Objectives: Barrett's esophagus (BE) is the precursor lesion and a major risk factor for esophageal adenocarcinoma (EAC). Although patients with BE undergo routine endoscopic surveillance, current screening methodologies have proven ineffective at identifying individuals at risk of EAC. Since microRNAs (miRNAs) have potential diagnostic and prognostic value as disease biomarkers, we sought to identify an miRNA signature of BE and EAC.
Methods: High-throughput sequencing of miRNAs was performed on serum and tissue biopsies from 31 patients identified either as normal, gastroesophageal reflux disease (GERD), BE, BE with low-grade dysplasia (LGD), or EAC. Logistic regression modeling of miRNA profiles with Lasso regularization was used to identify discriminating miRNA. Quantitative reverse transcription polymerase chain reaction was used to validate changes in miRNA expression using 46 formalin-fixed, paraffin-embedded specimens obtained from normal, GERD, BE, BE with LGD or HGD, and EAC subjects.
Results: A 3-class predictive model was able to classify tissue samples into normal, GERD/BE, or LGD/EAC classes with an accuracy of 80%. Sixteen miRNAs were identified that predicted 1 of the 3 classes. Our analysis confirmed previous reports indicating that miR-29c-3p and miR-193b-5p expressions are altered in BE and EAC and identified miR-4485-5p as a novel biomarker of esophageal dysplasia. Quantitative reverse transcription polymerase chain reaction validated 11 of 16 discriminating miRNAs.
Discussion: Our data provide an miRNA signature of normal, precancerous, and cancerous tissue that may stratify patients at risk of progressing to EAC. We found that serum miRNAs have a limited ability to distinguish between disease states, thus limiting their potential utility in early disease detection.