PLoS ONE · 2017 · 101 citations · 17 references
MedicineMolecular GeneticsOncologyCancer ScreeningEpigenomicsMolecular OncologyCancer BiomarkersCancer EpigenomicsColorectal CancerEpigenetic BiomarkersMolecular ScreeningTumor MarkersBiomarkersCancer Epigenetic BiomarkersCase-control Study DesignColorectal Cancer BiomarkersColorectal Cancer ScreeningMolecular Biomarker ScreeningMultivariable Regression AnalysisNucleic Acid Biomarkers
BACKGROUND: Colorectal cancer (CRC) is one of the most common cancers in the western world. Screening is an efficient method of reducing cancer-related mortality. Molecular biomarkers for cancer in general and CRC in particular have been proposed, and hypermethylated DNA from stool or blood samples are already implemented as biomarkers for CRC screening. We aimed to evaluate the performance of proven hypermethylated DNA promoter regions as plasma based biomarkers for CRC detection. METHODS: We conducted a cross-sectional case-control study of 193 CRC patients and 102 colonoscopy-verified healthy controls. Using methylation specific polymerase chain reaction, we evaluated 30 DNA promoter regions previously found to be CRC specific. We used multivariable logistic regression with stepwise backwards selection, and subsequent leave-pair-out cross validation, to calculate the optimism corrected area under the receiver operating characteristics curve (AUC) for all stage as well as early stage CRC. RESULTS: None of the individual DNA promoter regions provided an overall sensitivity above 30% at a reasonable specificity. However, seven hypermethylated promoter regions (ALX4, BMP3, NPTX2, RARB, SDC2, SEPT9, and VIM) along with the covariates sex and age yielded an optimism corrected AUC of 0.86 for all stage CRC and 0.85 for early stage CRC. Overall sensitivity for CRC detection was 90.7% at 72.5% specificity using a cut point value of 0.5. CONCLUSIONS: Individual hypermethylated DNA promoter regions have limited value as CRC screening markers. However, a panel of seven hypermethylated promoter regions show great promise as a model for CRC detection.
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