Abstract
Salivary mRNA biomarkers and serum carcinoembryonic antigen (CEA) have been recognized as promising liquid biopsy methods for detection of multiple cancers. However, current tests normally use solitary type of biomarkers, and are limited by unsatisfactory sensitivity and specificity when applied to differentiate cancer patients from healthy controls. In this study, a combined approach of CEA and salivary mRNA biomarkers was evaluated for discriminatory performance of ovarian cancer patients from healthy controls. We designed our study with two phases: a discovery phase to find and evaluate multiple biomarkers, and an independent validation phase to confirm the applicability of the selected biomarkers. In the discovery phase, a total of 140 ovarian cancer patients and 140 healthy controls were recruited. The CEA level in blood as well as five mRNA biomarkers in saliva (i.e. AGPAT1, B2M, BASP1, IER3 and IL1β) were measured, followed by developing a machine-learning model to differentiate ovarian cancer patients and healthy controls. We found a novel panel of biomarkers, which could differentiate ovarian cancer patients from healthy controls with high sensitivity (89.3%) and high specificity (82.9%). Next, we applied this panel of biomarkers in an independent validation study that consisted of 60 ovarian cancer patients and 60 healthy controls. The ovarian cancer patients were successfully differentiated from healthy controls in the validation phase, with sensitivity reaching 85.0% and specificity reaching 88.3%. To our best knowledge, it is the first time that a combined use of CEA and salivary mRNA biomarkers were applied for non-invasive detection of ovarian cancer.
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More From: Scandinavian Journal of Clinical and Laboratory Investigation
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