Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm

Ruan,Yan; Li,Yuan; Liu,Yingping; Zhou,Jianxin; Wang,Xin; Zhang,Weiyuan

doi:10.3892/etm.2019.7410

Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm

Authors:
- Yan Ruan
- Yuan Li
- Yingping Liu
- Jianxin Zhou
- Xin Wang
- Weiyuan Zhang
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Affiliations: Department of Obstetrics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, P.R. China
Published online on: March 18, 2019 https://doi.org/10.3892/etm.2019.7410
Pages: 4139-4143

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Abstract

This study investigated optimal pathways for preeclampsia (PE) utilizing the network-based guilt by association (GBA) algorithm. The inference method consisted of four steps: preparing differentially expressed genes (DEGs) between PE patients and normal controls from gene expression data; constructing co-expression network (CEN) for DEGs utilizing Spearman's correlation coefficient (SCC) method; and predicting optimal pathways by network-based GBA algorithm of which the area under the receiver operating characteristics curve (AUROC) was gained for each pathway. There were 351 DEGs and 61,425 edges in the CEN for PE. Subsequently, 53 pathways were obtained with a good classification performance (AUROC >0.5). AUROC for 9 was >0.9 and defined as optimal pathways, especially microRNAs in cancer (AUROC=0.9966), gap junction (AUROC=0.9922), and pathogenic Escherichia coli infection (AUROC=0.9888). Nine optimal pathways were identified through comprehensive analysis of data from PE patients, which might shed new light on uncovering molecular and pathological mechanism of PE.

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1	Maynard SE and Karumanchi SA: Angiogenic factors and preeclampsia. Semin Nephrol. 31:33–46. 2011. View Article : Google Scholar : PubMed/NCBI
2	Steegers EA, von Dadelszen P, Duvekot JJ and Pijnenborg R: Pre-eclampsia. Lancet. 376:631–644. 2010. View Article : Google Scholar : PubMed/NCBI
3	Glazko GV and Emmert-Streib F: Unite and conquer: Univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics. 25:2348–2354. 2009. View Article : Google Scholar : PubMed/NCBI
4	Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B and Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31:e15. 2003. View Article : Google Scholar : PubMed/NCBI
5	Bolstad BM, Irizarry RA, Astrand M and Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 19:185–193. 2003. View Article : Google Scholar : PubMed/NCBI
6	Miller JA, Menon V, Goldy J, Kaykas A, Lee CK, Smith KA, Shen EH, Phillips JW, Lein ES and Hawrylycz MJ: Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq. BMC Genomics. 15:1542014. View Article : Google Scholar : PubMed/NCBI
7	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
8	Datta S, Satten GA, Benos DJ, Xia J, Heslin MJ and Datta S: An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments. Bioinformatics. 20:235–242. 2004. View Article : Google Scholar : PubMed/NCBI
9	Reiner A, Yekutieli D and Benjamini Y: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 19:368–375. 2003. View Article : Google Scholar : PubMed/NCBI
10	Szmidt E and Kacprzyk J: The Spearman rank correlation coefficient between intuitionistic fuzzy sets. IEEE International Conference on Intelligent Systems, Is 2010, 7–9 July 2010. University of Westminster. (London, UK). 276–280. 2010.
11	Qiu Y-Q: KEGG Pathway Database. Encyclopedia of Systems Biology. Dubitzky W, Wolkenhauer O, Cho K-H and Yokota H: Springer New York; New York, NY: pp. 1068–1069. 2013, View Article : Google Scholar
12	Mostafavi S and Morris Q: Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics. 26:1759–1765. 2010. View Article : Google Scholar : PubMed/NCBI
13	Gillis J and Pavlidis P: The impact of multifunctional genes on ‘guilt by association’ analysis. PLoS One. 6:e172582011. View Article : Google Scholar : PubMed/NCBI
14	Huang J and Ling CX: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng. 17:299–310. 2005. View Article : Google Scholar
15	Laird DW: The gap junction proteome and its relationship to disease. Trends Cell Biol. 20:92–101. 2010. View Article : Google Scholar : PubMed/NCBI
16	Bird IM, Boeldt DS, Krupp J, Grummer MA, Yi FX and Magness RR: Pregnancy, programming and preeclampsia: Gap junctions at the nexus of pregnancy-induced adaptation of endothelial function and endothelial adaptive failure in PE. Curr Vasc Pharmacol. 11:712–729. 2013. View Article : Google Scholar : PubMed/NCBI
17	Ampey BC, Morschauser TJ, Lampe PD and Magness RR: Gap junction regulation of vascular tone: Implications of modulatory intercellular communication during gestation. Adv Exp Med Biol. 814:117–132. 2014. View Article : Google Scholar : PubMed/NCBI
18	Ishibashi O, Ohkuchi A, Ali MM, Kurashina R, Luo SS, Ishikawa T, Takizawa T, Hirashima C, Takahashi K, Migita M, et al: Hydroxysteroid (17-β) dehydrogenase 1 is dysregulated by miR-210 and miR-518c that are aberrantly expressed in preeclamptic placentas: A novel marker for predicting preeclampsia. Hypertension. 59:265–273. 2012. View Article : Google Scholar : PubMed/NCBI
19	Ohkuchi A, Ishibashi O, Hirashima C, Takahashi K, Matsubara S, Takizawa T and Suzuki M: Plasma level of hydroxysteroid (17-β) dehydrogenase 1 in the second trimester is an independent risk factor for predicting preeclampsia after adjusting for the effects of mean blood pressure, bilateral notching and plasma level of soluble fms-like tyrosine kinase 1/placental growth factor ratio. Hypertens Res. 35:1152–1158. 2012. View Article : Google Scholar : PubMed/NCBI
20	Li FH, Han N, Wang Y and Xu Q: Gadd45a knockdown alleviates oxidative stress through suppressing the p38 MAPK signaling pathway in the pathogenesis of preeclampsia. Placenta. 65:20–28. 2018. View Article : Google Scholar : PubMed/NCBI
21	Jiang J and Zhao ZM: LncRNA HOXD-AS1 promotes preeclampsia progression via MAPK pathway. Eur Rev Med Pharmacol Sci. 22:8561–8568. 2018.PubMed/NCBI
22	D'Oria R, Laviola L, Giorgino F, Unfer V, Bettocchi S and Scioscia M: PKB/Akt and MAPK/ERK phosphorylation is highly induced by inositols: Novel potential insights in endothelial dysfunction in preeclampsia. Pregnancy Hypertens. 10:107–112. 2017. View Article : Google Scholar : PubMed/NCBI