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Robust analysis of novel mRNA–lncRNA cross talk based on ceRNA hypothesis uncovers carcinogenic mechanism and promotes diagnostic accuracy in esophageal cancer

Authors Chen LP, Wang H, Zhang Y, Chen QX, Lin TS, Liu ZQ, Zhou YY

Received 9 August 2018

Accepted for publication 21 November 2018

Published 27 December 2018 Volume 2019:11 Pages 347—358


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Professor Nakshatri

Li-Ping Chen,1,2 Hong Wang,1 Yi Zhang,2 Qiu-Xiang Chen,3 Tie-Su Lin,4 Zong-Qin Liu,5 Yang-Yang Zhou1

1Department of Rheumatology and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; 2Chemical Biology Research Center, Department of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; 3Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; 4Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; 5Department of Laboratory Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China

Background: ceRNAs have emerged as pivotal players in the regulation of gene expression and play a crucial role in the physiology and development of various cancers. Nevertheless, the function and underlying mechanisms of ceRNAs in esophageal cancer (EC) are still largely unknown.
Methods: In this study, profiles of DEmRNAs, DElncRNAs, and DEmiRNAs between normal and EC tumor tissue samples were obtained from the Cancer Genome Atlas database using the DESeq package in R by setting the adjusted P<0.05 and |log2(fold change)|>2 as the cutoff. The ceRNA network (ceRNet) was initially constructed to reveal the interaction of these ceRNAs during carcinogenesis based on the bioinformatics of miRcode, miRDB, miRTarBase, and TargetScan. Then, independent microarray data of GSE6188, GSE89102, and GSE92396 and correlation analysis were used to validate molecular biomarkers in the initial ceRNet. Finally, a least absolute shrinkage and selection operator logistic regression model was built using an oncogenic ceRNet to diagnose EC more accurately.
Results: We successfully constructed an oncogenic ceRNet of EC, crosstalk of hsa-miR372-centered CADM2-ADAMTS9-AS2 and hsa-miR145-centered SERPINE1-PVT1. In addition, the risk-score model −0.0053*log2(CADM2)+0.0168*log2(SERPINE1)-0.0073*log2(ADAMTS9-AS2)+0.0905*log2(PVT1)+0.0047*log2(hsa-miR372)–0.0193*log2(hsa-miR145), (log2[gene count]) could improve diagnosis of EC with an AUC of 0.988.
Conclusion: We identified two novel pairs of ceRNAs in EC and its role of diagnosis. The pairs of hsa-miR372-centered CADM2-ADAMTS9-AS2 and hsa-miR145-centered SERPINE1-PVT1 were likely potential carcinogenic mechanisms of EC, and their joint detection could improve diagnostic accuracy.

Keywords: competitive endogenous RNA network, esophagus cancer, LASSO regression model, diagnosis

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