ArticleCancers2022
Machine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- DNA methylation markers for oral cancer detection in non- and minimally invasive samples: a systematic review.Clinical epigenetics · 2024Pooled it
- Emerging Diagnostic Strategies for Oral Cancer and Oral Potentially Malignant Disorders: A PRISMA-Guided Scoping Review.Diagnostics (Basel, Switzerland) · 2026Review
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Salivary biomarkers in oral cancer diagnosis: advancing conventional treatment strategies.Military Medical Research · 2026Review
- Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- A Deep Learning System to Predict Epithelial Dysplasia in Oral Leukoplakia.Journal of dental research · 2024Article
- A brief review of cytology in dentistry.British dental journal · 2024Review
- Brain Tumor Classification by Methylation Profile.Journal of Korean medical science · 2023Review
- Article
- Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer.Frontiers in oncology · 2023Review
- DNA methylation biomarkers as an early diagnostic tool in the progression of oral squamous cell carcinoma.Journal of oral and maxillofacial pathology : JOMFPArticle
- Visualization of artificial intelligence applications in oral disease diagnosis: A bibliometric analysis.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing. Reads with a minimum of 10× coverage were used to identify differentially methylated CpG sites (DMCs) and 100 bp regions (DMRs). The performance of eight ML models and three feature-selection methods (ANOVA, MRMR, and LASSO) were then compared to determine the optimal biomarker models based on DMCs and DMRs. A total of 1745 DMCs and 105 DMRs were identified for detecting OSCC. The proportion of hypomethylated and hypermethylated DMCs was similar (51% vs. 49%), while most DMRs were hypermethylated (62.9%). Furthermore, more DMRs than DMCs were annotated to promoter regions (36% vs. 16%) and more DMCs than DMRs were annotated to intergenic regions (50% vs. 36%). Of all the ML models compared, the linear SVM model based on 11 optimal DMRs selected by LASSO had a perfect AUC, recall, specificity, and calibration (1.00) for OSCC detection. Overall, genome-wide DNA methylation techniques can be applied directly to saliva samples for biomarker discovery and ML-based platforms may be useful in stratifying OSCC during disease screening and monitoring.
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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.