Open Access

Microarray analysis of differentially expressed genes and their functions in omental visceral adipose tissues of pregnant women with vs. without gestational diabetes mellitus

  • Authors:
    • Yuan Qian
    • Hao Sun
    • Hongli Xiao
    • Meirun Ma
    • Xue Xiao
    • Qinzai Qu
  • View Affiliations

  • Published online on: March 22, 2017     https://doi.org/10.3892/br.2017.878
  • Pages:503-512
  • Copyright: © Qian et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Increasing evidence has shown that insulin resistance in omental visceral adipose tissue (OVAT) is a characteristic of gestational diabetes mellitus (GDM). The present study aimed to identify differentially expressed genes (DEGs) and their associated functions and pathways involved in the pathogenesis of GDM by comparing the expression profiles of OVATs obtained from pregnant Chinese women with and without GDM during caesarian section. A total of 935 DEGs were identified, including 450 downregulated and 485 upregulated genes. In the gene ontology category cellular components, the DEGs were predominantly associated with functions of the extracellular region, while receptor binding was predominant in the molecular function category and biological process terms included antigen processing and presentation, extracellular matrix organization, positive regulation of cell‑substrate adhesion, response to nutrients and response to dietary excess. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed and a functional interaction network was constructed. Functions of downregulated genes included antigen processing and presentation as well as cell adhesion molecules, while those of upregulated genes included transforming growth factor (TGF)‑β‑signaling, focal adhesion, phosphoinositide‑3 kinase‑Akt‑signaling, P53 signaling, extracellular matrix‑receptor interaction and regulation of actin cytoskeleton pathway. The five main pathways associated with GDM were antigen processing and presentation, cell adhesion molecules, Type 1 diabetes mellitus, natural killer cell‑mediated cytotoxicity and TGF‑β signaling. These pathways were included in the KEGG pathway categories of ‘signaling molecules and interaction’, ‘immune system’ and ‘inflammatory response’, suggesting that these processes are involved in GDM. The results of the present study enhanced the present understanding of the mechanisms associated with insulin resistance in OVATs of GDM.

Introduction

Insulin resistance is a condition of the body not appropriately responding to circulating insulin; furthermore, insulin resistance in adipose tissue is an important characteristic of gestational diabetes mellitus (GDM) (1). To compensate for the insulin resistance in the adipose tissue, pancreatic β-cells initially produce more insulin; however, beyond a certain limit, β-cell failure and DM occur (2). At the cellular level, the insulin signaling cascade has an important metabolic role, while its disruption may induce insulin resistance and is closely associated with GDM (3). However, to date, the causes of insulin resistance in adipose tissue at the cellular and molecular level have remained elusive. As aberration of gene expression and molecular pathways may be involved in GDM, the present study determined and analyzed the gene expression profiles of adipose tissues from pregnant women with and without GDM. To the best of our knowledge, the present study was the first to show a distinct pattern of differentially expressed genes (DEGs) in omental visceral adipose tissue (OVAT) from Chinese GDM patients by using a whole genome microarray.

While adipose tissue has been regarded as a storage organ, it is likely to have further functions, as it has been convincingly shown to secrete a number of cytokines, which have a fundamental role in causing insulin resistance in GDM (4,5). Adipose tissues in different sites of the body have distinct biochemical properties (6). It has been reported that the accumulation of OVAT is mainly correlated with an increased risk of altered glucose homeostasis and insulin resistance (7).

Microarrays represent a powerful tool for studying the mechanisms of complex diseases and enable for comprehensive analysis of the interaction between multiple genes simultaneously implicated in pathological processes (810).

In the present study, the gene expression profiles of OVATs from pregnant women with or without GDM were compared with the aim of identifying DEGs. Functional enrichment analysis was performed to determine the gene ontology (GO) functions and the key signaling pathways in the pathogenetic processes of GDM. Several molecular mechanisms and pathways that may be responsible for the insulin resistance and progression of GDM were identified.

Materials and methods

Patients and tissues

OVATs were obtained from six patients during C-section at the Department of Obstetrics and Gynaecology, the First Affiliated Hospital of Kunming Medical College (Kunming, China) from January 2012 to September 2013, including three cases with normal glucose tolerance and three cases with GDM. The diagnosis of GDM was made by OGTT 75 g, according to the World Health Organization criteria, during the second trimester (24–28 weeks of gestation). Exclusion criteria for participation included multiple gestation, infection, pregnancy with complications, congenital or chromosomal abnormalities of the fetus, a family history of diabetes, and pregnancy with alcohol or drug abuse. Samples were obtained and immediately snap-frozen in liquid nitrogen. Informed consent was obtained prior to caesarean section. The present study was approved by the Ethics Committee of Kunming Medical College (Kunming, China).

RNA extraction and isolation

Adipose tissues from each of the six patients were individually minced with small scissors and total RNA was extracted using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) and purified using the RNeasy Mini kit (Qiagen, Hilden, Germany). The RNA concentration was detected using a 2100 Bioana bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA). RNA purity and integrity were assessed by gel electrophoresis as well as spectrophotometrically using a Nanodrop (Thermo Fisher Scientific, Inc.). The extracted RNA was temporarily stored in a −70°C freezer in 95% ethanol for further analysis.

Microarray assay and data normalization

The whole Human Gene Expression Array (Affymetrix GeneChip® PrimeView™, Homo sapiens; Affymetrix, Santa Clara, CA, USA) was used to screen for gene expression in the OVAT cells of pregnant women according to the manufacturer's instructions.

First, the ribosomal RNA was removed from the total RNA, and the purified mRNA was amplified and reversely transcribed into fluorescent/biotinylated complementary (c)DNA using the ENZO kit (Affymetrix). The cDNA was fragmented and then hybridized to the Affymetrix GeneChip® PrimeView™ containing 36,000 probe-sets representing ~20,000 unique genes. Six microarrays were run for the samples (from the six patients including three cases with normal glucose tolerance and three cases with GDM mentioned above) tested and each array was replicated twice. Subsequently, the arrays were processed on an Affymetrix fluidics station (Affymetrix), where they were subjected to automated washing and staining. The arrays were scanned using an Affymetrix GeneChip scanner 3000 (Affymetrix), raw data were obtained using the Feature Extraction Software 10.7 (Agilent Technologies, Inc.) and normalized using the quantile algorithm with Gene Spring 11.0 (Agilent Technologies, Inc.) to remove background bias with the normalization value set to 1. Systematic bioinformatic analyses of the microarray data were performed by Novel Bioinformatics Co., Ltd. (Shanghai, China).

Differential expression analysis, GO analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, gene-gene interaction network (Gene-Act-Net) and pathway-pathway (Path)-Act-Net construction

Genes with fold-changes of ≥2 or <0.5 and P<0.05 were subjected to a secondary selection based on the size of the negative log2 of their P-values, and the thereby selected genes were considered to be DEGs.

To assess the function of the DEGs, they were enriched into terms of the GO categories cellular component (CC), biological process (BP) and molecular function (MF) using the GO chart feature of the Database for Annotation, Visualization and Integrated Discovery.

Significantly enriched pathways of these DEGs were then determined using the KEGG database, and Gene-Act-Net interaction network was constructed with regard to genes, included in the database.

Based on the results of the KEGG enrichment analyses, the Path-Act-Net was constructed. Furthermore, the Gene-Act-Net was constructed based on the DEGs in the GO terms and pathways.

Results

Patient characteristics

Between the patients with and without GDM, no significant differences in age, G1P0 parity, gestational week or height were present. However, the weight and body mass index were significantly higher in the GDM group.

DEGs in OVATS of GDM patients

A total of 935 DEGs were identified, including 450 genes which were downregulated and 485 which were upregulated in the OVATS of pregnant women with GDM compared with those without.

GO analysis of DEGs

In the GO category MF, terms including receptor binding, actin binding, extracellular matrix binding and C-X-C motif chemokine receptor R3 binding were found to be enriched. Moreover, in the category CC, DEGs were enriched in the terms extracellular matrix/region or actin cytoskeleton, while in the category BP, antigen processing and presentation, extracellular matrix organization, positive regulation of cell-substrate adhesion, response to nutrients and response to dietary excess were significantly over-represented.

In the BP category, antigen processing and presentation, immune response and regulation of immune response were among the major downregulated GO terms (Fig. 1A), while vascular smooth muscle contraction, extracellular matrix organization and response to nutrients were among the major upregulated GO terms (Fig. 1B). Similarly, the top 10 down- and upregulated GO terms in the CC category are shown in Fig. 1C and D, respectively, and those in the MF category are shown in Fig. 1E and F, respectively.

In the CC category, extracellular region, major histocompatibility complex class II and nuclear RNA export factor complex were the major donwnregulated GO terms (Fig. 1C), while melanosome, smooth muscle contractile fiber and extracellular matrix were the major downregulated GO terms (Fig. 1D).

In the MF category, natural killer (NK) cell lectin-like receptor binding, serine-type endopeptidase activity and peptide antigen binding were the top three donwnregulated GO terms (Fig. 1E), while heparin binding, phosphatidylserine binding and phosphatidylinositol-4,5-bisphosphate binding were the top three upregulated GO terms (Fig. 1F). The terms likely to be relevant for GDM were antigen processing and presentation, immune response and response to nutrients.

KEGG pathway analysis

To identify the most commonly dysregulated pathways in OVATs of pregnant women with GDM, KEGG pathway analysis was performed. The top six enriched pathways of the DEGs were antigen processing and presentation, viral myocarditis, graft-versus-host disease, phagosome, allograft rejection and type I diabetes mellitus (T1D) (Fig. 2A). With regard to the antigen processing and presentation pathway, 79 genes were represented on the array chips, of which 14 were differentially expressed [transporter 2, ATP-Binding Cassette, Sub-Family B (P=0.0046), LOC100287534 (P=0.0160), major histocompatibility complex, class I, A (HLA-A; P=0.011), killer cell immunoglobulin-like receptor (KIR)2DL4 (P=0.0160), KIR2DL5A (P=0.0161), HLA-DQA1 (P=0.0139), heat shock protein (HSP)A5 (P=0.0189), HLA-E (P=0.0199), HLA-DOA (P=0.0251), HLA-F (P=0.0299), HLA-DMB (P=0.033), HSPA4 (P=0.0379), HSPA6 (P=0.0390), HLA-DPB1 (P=0.0489)].

The top five upregulated pathways included focal adhesion, extracellular matrix (ECM)-receptor interaction, proteoglycans in cancer, vascular smooth muscle contraction and the peroxisome proliferator-activated receptor signaling pathway (Fig. 2B). The top four downregulated pathways included graft-versus-host disease, antigen processing and presentation, allograft rejection and NK cell-mediated cytotoxicity (Fig. 2C).

Gene-Act-Net and Pathway-Act-Net

The Gene-Act-Net and the Path-Act-Net were constructed based on the interaction of the DEGs or their enriched pathways, respectively (P<0.05). In the Path-Act-Net, antigen processing and presentation was the top-scoring component identified. Furthermore, complement and coagulation cascades, cell adhesion molecules, the transforming growth factor (TGF)-β, phosphoinositide-3 kinase (PI3K)-Akt and P53 signaling pathways, and regulation of the actin cytoskeleton were high-scoring components in the network, suggesting that immune response and signaling pathways associated with insulin are the major pathways associated with the pathogenesis of GDM. Within the network, antigen processing and presentation as well as cell adhesion molecules were pathways associated with downregulated DEGs with the highest number of connections, while TGF-β signaling, focal adhesion, PI3K-Akt signaling, P53 signaling, ECM-receptor interaction and regulation of the actin cytoskeleton were pathways associated with upregulated DEGs with the highest number of connections (Fig. 3).

In the Gene-Act-Net, the four genes with the highest degree of interaction were integrin subunit β-1 (ITGB1), AKT serine/threonine kinase 3 (AKT3), integrin subunit α V (ITGAV) and fibroblast growth factor receptor (FGFR)3 (≥10 connections) (Table I).

Table I.

Differentially expressed genes with the highest degree of interaction in the gene-gene interaction network.

Table I.

Differentially expressed genes with the highest degree of interaction in the gene-gene interaction network.

Gene IDSymbolDescriptionInDegreeOutDegreeDegree
3688aITGB1Integrin subunit β-19  716
10000aAKT3AKT serine/threonine kinase 321012
3685aITGAVIntegrin subunit α V9  110
2261bFGFR3Fibroblast growth factor receptor 38  210
3105bHLA-AMajor histocompatibility complex, class I, A3  5  8
3134bHLA-FMajor histocompatibility complex, class I, F3  5  8
3133bHLA-EMajor histocompatibility complex, class I, E3  5  8
10398aMYL9Myosin light chain 96  1  7
6385bSDC4Syndecan-46  0  6
4659aPPP1R12AProtein phosphatase 1 regulatory subunit 12A3  3  6
5500aPPP1CBProtein phosphatase 1 catalytic subunit β3  3  6
3479aIGF1Insulin-like growth factor 13  3  6
2257aFGF12Fibroblast growth factor 123  3  6
2259aFGF14Fibroblast growth factor 143  3  6
7410bVAV2VAV2 guanine nucleotide exchange factor3  3  6
2952aGSTT1Glutathione S-transferase theta-13  3  6
4257aMGST1Microsomal glutathione S-transferase 1, isoform CRA_a3  3  6
2949aGSTM5Glutathione S-transferase Mu 53  3  6
3111bHLA-DOAMajor histocompatibility complex, class II, DO α3  3  6
3109bHLA-DMBMajor histocompatibility complex, class II, DM β3  3  6
3115bHLA-DPB1Major histocompatibility complex, class II, DP β 13  3  6
3117bHLA-DQA1cDNA FLJ51239, moderately similar to major histocompatibility complex class II histocompatibility antigen, DQ(W3) α chain3  3  6
7057aTHBS1Thrombospondin 1, isoform CRA_a1  4  5
21826aTHBS2 Thrombospondin-21  4  5
998aCDC42Cell division cycle 42, isoform CRA_a1  4  5
8644aAKR1C3Aldo-keto reductase family 1 member C3 homolog4  1  5
7363aUGT2B4 UDP-glucuronosyltransferase 2 member B41  4  5
7037bTFRCTransferrin receptor (p90, CD71) mRNA4  1  5
1282aCOL4A1Collagen type IV α 1 chain1  3  4
1281aCOL3A1Collagen type III α 1 chain1  3  4
4638aMYLKMyosin light chain kinase3  1  4
1277aCOL1A1Collagen type I α-1 chain1  3  4
3371aTNCTenascin C1  3  4
57292bKIR2DL5AKiller cell immunoglobulin like receptor, two Ig domains and long cytoplasmic tail 5A3  1  4
3805bKIR2DL4Killer cell immunoglobulin like receptor, two Ig domains and long cytoplasmic tail 43  1  4
6891bTAP2Transporter 2, ATP binding cassette subfamily B member0  4  4
4192aMDM2E3 ubiquitin-protein ligase Mdm22  1  3
5781aPTPN11Protein tyrosine phosphatase, non-receptor type 112  1  3
1962aEHHADHEnoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase1  2  3
2180aACSL1Acyl-CoA synthetase long-chain family member 1 mRNA1  2  3
4773bNFATC2Nuclear factor of activated T-cells 21  1  2
948aCD36CD36 antigen mRNA2  0  2
4629aMYH11Myosin heavy chain 111  1  2
2316aFLNAFilamin-A0  2  2
94274aPPP1R14AProtein phosphatase 1 regulatory subunit 14A0  2  2
51aACOX1Acyl-CoA oxidase 12  0  2
7043aTGFB3Transforming growth factor, β 32  0  2
4734bNEDD4Neural precursor cell expressed, developmentally downregulated 4, E3 ubiquitin protein ligase1  1  2
548596bCKMT1ACreatine kinase, mitochondrial 1A1  1  2
1159bCKMT1BCreatine kinase, mitochondrial 1B (EC 2.7.3.2)1  1  2
10725bNFAT5Nuclear factor of activated T-cells 5, tonicity-responsive, isoform CRA_b1  0  1
3952aLEPLeptin0  1  1
3563bIL3RAInterleukin-3 receptor subunit α1  0  1
284aANGPT1Angiopoietin-10  1  1
2034aEPAS1Endothelial PAS domain-containing protein 10  1  1
10332bCLEC4MC-type lectin domain family 4, member M, transcript variant 6 mRNA0  1  1
1759bDNM1Dynamin-1 (EC 3.6.5.5)0  1  1

a Upregulated genes

b downregulated genes.

{ label (or @symbol) needed for fn[@id='tfn3-br-0-0-878'] } InDegree, number of inbound gene interactions; OutDegree, number of outbound gene interactions; cDNA, complementary DNA; Ig, immunoglobulin; CoA, coenzyme A.

Discussion

GDM is defined as the first occurrence of glucose intolerance during pregnancy. Although it is a common disorder, its pathophysiology has remained to be fully elucidated. Adipose tissue has been implicated in the development of insulin resistance and excess adipose tissue has been shown to be associated with GDM (11,12).

Adipose tissue can be further classified as sub-cutaneous and visceral adipose tissue. The human body has six visceral fat depots: Perirenal, gonadal, epicardial, retroperitoneal, omental and mesenteric adipose, each of which has distinct characteristics. Human omental adipose tissue exhibits specific mRNA expression profiles. Only few studies have compared the difference in expression profiles in OVATs of pregnant women with and without GDM (13,14). To the best of our knowledge, the present study was the first to assess the DEGs in OVATs from pregnant women with vs. without GDM using the Affymetrix chip due to the difficulty in obtaining OVAT samples during the same period, which are matched with regard to age, gestational age, G1P0 parity and height.

The microarray approach and bioinformatic technologies were combined to perform a comparative gene expression profiling analysis of normal and pathological OVATs. Subsets of DEGs were identified, GO and KEGG pathways were analyzed, and interaction networks were generated.

The DEGs identified between the two groups may represent new candidate genes associated with GDM. In the present study, the top four genes in the Gene-Act-Net with the highest degree of interaction were ITGB1, AKT3, ITGAV (upregulated) and FGFR3 (downregulated), suggesting their function as hub genes with a high regulatory ability. ITGB7 and ITGB1 have been previously reported to be associated with type 1 diabetes and their proteins engage in direct mutual receptor-ligand interactions associated with the homing of T cells from blood to tissues, such as the intestine and pancreas (15). ITGB1 was also found to be involved in the regulation of cell migration associated with numerous pathologies (1618). AKT3 is activated by growth factors and other extracellular stimuli, including glucose, as well as key upstream regulatory proteins including Ras, PI3K subunits and phosphatase and tensin homolog, which are involved in the regulation of a diversity of biological roles of activated Akt, including the regulation of cell metabolism, survival and proliferation (19,20). ITGAV and FGFR3 are involved in cell migration and proliferation, and have been reported to be associated with the progression and dissemination of cancer (2124).

GO analysis revealed enrichment of the DEGs in the MF category in terms including receptor binding, extracellular matrix binding and CXCR3, as well as in the BP category in terms including antigen processing and presentation, extracellular matrix organization, positive regulation of cell-substrate adhesion, which were also closely associated with inflammation response or immune response. In addition, the DEGs in the GDM group were enriched in GO terms including vascular smooth muscle contraction (GO:0014829) and muscle contraction (GO:0006936), possibly due to the impaired vasodilation in the GDM group. The function of vascular smooth muscle cells is to regulate blood flow and pressure through contraction and relaxation. The principle complications of GDM are cardiovascular disease, the risk of which is potentiated by obesity, hypertensive disorders of pregnancy. Studies have shown that women with GDM have higher risk of cardiac dysfunction and endothelial dysfunction very soon after pregnancy (25), which may explain for the upregulation of these pathways compared with those in normal women in the same gestational week. Furthermore, preterm delivery is relatively common in women with GDM and birth-associated muscle contractions may be another reason for the enrichment of these pathways in the GDM group (26).

In the present study, KEGG analysis revealed that the DEGs in the OVATs from patients with GDM were enriched in pathways including antigen processing and presentation, cell adhesion molecules, T1D, NK cell-mediated cytotoxicity and TGF-β signaling. These pathways were categorized as ‘signaling molecules and interaction’, ‘immune system’ and ‘inflammatory response’, suggesting that these processes are involved in GDM. Among them, four pathways (antigen processing and presentation, cell adhesion molecules, T1D and NK cell-mediated cytotoxicity) have been previously reported in the blood of Chinese women with GDM (27). Considering that OVAT is composed of several different cell types, including adipocytes, pre-adipocytes, macrophages, vascular cells and other blood cells, it is reasonable to assume that certain significant pathways are similar between OVAT and peripheral blood, and insulin resistance in peripheral blood and adipose tissue may also share certain pathways. A study by Yang et al (28) demonstrated that TGF-β signaling participates in steatohepatitis through the regulation of lipid metabolism and apoptosis in hepatocytes. Considering that the present study used adipose tissues, it is expected that pathways associated with lipid metabolism dysfunction have a marked role in the insulin resistance of patients with GDM. In the present study, DEGs were enriched in pathways including peroxisome proliferator-activated receptor signaling, adipocytokine signaling and fatty acid degradation, which are associated with lipid metabolism.

As for pathways associated with T1D, the results of the present study suggested that GDM is mainly facilitated by autoimmune destruction of insulin-producing pancreatic β-cells. The nine genes found to be involved, HLA-A, HLA-DQA1, granzyme B, HLA-F, HLA-E, perforin 1, HLA-DOA, HLA-DMB and HLA-DPB1, were all downregulated. These results may indicate similarities between GDM and T1D. However, it has been reported that pregnant women have an increased risk of developing T2D. GDM may share common characteristics with T1D, including insulin resistance and the immune response; the findings of the present study are therefore in accordance with those of previous ones, which appeared to have detected autoimmune phenomena in patients with GDM (29). Evangelista et al (30) reported that gene expression signatures of GDM patients were closer to those of T1D patients than to those of T2D patients, which may provide an explanation for the findings of the present study.

In the present study, KEGG analysis also suggested that pathways associated with antigen processing and presentation were dysregulated in the GDM group. The findings highlight a significant role of HLA genes in the adipose tissue of women with GDM. A general downregulation of HLA genes has been observed among placentas and blood samples from women with GDM (31). In general, GDM has been associated with increased anti-HLA-class II antibodies in the maternal circulation and reduced tolerance towards alloantigen through inflammatory activation (32).

In the present study, the relevant terms in which DEGs were enriched were antigen processing and presentation, immune response and response to nutrients. Maternal over-nutrition has been reported to be associated with elevated triglyceride levels, increased inflammatory markers and fatty livers in offspring (33). GDM is reconsidered to be a state of chronic, low-grade inflammation and is, distinct from an acute pro-inflammatory response, primarily triggered by metabolites and nutrients, leading to systemic insulin resistance.

As for NK cell-mediated cytotoxicity pathways, their participation in GDM progression is expected due to the proposed involvement of the NK pathway in T1D (34). It is thought that the NK pathway causes the release of cytotoxic granules, penetration of effector proteins through the cell membrane and subsequent induction of apoptosis. Considering adipose tissue being the main inflammatory organ and highly expressing numerous inflammatory mediators, it is expected that inflammatory pathways are associated with its role in GDM. It has been speculated that cytokine-mediated inflammation leads to metabolic abnormalities by increasing insulin resistance in patients (35). Numerous pre-clinical and clinical studies support the notion that obesity-induced inflammation may be a specific type of inflammation resulting from overnutrition and stress pathways that drive abnormal metabolic homeostasis and lead to insulin resistance (36). It has been widely evidenced that obesity is a major cause of impaired insulin signaling (3739). However, the precise molecular mechanisms of the obesity-induced inflammation causing insulin resistance have remained to be elucidated.

As for focal adhesion, the present study revealed a hub function in the Path-Act-Net of upregulated DEGs. The focal adhesion pathway is closely associated with the insulin signaling pathway. Focal adhesion proteins are integrin-rich microdomains, which transmit mechanical signals from the ECM to activate signaling pathways inside the cell and structurally link the cytoskeleton to the ECM. Cell adhesion and focal adhesion kinases regulate insulin receptor substrate-1 expression (40). Focal adhesion kinase is a substrate for the insulin and insulin-like growth factor-1 tyrosine kinase receptors (4143). For instance, Bisht and Dey (43) reported that focal adhesion kinase regulates insulin resistance in skeletal muscle. Zhang et al (44) also reported that the focal adhesion pathway was of high significance in the functional interaction network of DEGs in adipose tissues of patients with insulin resistance.

The results of the KEGG and GO enrichment analyses suggested that antigen processing and presentation was significantly different in OVAT tissues of pregnant women with and without GDM, suggesting that immune responses are involved in GDM. This may be due to the fact that the OVAT tissues are under pathological stress and hypoxia, leading to immune-associated responses, such as the feto-matenal tolerance, innate immunity and inflammation response. Based on the previously suggested involvement of immune responses T1D, it is likely that it is also involved in the development and progression of GDM.

In conclusion, the results of the present study suggested that hub genes of the Gene-Act-Net, including ITGB1, AKT3, ITGAV and FGFR3, in OVAT are associated with GDM. Furthermore, molecular signaling pathways associated with immune responses, inflammatory processes, focal adhesion and signal transduction were identified to be closely associated with GDM in Chinese women. These candidate genes and pathways provided novel insight into the pathogenesis of insulin resistance in OVAT of GDM patents. However, the present study was limited by the low number of study subjects; furthermore, the differential expression of certain genes may have been a result, but not necessarily a cause of GDM. Further experimental studies are therefore required to confirm or expand the conclusions of the present study.

Acknowledgements

The present study was supported by the Natural Science Foundation of China (no. 81360103) and the Application of Basic Research Project of Yunnan Province (no. 2012FB039).

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May 2017
Volume 6 Issue 5

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APA
Qian, Y., Sun, H., Xiao, H., Ma, M., Xiao, X., & Qu, Q. (2017). Microarray analysis of differentially expressed genes and their functions in omental visceral adipose tissues of pregnant women with vs. without gestational diabetes mellitus. Biomedical Reports, 6, 503-512. https://doi.org/10.3892/br.2017.878
MLA
Qian, Y., Sun, H., Xiao, H., Ma, M., Xiao, X., Qu, Q."Microarray analysis of differentially expressed genes and their functions in omental visceral adipose tissues of pregnant women with vs. without gestational diabetes mellitus". Biomedical Reports 6.5 (2017): 503-512.
Chicago
Qian, Y., Sun, H., Xiao, H., Ma, M., Xiao, X., Qu, Q."Microarray analysis of differentially expressed genes and their functions in omental visceral adipose tissues of pregnant women with vs. without gestational diabetes mellitus". Biomedical Reports 6, no. 5 (2017): 503-512. https://doi.org/10.3892/br.2017.878