{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kuwabara H"],"funding":["JSPS KAKENHI","Yamagata Prefectural Government and the City of Tsuruoka"],"pagination":["3234-3243"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9459332"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["113(9)"],"pubmed_abstract":["As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva-based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry. An alternative decision tree (ADTree)-based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjec"],"journal":["Cancer science"],"pubmed_title":["Salivary metabolomics with machine learning for colorectal cancer detection."],"pmcid":["PMC9459332"],"funding_grant_id":["16H05408","21K07228","16K10554","20H05743","15K08751","26462027"],"pubmed_authors":["Soya R","Ota S","Sunamura M","Soga T","Tomita M","Katsumata K","Enomoto M","Ishizaki T","Mazaki J","Udo R","Kasahara K","Nagakawa Y","Kuwabara H","Kaneko M","Tago T","Tsuchida A","Enomoto A","Sugimoto M","Iwabuchi A"],"additional_accession":[]},"is_claimable":false,"name":"Salivary metabolomics with machine learning for colorectal cancer detection.","description":"As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva-based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry. An alternative decision tree (ADTree)-based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjec","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2025-04-22T04:09:07.046Z","creation":"2025-04-05T20:54:40.239Z"},"accession":"S-EPMC9459332","cross_references":{"pubmed":["35754317"],"doi":["10.1111/cas.15472"]}}