Using saliva epigenetic data to develop and validate a multivariable predictor of esophageal cancer status

Salivary epigenetic biomarkers may detect esophageal cancer. A total of 256 saliva samples from esophageal adenocarcinoma patients and matched volunteers were analyzed with Illumina EPIC methylation arrays. Three datasets were created, using 64% for discovery, 16% for testing and 20% for validation....

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Bibliographic Details
Published in:Epigenomics 2024-01, Vol.16 (2), p.109-125
Main Authors: Stone, Timothy C, Ward, Vanessa, Hogan, Aine, Alexander Ho, Kai Man, Wilson, Ash, McBain, Hazel, Duku, Margaret, Wolfson, Paul, Cheung, Sharon, Rosenfeld, Avi, Lovat, Laurence B
Format: Article
Language:English
Subjects:
biomarker panel
diagnosis
epigenetics
esophageal adenocarcinoma
saliva
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Summary:Salivary epigenetic biomarkers may detect esophageal cancer. A total of 256 saliva samples from esophageal adenocarcinoma patients and matched volunteers were analyzed with Illumina EPIC methylation arrays. Three datasets were created, using 64% for discovery, 16% for testing and 20% for validation. Modules of gene-based methylation probes were created using weighted gene coexpression network analysis. Module significance to disease and gene importance to module were determined and a random forest classifier generated using best-scoring gene-related epigenetic probes. A cost-sensitive wrapper algorithm maximized cancer diagnosis. Using age, sex and seven probes, esophageal adenocarcinoma was detected with area under the curve of 0.72 in discovery, 0.73 in testing and 0.75 in validation datasets. Cancer sensitivity was 88% with specificity of 31%. We have demonstrated a potentially clinically viable classifier of esophageal cancer based on saliva methylation.
ISSN:1750-1911
1750-192X
1750-192X
DOI:10.2217/epi-2023-0248