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Analysis of Key Genes and Their Functions in Placental Tissue of Patients with Gestational Diabetes Mellitus Yuxia Wang1, Haifeng Yu2, Fangmei Liu2 and Xiue Song2*

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Analysis of Key Genes and Their Functions in Placental Tissue of Patients with Gestational Diabetes Mellitus Yuxia Wang1, Haifeng Yu2, Fangmei Liu2 and Xiue Song2* Wang et al. Reproductive Biology and Endocrinology (2019) 17:104 https://doi.org/10.1186/s12958-019-0546-z RESEARCH Open Access Analysis of key genes and their functions in placental tissue of patients with gestational diabetes mellitus Yuxia Wang1, Haifeng Yu2, Fangmei Liu2 and Xiue Song2* Abstract Background: This study was aimed at screening out the potential key genes and pathways associated with gestational diabetes mellitus (GDM). Methods: The GSE70493 dataset used for this study was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in the placental tissue of women with GDM in relation to the control tissue samples were identified and submitted to protein-protein interaction (PPI) network analysis and subnetwork module mining. Functional enrichment analyses of the PPI network and subnetworks were subsequently carried out. Finally, the integrated miRNA–transcription factor (TF)–DEG regulatory network was analyzed. Results: In total, 238 DEGs were identified, of which 162 were upregulated and 76 were downregulated. Through PPI network construction, 108 nodes and 278 gene pairs were obtained, from which chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, protein tyrosine phosphatase, receptor type C (PTPRC), and human leukocyte antigen (HLA) were screened out as hub genes. Moreover, genes associated with the immune-related pathway and immune responses were found to be significantly enriched in the process of GDM. Finally, miRNAs and TFs that target the DEGs were predicted. Conclusions: Four candidate genes (viz., CXCL9, CXCL10, PTPRC, and HLA) are closely related to GDM. miR-223-3p, miR-520, and thioredoxin-binding protein may play important roles in the pathogenesis of this disease. Keywords: Gestational diabetes mellitus, Differentially expressed genes, Protein-protein interaction network, Integrated regulatory network, Transcription factors Background there is an urgent need to grasp the pathophysiology Expectant mothers with gestational diabetes mellitus and pathogenesis of the disease [2]. (GDM), a common pregnancy complication, have an in- Previous studies have suggested that GDM is caused creased risk of developing type 2 diabetes mellitus [1]. by enhanced insulin resistance and pancreatic beta (β)- Over the past 20 years, the prevalence of GDM has dou- cell dysfunction [7], involving genes that are related to bled, affecting approximately 10% of pregnancies in the insulin signaling, insulin secretion, maturity-onset dia- USA [2, 3]. Babies born to mothers with GDM are typic- betes of the young, and lipid and glucose metabolism, to ally at a high risk for macrosomia, neonatal cardiac dys- name a few [8, 9]. Subsequently, it was found that in- function, neonatal hypoglycemia, stillbirth, childhood flammatory pathways [10], metabolic disorder [11], oxi- obesity, and type 2 diabetes mellitus [4–6]. Given the dative stress [12], and vitamin D concentrations [13] worldwide prevalence and adverse outcomes of GDM, were also related to GDM. Furthermore, some genetic alterations, such as those of the genes encoding β3- adrenergic receptor [14] and transcription factor 7-like 2 polymorphism [15], were also found to be associated * Correspondence: [email protected] 2Department of Obstetrics, Jinan Central Hospital, No. 105 Jiefang Road, Lixia with GDM. Moreover, GDM results in major changes in District, Jinan City 250013, Shandong Province, China the expression profiles of placental genes, with a Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wang et al. Reproductive Biology and Endocrinology (2019) 17:104 Page 2 of 10 significant increase in markers and mediators of inflam- function (MF) categories, as well as the KEGG pathway mation [10]. Recently, several microarray studies have categories. The hypergeometric distribution threshold verified that the cytochrome P450, family 1, subfamily A, for these analyses was a p-value of < 0.05. polypeptide 1 (CYP1A1), estrogen receptor 1 (ESR1) [16], fibronectin 1 (FN1), and leptin (LEP)[17] genes PPI network construction and subnetwork module mining were essential for the pathogenesis of GDM. However, The Search Tool for the Retrieval of Interacting Genes because the genes related to GDM have not yet been (STRING, ver. 10.0, https://string-db.org/)[23] database fully identified, the biological processes underlying the was used to analyze functional interactions between the pathogenesis of this disease remain unclear. DEGs and other genes, under a confidence score threshold In this study, the gene expression profiles of placental of > 0.4. The PPI network was established using Cytoscape tissue from women with GDM were compared with (ver. 3.3.0, http://www.cytoscape.org/)[24]. Then, the top- those of matched normal placental tissue by microarray ology of the network was analyzed, and the hub nodes in analysis, to screen out differentially expressed genes the network were obtained by calculating the average de- (DEGs) in GDM. The identified DEGs were then sub- gree of each node. The average degree is the average num- mitted to Kyoto Encyclopedia of Genes and Genomes ber of edges connecting all the nodes in the network, (KEGG) pathway and Gene Ontology (GO) enrichment measured by three indexes: degree centrality [25], between- analyses to explore the crucial pathways of GDM. Add- ness centrality [26], and closeness centrality [27]. itionally, a protein-protein interaction (PPI) network was Molecular Complex Detection (MCODE) [28]isan constructed and subnetwork module mining was per- automated method for searching molecular complexes formed to seek out the candidate disease genes. Finally, with similar functions in large protein interaction net- microRNAs (miRNAs) and transcription factors (TFs) works. The MCODE (ver. 1.4.2, http://apps.cytoscape. that target the candidate DEGs were identified and ana- org/apps/mcode) plugin of Cytoscape was used to lyzed. The results from this study may lay the ground- analyze the subnetwork modules with similar functions work for future research on the pathogenesis of GDM. in the original PPI network. Then, GO and KEGG path- way analyses of the subnetwork modules were per- Methods formed to evaluate their functions. Microarray analysis The gene expression dataset GSE70493, which is based Prediction of miRNAs and transcription factors that on the GPL17586 [HTA-2_0] Affymetrix Human Tran- regulate the DEGs scriptome Array 2.0 [transcript (gene) version] platform, The TFs associated with the DEGs were predicted by the was downloaded from the National Center for Biotech- position weight matrices from TRANSFAC and JASPAR nology Information’s Gene Expression Omnibus data- in the Enrichr database [29], under the hypergeometric base (http://www.ncbi.nlm.nih.gov/geo/). This dataset distribution threshold of p < 0.01. miRNAs associated comprised 63 placental tissue specimens collected from with the DEGs were predicted by miRTarBase in the 32 cases of GDM and 31 matched pregnancies without Enrichr database, under the hypergeometric distribution maternal complications. threshold of p < 0.01. On the basis of the miRNA–DEG regulatory network and TF–DEG regulatory network, Data reprocessing the integrated DEG–miRNA–TF regulatory network was The probe-level data (CEL files) were converted to ex- constructed using Cytoscape. pression estimates by the Puma [18] and Oligo [19] packages in R, and the original expression dataset was Results processed into expression values using the robust multi- Analysis of the differentially expressed genes array average algorithm [20] with the default settings im- After the microarray analysis, the probes that were mapped plemented in Bioconductor. The DEGs were identified to multiple genes were considered nonspecific and were re- with the limma [21] software package according to the moved, and only those with unique genes were distin- expression values of the sample probes, and only those guished as DEGs. In total, 238 DEGs (comprising 162 with a p-value of less than 0.01 were selected and anno- upregulated and 76 downregulated genes) were identified tated for further analysis. from the GDM placental tissue samples compared with the matching normal pregnant tissue samples (Fig. 1). GO and KEGG pathway enrichment analyses To assess the functions and significantly enriched path- Functional enrichment analyses ways of the DEGs, ClusterProfiler [22] was used to iden- Through GO analysis, the top 10 overrepresented GO terms tify the overrepresented GO terms in the biological in the BP, MF, and CC categories were identified on the process (BP), cellular component (CC), and molecular basis of the p-value (Fig. 2a). In the BP category, the Wang et al. Reproductive Biology and Endocrinology (2019) 17:104 Page 3 of 10 Fig. 1 Volcano map of the distribution of differentially expressed genes. Each blue dot represents a differentially expressed gene overrepresented terms included the interferon-gamma-
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