Integrated microarray analysis of key genes and a miRNA‑mRNA regulatory network of early‑onset preeclampsia

Zhang,Hao; Xue,Lu; Lv,Yan; Yu,Xiang; Zheng,Yiwei; Miao,Zhijing; Ding,Hongjuan

doi:10.3892/mmr.2020.11551

Integrated microarray analysis of key genes and a miRNA‑mRNA regulatory network of early‑onset preeclampsia

Authors:
- Hao Zhang
- Lu Xue
- Yan Lv
- Xiang Yu
- Yiwei Zheng
- Zhijing Miao
- Hongjuan Ding
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Affiliations: Department of Internal Medicine, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu 210008, P.R. China, Department of Obstetrics and Gynecology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, Jiangsu 210004, P.R. China
Published online on: September 30, 2020 https://doi.org/10.3892/mmr.2020.11551
Pages: 4772-4782
Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Early‑onset preeclampsia (EOPE) is a serious threat to maternal and foetal health. The present study aimed to identify potential biomarkers and targets for the treatment of EOPE. Expression profiles of placenta from patients with EOPE and healthy controls (GSE103542, GSE74341 and GSE44711) were downloaded from the Gene Expression Omnibus database. Integrated analysis revealed 246 genes and 28 microRNAs (miRNAs) that were differentially expressed between patients with EOPE and healthy controls. Differentially expressed genes (DEGs) were primarily enriched in ‘biological processes’, such as ‘cell adhesion’, ‘female pregnancy’, ‘extracellular matrix organization’ and ‘response to hypoxia’. Significant pathways associated with DEGs primarily included ‘focal adhesion’, ‘ECM‑receptor interaction’, ‘PI3K‑Akt signaling’ and ‘ovarian steroidogenesis’. A Protein‑Protein Interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins online database, and epidermal growth factor receptor, collagen α‑1(I) chain, secreted phosphoprotein 1, leptin (LEP), collagen α‑2(I) chain (COL1A2), plasminogen activator inhibitor 1 (SERPINE1), Thy‑1 membrane glycoprotein, bone morphogenetic protein 4, vascular cell adhesion protein 1 and matrix metallopeptidase 1 were identified as hub genes. The alterations of hsa‑miR‑937, hsa‑miR‑148b*, hsa‑miR‑3907, hsa‑miR‑367*, COL1A2, LEP and SERPINE1 in placenta were validated using our local samples. Our research showed that the expression of hsa‑miR‑937, hsa‑miR‑1486*, hsa‑miR‑3907, hsa‑miR‑367* and hub genes in the placenta were closely associated with the pathophysiology of EOPE. hsa‑miR‑937, hsa‑miR‑1486*, hsa‑miR‑3907, hsa‑miR‑367* and hub genes could serve as biomarkers for diagnosis and as potential targets for the treatment of EOPE.

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1	Steegers EA, von Dadelszen P, Duvekot JJ and Pijnenborg R: Pre-eclampsia. Lancet. 376:631–644. 2010. View Article : Google Scholar : PubMed/NCBI
2	Ghulmiyyah L and Sibai B: Maternal mortality from preeclampsia/eclampsia. Semin Perinatol. 36:56–59. 2012. View Article : Google Scholar : PubMed/NCBI
3	Clark SL, Belfort MA, Dildy GA, Herbst MA, Meyers JA and Hankins GD: Maternal death in the 21st century: Causes, prevention, and relationship to cesarean delivery. Am J Obstet Gynecol. 199:36.e1–e5, 91-2. e7-e11. 2008. View Article : Google Scholar
4	Roberts JM and Cooper DW: Pathogenesis and genetics of pre-eclampsia. Lancet. 357:53–56. 2001. View Article : Google Scholar : PubMed/NCBI
5	von Dadelszen P, Magee LA and Roberts JM: Subclassification of preeclampsia. Hypertens Pregnancy. 22:143–148. 2003. View Article : Google Scholar : PubMed/NCBI
6	Lisonkova S, Sabr Y, Mayer C, Young C, Skoll A and Joseph KS: Maternal morbidity associated with early-onset and late-onset preeclampsia. Obstet Gynecol. 124:771–781. 2014. View Article : Google Scholar : PubMed/NCBI
7	Brosens I, Pijnenborg R, Vercruysse L and Romero R: The ‘Great Obstetrical Syndromes’ are associated with disorders of deep placentation. Am J Obstet Gynecol. 204:193–201. 2011. View Article : Google Scholar : PubMed/NCBI
8	Rupaimoole R and Slack FJ: MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov. 16:203–222. 2017. View Article : Google Scholar : PubMed/NCBI
9	Hata A: Functions of microRNAs in cardiovascular biology and disease. Annu Rev Physiol. 75:69–93. 2013. View Article : Google Scholar : PubMed/NCBI
10	Wang Y, Jiao J, Ren P and Wu M: Upregulation of miRNA-223-3p ameliorates RIP3-mediated necroptosis and inflammatory responses via targeting RIP3 after spinal cord injury. J Cell Biochem. Feb 28–2019.(Epub ahead of print).
11	Ranz JM and Machado CA: Uncovering evolutionary patterns of gene expression using microarrays. Trends Ecol Evol. 21:29–37. 2006. View Article : Google Scholar : PubMed/NCBI
12	Huang GH, Cao XY, Li YY, Zhou CC, Li L, Wang K, Li H, Yu P, Jin Y and Gao L: Gene expression profile of the hippocampus of rats subjected to traumatic brain injury. J Cell Biochem. 120:15776–15789. 2019. View Article : Google Scholar : PubMed/NCBI
13	Tan J, Hu L, Yang X, Zhang X, Wei C, Lu Q, Chen Z and Li J: miRNA expression profiling uncovers a role of miR-302b-3p in regulating skin fibroblasts senescence. J Cell Biochem. 121:70–80. 2020. View Article : Google Scholar : PubMed/NCBI
14	Liu Z, Xie M, Yao Z, Niu Y, Bu Y and Gao C: Three meta-analyses define a set of commonly overexpressed genes from microarray datasets on astrocytomas. Mol Neurobiol. 47:325–336. 2013. View Article : Google Scholar : PubMed/NCBI
15	Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:D991–D995. 2013. View Article : Google Scholar : PubMed/NCBI
16	Del Carratore F, Jankevics A, Eisinga R, Heskes T, Hong F and Breitling R: RankProd 2.0: A refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinformatics. 33:2774–2775. 2017. View Article : Google Scholar : PubMed/NCBI
17	Marot G, Foulley JL, Mayer CD and Jaffrézic F: Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics. 25:2692–2699. 2009. View Article : Google Scholar : PubMed/NCBI
18	Heberle H, Meirelles GV, da Silva FR, Telles GP and Minghim R: InteractiVenn: A web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics. 16:1692015. View Article : Google Scholar : PubMed/NCBI
19	Kolde R: Pheatmap: Pretty heatmaps: R package version 1. 2012.
20	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
21	Huang da W, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
22	The Gene Ontology Consortium: The gene ontology resource: 20 Years and still GOing strong. Nucleic Acids Res. 47:D330–D338. 2019. View Article : Google Scholar : PubMed/NCBI
23	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
24	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
25	Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43:D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI
26	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
27	Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI
28	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
29	Wickham H: ggplot2: Elegant graphics for data analysis. Springer International Publishing; 2016
30	Pajak M and Simpson TI: miRNAtap: miRNAtap: microRNA Targets-Aggregated Predictions. R package version 1.22.0. 2020.
31	Dhawan A, Scott JG, Harris AL and Buffa FM: Pan-cancer characterisation of microRNA across cancer hallmarks reveals microRNA-mediated downregulation of tumour suppressors. Nat Commun. 9:52282018. View Article : Google Scholar : PubMed/NCBI
32	Uzan J, Carbonnel M, Piconne O, Asmar R and Ayoubi JM: Pre-eclampsia: Pathophysiology, diagnosis, and management. Vasc Health Risk Manag. 7:467–474. 2011.PubMed/NCBI
33	Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI
34	Osungbade KO and Ige OK: Public health perspectives of preeclampsia in developing countries: Implication for health system strengthening. J Pregnancy. 2011:4810952011. View Article : Google Scholar : PubMed/NCBI
35	Chen J and Khalil RA: Matrix metalloproteinases in normal pregnancy and preeclampsia. Prog Mol Biol Transl Sci. 148:87–165. 2017. View Article : Google Scholar : PubMed/NCBI
36	Karumanchi SA and Bdolah Y: Hypoxia and sFlt-1 in preeclampsia: The ‘Chicken-and-Egg’ question. Endocrinology. 145:4835–4837. 2004. View Article : Google Scholar : PubMed/NCBI
37	Janku F, Yap TA and Meric-Bernstam F: Targeting the PI3K pathway in cancer: Are we making headway? Nat Rev Clin Oncol. 15:273–291. 2018. View Article : Google Scholar : PubMed/NCBI
38	Mayer IA and Arteaga CL: The PI3K/AKT pathway as a target for cancer treatment. Annu Rev Med. 67:11–28. 2016. View Article : Google Scholar : PubMed/NCBI
39	Cudmore MJ, Ahmad S, Sissaoui S, Ramma W, Ma B, Fujisawa T, Al-Ani B, Wang K, Cai M, Crispi F, et al: Loss of Akt activity increases circulating soluble endoglin release in preeclampsia: Identification of inter-dependency between Akt-1 and heme oxygenase-1. Eur Heart J. 33:1150–1158. 2012. View Article : Google Scholar : PubMed/NCBI
40	Hastie R, Brownfoot FC, Pritchard N, Hannan NJ, Cannon P, Nguyen V, Palmer K, Beard S, Tong S and Kaitu'u-Lino TJ: EGFR (Epidermal Growth Factor Receptor) signaling and the mitochondria regulate sFlt-1 (Soluble FMS-Like Tyrosine Kinase-1) secretion. Hypertension. 73:659–670. 2019. View Article : Google Scholar : PubMed/NCBI
41	Goddard KA, Tromp G, Romero R, Olson JM, Lu Q, Xu Z, Parimi N, Nien JK, Gomez R, Behnke E, et al: Candidate-gene association study of mothers with pre-eclampsia, and their infants, analyzing 775 SNPs in 190 genes. Hum Hered. 63:1–16. 2007. View Article : Google Scholar : PubMed/NCBI
42	Gabinskaya T, Salafia CM, Gulle VE, Holzman IR and Weintraub AS: Gestational age-dependent extravillous cytotrophoblast osteopontin immunolocalization differentiates between normal and preeclamptic pregnancies. Am J Reprod Immunol. 40:339–346. 1998. View Article : Google Scholar : PubMed/NCBI
43	Xia J, Qiao F, Su F and Liu H: Implication of expression of osteopontin and its receptor integrin alphanubeta3 in the placenta in the development of preeclampsia. J Huazhong Univ Sci Technolog Med Sci. 29:755–760. 2009. View Article : Google Scholar : PubMed/NCBI
44	Taylor BD, Ness RB, Olsen J, Hougaard DM, Skogstrand K, Roberts JM and Haggerty CL: Serum leptin measured in early pregnancy is higher in women with preeclampsia compared with normotensive pregnant women. Hypertension. 65:594–599. 2015. View Article : Google Scholar : PubMed/NCBI
45	Salimi S, Farajian-Mashhadi F, Naghavi A, Naghavi A, Mokhtari M, Shahrakipour M, Saravani M and Yaghmaei M: Different profile of serum leptin between early onset and late onset preeclampsia. Dis Markers. 2014:6284762014. View Article : Google Scholar : PubMed/NCBI
46	Buurma AJ, Turner RJ, Driessen JH, Mooyaart AL, Schoones JW, Bruijn JA, Bloemenkamp KW, Dekkers OM and Baelde HJ: Genetic variants in pre-eclampsia: A meta-analysis. Hum Reprod Update. 19:289–303. 2013. View Article : Google Scholar : PubMed/NCBI
47	Estrada-Gutierrez G, Cappello RE, Mishra N, Romero R, Strauss JF III and Walsh SW: Increased expression of matrix metalloproteinase-1 in systemic vessels of preeclamptic women: A critical mediator of vascular dysfunction. Am J Pathol. 178:451–460. 2011. View Article : Google Scholar : PubMed/NCBI
48	Yang X and Meng T: MicroRNA-431 affects trophoblast migration and invasion by targeting ZEB1 in preeclampsia. Gene. 683:225–232. 2019. View Article : Google Scholar : PubMed/NCBI
49	Lv Y, Lu X, Li C, Fan Y, Ji X, Long W, Meng L, Wu L, Wang L, Lv M and Ding H: miR-145-5p promotes trophoblast cell growth and invasion by targeting FLT1. Life Sci. 239:1170082019. View Article : Google Scholar : PubMed/NCBI
50	Brkić J, Dunk C, O'Brien J, Fu G, Nadeem L, Wang YL, Rosman D, Salem M, Shynlova O, Yougbaré I, et al: MicroRNA-218-5p promotes endovascular trophoblast differentiation and spiral artery remodeling. Mol Ther. 26:2189–2205. 2018. View Article : Google Scholar : PubMed/NCBI
51	Hong F, Li Y and Xu Y: Decreased placental miR-126 expression and vascular endothelial growth factor levels in patients with pre-eclampsia. J Int Med Res. 42:1243–1251. 2014. View Article : Google Scholar : PubMed/NCBI
52	Yan T, Liu Y, Cui K, Hu B, Wang F and Zou L: MicroRNA-126 regulates EPCs function: Implications for a role of miR-126 in preeclampsia. J Cell Biochem. 114:2148–2159. 2013. View Article : Google Scholar : PubMed/NCBI
53	He P, Shao D, Ye M and Zhang G: Analysis of gene expression identifies candidate markers and pathways in pre-eclampsia. J Obstet Gynaecol. 35:578–584. 2015. View Article : Google Scholar : PubMed/NCBI
54	Ma Y, Lin H, Zhang H, Song X and Yang H: Identification of potential crucial genes associated with early-onset pre-eclampsia via a microarray analysis. J Obstet Gynaecol Res. 43:812–819. 2017. View Article : Google Scholar : PubMed/NCBI
55	Song J, Li Y and An RF: Identification of early-onset preeclampsia-related genes and MicroRNAs by bioinformatics approaches. Reprod Sci. 22:954–963. 2015. View Article : Google Scholar : PubMed/NCBI
56	Gunel T, Hosseini MK, Gumusoglu E, Kisakesen HI, Benian A and Aydinli K: Expression profiling of maternal plasma and placenta microRNAs in preeclamptic pregnancies by microarray technology. Placenta. 52:77–85. 2017. View Article : Google Scholar : PubMed/NCBI
57	Betoni JS, Derr K, Pahl MC, Rogers L, Muller CL, Packard RE, Carey DJ, Kuivaniemi H and Tromp G: MicroRNA analysis in placentas from patients with preeclampsia: Comparison of new and published results. Hypertens Pregnancy. 32:321–339. 2013. View Article : Google Scholar : PubMed/NCBI
58	Lykoudi A, Kolialexi A, Lambrou GI, Braoudaki M, Siristatidis C, Papaioanou GK, Tzetis M, Mavrou A and Papantoniou N: Dysregulated placental microRNAs in early and late onset preeclampsia. Placenta. 61:24–32. 2018. View Article : Google Scholar : PubMed/NCBI
59	Yang-Dong O, Ya-Xian L and Xue-qiong Z: Excavation and bioinformatics analysis of early-onset preeclampsia related MicroRNA and target genes. J Nongken Med. 40:494–499. 2018.