[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125039-en":3,"doc-seo-125039-105":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":4,"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},125039,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Serum Metabolomic Profiling for Colorectal Cancer using Machine Learning - Article 2","Colorectal cancer ranks among the deadliest diseases worldwide and is often preceded by adenomatous polyps in the colon mucosa. This study evaluates serum metabolites as promising non-invasive biomarkers to support colorectal cancer detection and prognostication. In silico analysis and validation use machine learning on a metabolite dataset from Metabolomic Workbench. Among 234 samples, 113 metabolites were found, and five showed the strongest group differentiation. Pathway analysis links aspartic acid and histidine to colorectal cancer progression and cancer-related phenotypes, supporting biomarker selection while noting needs for refinement.","Indonesian Journal of Medical Chemistry and Bioinformatics  \n\n| Volume 2  Number 1 | Article 2 |\n| --- | --- |\n| 7-28-2023\u003Cbr>Serum Metabolomic Profiling for Colorectal Cancer using Machine Learning\u003Cbr>Ria Nur Puspa Sari\u003Cbr>Universitas Indonesia, Jakarta, [ria.nur@ui.ac.id](ria.nur@ui.ac.id)\u003Cbr>Diah Balqis Ikfi Hidayati\u003Cbr>Indonesia’s Internship Doctor Programme in Malingping General Hospital, Lebak, Banten, [diahbalqis@gmail.com](diahbalqis@gmail.com)\u003Cbr>Arleni Bustami\u003Cbr>Universitas Indonesia, [arleni.ab@gmail.com](arleni.ab@gmail.com)\u003Cbr>Follow this and additional works at: [https://scholarhub.ui.ac.id/ijmcb](https://scholarhub.ui.ac.id/ijmcb)\u003Cbr> Part of the Alternative and Complementary Medicine Commons, Bioinformatics Commons, and the Biomedical Engineering and Bioengineering Commons |  |\n\nRecommended Citation  \nSari, Ria Nur Puspa; Hidayati, Diah Balqis Ikfi; and Bustami, Arleni (2023) \"Serum Metabolomic Profiling for Colorectal Cancer using Machine Learning,\" Indonesian Journal of Medical Chemistry and Bioinformatics: Vol. 2: No. 1, Article 2.  \nDOI: 10.7454/ijmcb.v2i1 .1021  \nAvailable at: [https://scholarhub.ui.ac.id/ijmcb/vol2/iss1/2](https://scholarhub.ui.ac.id/ijmcb/vol2/iss1/2)  \nThis Article is brought to you for free and open access by the Faculty of Medicine at UI Scholars Hub. It has been accepted for inclusion in Indonesian Journal of Medical Chemistry and Bioinformatics by an authorized editor of UI Scholars Hub.  \nArticle  \nSerum Metabolomic Profiling for Colorectal Cancer using Machine Learning  \nRia Nur Puspa Sari 1*, Diah Balqis Ikfi Hidayati 2, Arleni 3  \nCitation: Sari, R.N.P; Hidayati,  \nD.B.I; Arleni. Serum Metabolomic Profiling for Colorectal Cancer using Machine Learning. Ind. J. Med. Chem. Bio. IJMCB. 2023, 2, 1.  \nReceived: Wed Jun 07, 2023  \nAccepted: Fri Jul 28, 2023  \nPublished: Fri Jul 28, 2023  \nPublisher’s Note: IJMCB stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit [http://creativecommons.org/licenses/](http://creativecommons.org/licenses/)[ ](http://creativecommons.org/licenses/)[by/4.0/ or send a letter to Creative](by/4.0/ or send a letter to Creative)[ ](by/4.0/ or send a letter to Creative)Commons, PO Box 1866, Mountain View, CA 94042, USA.  \n1 Master’s Programme in Biomedical Science, Faculty of Medicine, Universitas Indonesia, Jakarta 10430, Indonesia  \n2 Indonesia’s Internship Doctor Programme in Malingping General Hospital, Lebak, Banten, 42391, Indonesia  \n3 Undergraduate Programme, Faculty Medicine, Universitas Indonesia, Jakarta 10430, Indonesia  \n* Correspondence: [ria.nur@ui.ac.id](ria.nur@ui.ac.id)  \nAbstract: Introduction: Colorectal cancer is one of the deadliest diseases with a high prevalence worldwide and is characterized by the appearance of adenomatous polyps in the colon mucosa which are at high risk of developing into colorectal cancer. This study aimstouse serum metabolites as promising non-invasive biomarkers for colorectal cancer detection and prognostication. Differences in serum metabolites in patients with adenomatous polyps, colorectal cancer, and healthy controls are considered tobe able to support the prognosis of colorectal cancer. Methods: Metabolite dataset is taken from the Metabolomic Workbench. Analysis and validation are carried out in silico using machine learning methods. Results: From a total of 234 samples, 113 metabolites were found and 5 metabolites; histidine, lysine, glyceraldehyde, linolenic acid, and aspartic acid were identified as the most significant in differentiating the sample groups. CTD analysis showed that aspartic acid and histidine are associated with the biological pathways of colorectal cancer progression and significant metabolites are associated with cancer-related phenotypes. Conclusion: The serum metabolites differ in colorectal cancer","cbCaidpY4GktkSE7","https://ap.wps.com/l/cbCaidpY4GktkSE7","pdf",834879,1,11,"English","en",105,"# Abstract\n## Introduction\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What is the study’s main goal for colorectal cancer?\",\"answer\":\"To use serum metabolites as non-invasive biomarkers that can support colorectal cancer detection and prognostication.\"},{\"question\":\"How were metabolite data analyzed in the study?\",\"answer\":\"A metabolite dataset from Metabolomic Workbench was analyzed and validated in silico using machine learning methods.\"},{\"question\":\"Which metabolites were identified as most significant?\",\"answer\":\"Histidine, lysine, glyceraldehyde, linolenic acid, and aspartic acid were identified as the most significant for differentiating sample groups.\"}]","Serum Metabolomic Profiling for Colorectal Cancer using Machine Learning - Article 2 | PDF",1785896302,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"serum-metabolomic-profiling-for-colorectal-cancer-using-machine-learning-article-2","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/serum-metabolomic-profiling-for-colorectal-cancer-using-machine-learning-article-2/125039/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the study’s main goal for colorectal cancer?","Question",{"text":75,"@type":76},"To use serum metabolites as non-invasive biomarkers that can support colorectal cancer detection and prognostication.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were metabolite data analyzed in the study?",{"text":80,"@type":76},"A metabolite dataset from Metabolomic Workbench was analyzed and validated in silico using machine learning methods.",{"name":82,"@type":73,"acceptedAnswer":83},"Which metabolites were identified as most significant?",{"text":84,"@type":76},"Histidine, lysine, glyceraldehyde, linolenic acid, and aspartic acid were identified as the most significant for differentiating sample groups.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]