Research Papers:
MiR-3613-3p affects cell proliferation and cell cycle in hepatocellular carcinoma
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Abstract
Donghui Zhang1, Enqin Liu1, Jian Kang2, Xin Yang3 and Hong Liu1
1Department of Infectious Disease, Linyi People’s Hospital, Linyi 276000, China
2Department of Colorectal Surgery, Tai’an City Central Hospital, Tai’an 271000, China
3Culverhouse College of Commerce and Business Administration, The University of Alabama, Tuscaloosa, AL 35401, USA
Correspondence to:
Hong Liu, email: [email protected]
Keywords: cell cycle, cell proliferation, hepatocellular carcinoma, hsa-miR-3613-3p
Received: May 04, 2017 Accepted: August 23, 2017 Published: October 10, 2017
ABSTRACT
Hepatocellular carcinoma (HCC) is one of the most common types of malignant tumors with poor sensitivity to chemotherapy drugs and poor prognosis among patients. In the present study, we downloaded the original data from the Gene Expression Omnibus and compared gene expression profiles of liver cancer cells in patients with HCC with those of colon epithelial cells of healthy controls to identify differentially expressed genes (DEGs). After filtering target microRNAs (miRNA) from core DEGs, we cultured HepG2 cells in vitro, knocked down the miRNA and core mRNAs, and analyzed the effects. We found 228 differentially expressed genes between liver cancer tissue and healthy control tissue. We also integrated the protein-proteininteraction network and module analysis to screen 13 core genes, consisting of 12 up-regulated genes and 1 down-regulated gene. Five core genes were regulated hsa-miR-3613-3p, therefor we hypothesized that hsa-miR-3613-3p was a critical miRNA. After the transfection procedure, we found that changes in hsa-miR-3613-3p were the most obvious. Therefore, we speculated that hsa-miR-3613-3p was a main target miRNA. In addition, we transfected with si (BIRC5, CDK1, NUF2, ZWINT and SPC24), to target genes that can be targeted by miR-3613-3p. Our data shows that BIRC5, NUF2, and SPC24 may be promising liver cancer biomarkers that may not only predict disease occurrence but also potential personalized treatment options.
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PII: 21745