Predicting the target genes of miRNAs in preterm via targetscore algorithm

Lu,Kexin; Huang,Junzhi; Yang,Yandong; Lu,Dongli

doi:10.3892/etm.2019.7179

Predicting the target genes of miRNAs in preterm via targetscore algorithm

Authors:
- Kexin Lu
- Junzhi Huang
- Yandong Yang
- Dongli Lu
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Affiliations: Department of Obstetrics and Gynecology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, P.R. China, Department of Neurology, Binzhou Medical University Hospital, Binzhou, Shandong 256603, P.R. China
Published online on: January 16, 2019 https://doi.org/10.3892/etm.2019.7179
Pages: 2085-2090
Copyright: © Lu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Compared with normal neonates, preterm infants have an immature immune system which causes them to have a higher morbidity rate and even death. In order to reduce the mortality of newborns, we need to find the target genes which affect the preterm and understand their mechanism. It has been verified that microRNA (miRNA)‑200 and miRNA‑182 are closely related to the incidence of preterm. Therefore, it is significant to predict the target genes which are regulated by them for further understanding the mechanism of preterm. We chose the targetscore method for calculating the variational Bayesian‑Gaussian mixture model (VB‑GMM) as the target genes prediction method. It is designed for condition‑specific target predictions and not limited to predict conserved genes, so the results are more accurate than previous sequence‑based target prediction algorithms. In this study, our major contribution is to predict the target mRNAs of the chosen miRNAs with the gene expression profiles and a new method, which can effectively improve the accuracy of the prediction.

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1	Gray C, McCowan LM, Patel R, Taylor RS and Vickers MH: Maternal plasma miRNAs as biomarkers during mid-pregnancy to predict later spontaneous preterm birth: A pilot study. Sci Rep. 7:8152017. View Article : Google Scholar : PubMed/NCBI
2	Peixoto AB, da Cunha Caldas TMR, Tahan LA, Petrini CG, Martins WP, Costa FDS and Araujo Júnior E: Second trimester cervical length measurement for prediction spontaneous preterm birth in an unselected risk population. Obstet Gynecol Sci. 60:329–335. 2017. View Article : Google Scholar : PubMed/NCBI
3	Kalikstad B, Kultima HG, Andersstuen TK, Klungland A and Isaksson A: Gene expression profiles in preterm infants on continuous long-term oxygen therapy suggest reduced oxidative stress-dependent signaling during hypoxia. Mol Med Rep. 15:1513–1526. 2017. View Article : Google Scholar : PubMed/NCBI
4	Chen Y, Song Y, Huang J, Qu M, Zhang Y, Geng J, Zhang Z, Liu J and Yang GY: Increased circulating exosomal miRNA-223 is associated with acute ischemic stroke. Front Neurol. 8:572017. View Article : Google Scholar : PubMed/NCBI
5	Dutta S, Singh S, Bhattacharya A, Venkataseshan S and Kumar P: Relation of thyroid hormone levels with fluid-resistant shock among preterm septicemic neonates. Indian Pediatr. 54:121–124. 2017. View Article : Google Scholar : PubMed/NCBI
6	Bukowski R, Sadovsky Y, Goodarzi H, Zhang H, Biggio JR, Varner M, Parry S, Xiao F, Esplin SM, Andrews W, et al: Onset of human preterm and term birth is related to unique inflammatory transcriptome profiles at the maternal fetal interface. PeerJ. 5:e36852017. View Article : Google Scholar : PubMed/NCBI
7	Anton L, DeVine A, Sierra LJ, Brown AG and Elovitz MA: miR-143 and miR-145 disrupt the cervical epithelial barrier through dysregulation of cell adhesion, apoptosis and proliferation. Sci Rep. 7:30202017. View Article : Google Scholar : PubMed/NCBI
8	Sanders AP, Burris HH, Just AC, Motta V, Svensson K, Mercado-Garcia A, Pantic I, Schwartz J, Tellez-Rojo MM, Wright RO, et al: microRNA expression in the cervix during pregnancy is associated with length of gestation. Epigenetics. 10:221–228. 2015. View Article : Google Scholar : PubMed/NCBI
9	Brunquell J, Snyder A, Cheng F and Westerheide SD: HSF-1 is a regulator of miRNA expression in Caenorhabditis elegans. PLoS One. 12:e01834452017. View Article : Google Scholar : PubMed/NCBI
10	Mayor-Lynn K, Toloubeydokhti T, Cruz AC and Chegini N: Expression profile of microRNAs and mRNAs in human placentas from pregnancies complicated by preeclampsia and preterm labor. Reprod Sci. 18:46–56. 2011. View Article : Google Scholar : PubMed/NCBI
11	Williams KC, Renthal NE, Condon JC, Gerard RD and Mendelson CR: MicroRNA-200a serves a key role in the decline of progesterone receptor function leading to term and preterm labor. Proc Natl Acad Sci USA. 109:7529–7534. 2012. View Article : Google Scholar : PubMed/NCBI
12	Pineles BL, Romero R, Montenegro D, Tarca AL, Han YM, Kim YM, Draghici S, Espinoza J, Kusanovic JP, Mittal P, et al: Distinct subsets of microRNAs are expressed differentially in the human placentas of patients with preeclampsia. Am J Obstet Gynecol. 196:261.e1–261.e6. 2007. View Article : Google Scholar
13	L'Yi S, Jung D, Oh M, Kim B, Freishtat RJ, Giri M, Hoffman E and Seo J: miRTarVis+: Web-based interactive visual analytics tool for microRNA target predictions. Methods. 124:78–88. 2017. View Article : Google Scholar : PubMed/NCBI
14	Nam JW, Rissland O, Koppstein D, Abreu-Goodger C, Jan CH, Agarwal V, Yildirim MA, Rodriguez A and Bartel DP: Global analyses of the effect of different cellular contexts on microRNA targeting. Molecular Cell. 53:1031–1043. 2014. View Article : Google Scholar : PubMed/NCBI
15	Li Y, Goldenberg A, Wong KC and Zhang Z: A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information. Bioinformatics. 30:621–628. 2014. View Article : Google Scholar : PubMed/NCBI
16	Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI
17	Winger EE, Reed JL and Ji X: Early first trimester peripheral blood cell microRNA predicts risk of preterm delivery in pregnant women: Proof of concept. PLoS One. 12:e01801242017. View Article : Google Scholar : PubMed/NCBI
18	Zhu Q, Chen Y, Dai J, Wang B, Liu M, Wang Y, Tao J and Li H: Methylenetetrahydrofolate reductase polymorphisms at 3′-untranslated region are associated with susceptibility to preterm birth. Transl Pediatr. 4:57–62. 2015.PubMed/NCBI
19	Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP and Burge CB: Prediction of mammalian microRNA targets. Cell. 115:787–798. 2003. View Article : Google Scholar : PubMed/NCBI
20	Salehi Z and Akrami H: Target genes prediction and functional analysis of microRNAs differentially expressed in gastric cancer stem cells MKN-45. J Cancer Res Ther. 13:477–483. 2017.PubMed/NCBI
21	Zhao B and Xue B: Consensus datasets of mouse miRNA-mRNA interactions from multiple online resources. Data Brief. 14:143–147. 2017. View Article : Google Scholar : PubMed/NCBI
22	Shi Y, Yang F, Wei S and Xu G: Identification of key genes affecting results of hyperthermia in osteosarcoma based on integrative ChIP-Seq/TargetScan analysis. Med Sci Monit. 23:2042–2048. 2017. View Article : Google Scholar : PubMed/NCBI
23	Mees ST, Mardin WA, Wendel C, Baeumer N, Willscher E, Senninger N, Schleicher C, Colombo-Benkmann M and Haier J: EP300 - a miRNA-regulated metastasis suppressor gene in ductal adenocarcinomas of the pancreas. Int J Cancer. 126:114–124. 2010. View Article : Google Scholar : PubMed/NCBI