Predictive microRNAs for lymph node metastasis in endoscopically resectable submucosal colorectal cancer
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Chan Kwon Jung1,*, Seung-Hyun Jung2,3,6,*, Seon-Hee Yim3, Ji-Han Jung1, Hyun Joo Choi1, Won-Kyung Kang4, Sung-Won Park3,6, Seong-Taek Oh4, Jun-Gi Kim4, Sug Hyung Lee5,6, Yeun-Jun Chung2,3
1Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
2Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
3Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
4Department of Surgery, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
5Department of Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
6Cancer Evolution Research Center, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
*These authors have contributed equally to this work
Yeun-Jun Chung, email: [email protected]
Keywords: endoscopically resectable colorectal cancer, microRNA, lymph node metastasis
Received: September 03, 2015 Accepted: March 28, 2016 Published: April 16, 2016
Accurate prediction of regional lymph node metastasis (LNM) in endoscopically resected T1-stage colorectal cancers (CRCs) can reduce unnecessary surgeries. To identify miRNA markers that can predict LNM in T1-stage CRCs, the study was conducted in two phases; (I) miRNA classifier construction by miRNA-array and quantitative reverse transcription PCR (qRT-PCR) using 36 T1-stage CRC samples; (II) miRNA classifier validation in an independent set of 20 T1-stage CRC samples. The expression of potential downstream target genes of miRNAs was assessed by immunohistochemistry. In the discovery analysis by miRNA microarray, expression of 66 miRNAs were significantly different between LNM-positive and negative CRCs. After qRT-PCR validation, 11 miRNAs were consistently significant in the combined classifier construction set. Among them, miR-342-3p was the most significant one (P=4.3×10-4). Through logistic regression analysis, we developed a three-miRNA classifier (miR-342-3p, miR-361-3p, and miR-3621) for predicting LNM in T1-stage CRCs, yielding the area under the curve of 0.947 (94% sensitivity, 85% specificity and 89% accuracy). The discriminative ability of this system was consistently reliable in the independent validation set (83% sensitivity, 64% specificity and 70% of accuracy). Of the potential downstream targets of the three-miRNAs, expressions of E2F1, RAP2B, and AKT1 were significantly associated with LNM. In conclusion, this classifier can predict LNM more accurately than conventional pathologic criteria and our study results may be helpful to avoid unnecessary bowel surgery after endoscopic resection in early CRC.
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