ArticleCancer science2022
Salivary metabolomics with machine learning for colorectal cancer detection.
Article in Cancer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- The presence and relative abundance of salivary Fusobacterium nucleatum are not associated with colorectal cancer: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Human salivary microbiome as a potential non-invasive biomarker for early-onset colorectal cancer screening: a prospective study.BMC microbiology · 2026Article
- Biomolecular profiling for noninvasive health monitoring.Nature biotechnology · 2026Review
- Systematic verification of saliva-based biosignatures for oral cancer via targeted metabolomics.Journal of food and drug analysis · 2026Article
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Navigating the molecular landscape: integrated multiomics liquid biopsy for biomarker discovery in early detection and monitoring of colorectal cancer.Frontiers in molecular biosciences · 2026Review
- Global research dynamics in metabolic biomarkers and cancer: a bibliometric study from 2015 to 2025.Discover oncology · 2025Review
- Advancements and Challenges in Salivary Metabolomics for Early Detection and Monitoring of Systemic Diseases.MedComm · 2025Review
- Can Salivary Biomarkers Serve as Diagnostic and Prognostic Tools for Early Detection in Patients with Colorectal Cancer? A Systematic Review.Current issues in molecular biology · 2025Review
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Identification of Salivary Biomarkers in Colorectal Cancer by Integrating Olink Proteomics and Metabolomics.Journal of proteome research · 2025Article
- Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients.Scientific reports · 2025Article
- An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern.Nutrition & metabolism · 2024Article
- Stage-Specific Plasma Metabolomic Profiles in Colorectal Cancer.Journal of clinical medicine · 2024Article
- Salivary metabolomic biomarkers for esophageal and gastric cancers by liquid chromatography-mass spectrometry.Cancer science · 2024Article
- Article
- Salivary metabolomic biomarkers for non-invasive lung cancer detection.Cancer science · 2024Article
- Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens.Metabolites · 2024Review
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
19 authors.
Funding
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 subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross-validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver-operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828-0.891) for CV and 0.870 (95% CI: 0.837-0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851-0.907) and 0.870 (95% CI: 0.838-0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.