File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Machine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis

TitleMachine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis
Authors
Keywordsbiomarkers
diagnosis
DNA methylation
epigenomics
oral cancer
oral potentially malignant disorders
Issue Date8-Oct-2022
PublisherMDPI
Citation
Cancers, 2022, v. 14, n. 19 How to Cite?
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.


Persistent Identifierhttp://hdl.handle.net/10722/329178
ISSN
2023 Impact Factor: 4.5
2023 SCImago Journal Rankings: 1.391
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorAdeoye, John-
dc.contributor.authorWan, Chi Ching Joan-
dc.contributor.authorZheng, Li Wu-
dc.contributor.authorThomson, Peter-
dc.contributor.authorChoi, Siu Wai-
dc.contributor.authorSu, Yu Xiong-
dc.date.accessioned2023-08-05T07:55:52Z-
dc.date.available2023-08-05T07:55:52Z-
dc.date.issued2022-10-08-
dc.identifier.citationCancers, 2022, v. 14, n. 19-
dc.identifier.issn2072-6694-
dc.identifier.urihttp://hdl.handle.net/10722/329178-
dc.description.abstract<p>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.</p>-
dc.languageeng-
dc.publisherMDPI-
dc.relation.ispartofCancers-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectbiomarkers-
dc.subjectdiagnosis-
dc.subjectDNA methylation-
dc.subjectepigenomics-
dc.subjectoral cancer-
dc.subjectoral potentially malignant disorders-
dc.titleMachine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis-
dc.typeArticle-
dc.identifier.doi10.3390/cancers14194935-
dc.identifier.scopuseid_2-s2.0-85139765494-
dc.identifier.volume14-
dc.identifier.issue19-
dc.identifier.eissn2072-6694-
dc.identifier.isiWOS:000868128700001-
dc.identifier.issnl2072-6694-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats