"},{\"question\":\"How did the research interpret which proteins drove model predictions?\",\"answer\":\"It used SHapley Additive exPlanations (SHAP) values to quantify each protein’s contribution to the model’s predictions.\"},{\"question\":\"What proteins were highlighted as key biomarkers in the analysis?\",\"answer\":\"SHAP-based interpretation identified TFF3, LCN2, and CEACAM5 as key biomarkers associated with cell adhesion and inflammation.\"}]","Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data | 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machine learning models were used to identify colorectal cancer proteomic biomarkers?","Question",{"text":75,"@type":76},"The study evaluated LASSO, XGBoost, and LightGBM, with hyperparameter tuning via grid search to optimize predictive performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did the research interpret which proteins drove model predictions?",{"text":80,"@type":76},"It used SHapley Additive exPlanations (SHAP) values to quantify each protein’s contribution to the model’s predictions.",{"name":82,"@type":73,"acceptedAnswer":83},"What proteins were highlighted as key biomarkers in the analysis?",{"text":84,"@type":76},"SHAP-based interpretation identified TFF3, LCN2, and CEACAM5 as key biomarkers associated with cell adhesion and 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