Potential and functional prediction of six circular RNAs as diagnostic markers for colorectal cancer
Li yuan Liu,
Dan Jiang,
Yuliang Qu,
Hongxia Wang,
Yanting Zhang,
Shaoqi Yang,
Xiaoliang Xie,
Shan Wu,
Haijin Zhou,
Guangxian Xu
Affiliations
Li yuan Liu
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Dan Jiang
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Yuliang Qu
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Hongxia Wang
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Yanting Zhang
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Shaoqi Yang
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Xiaoliang Xie
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Shan Wu
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Haijin Zhou
Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, School of Medical Technology, Institute of Clinical Laboratory, Guangdong Medical University, Dongguan, Guangdong, China
Guangxian Xu
School of Clinical Medicine, The Third Affiliated Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, China
Background Circular RNAs (circRNAs) have been discovered in colorectal cancer (CRC), but there are few reports on the expression distribution and functional mining analysis of circRNAs. Methods Differentially expressed circRNAs in CRC tissues and adjacent normal tissues were screened and identified by microarray and qRT-PCR. ROC curves of the six circRNAs were analyzed. A series of bioinformatics analyses on differentially expressed circRNAs were performed. Results A total of 207 up-regulated and 357 down-regulated circRNAs in CRC were screened, and three top up-regulated and down-regulated circRNAs were chosen to be verified in 33 pairs of CRCs by qRT-PCR. 6 circRNAs showed high diagnostic values (AUC = 0.6860, AUC = 0.8127, AUC = 0.7502, AUC = 0.9945, AUC = 0.9642, AUC = 0.9486 for hsa_circRNA_100833, hsa_circRNA_103828, hsa_circRNA_103831 and hsa_circRNA_103752, hsa_circRNA_071106, hsa_circRNA_102293). A circRNA-miRNA-mRNA regulatory network (cirReNET) including six candidate circRNAs, 19 miRNAs and 210 mRNA was constructed, and the functions of the cirReNET were predicted and displayed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on these mRNAs and protein-protein interaction (PPI) network of the hub genes acquired by string and CytoHubba. Conclusion A cirReNET containing potential diagnostic and predictive indicators of CRCs and several critical circRNA-miRNA-mRNA regulatory axes (cirReAXEs) in CRC were mined, and may provide a novel route to study the mechanism and clinical targets of CRC.
