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Introduction

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder caused by hormone imbalance and often presents reproductive, metabolic and psychological syndromes (1). The reproductive syndromes mainly include ovulatory dysfunction, hyperandrogenism and polycystic ovaries, and the metabolic syndromes primarily include insulin resistance, obesity and dyslipidemia (2,3). The prevalence of PCOS is ~18% in women of productive age based on Rotterdam diagnostic criteria (4). To date, there is no cure for PCOS, and treatment and interventions tailored to clinical features are recommended. The etiology of PCOS is complex and remains largely unknown. Genetic and environmental factors are two primary contributors to the disorder (5). Exploration of the molecular mechanisms of PCOS will promote the understanding of its pathogenesis and has important implications for designing novel therapy.

Molecular mechanisms of PCOS have been increasingly investigated. It was recently found that hypoxia-inducible factor (HIF)-1α-mediated endothelin (ET)-2 signaling was suppressed in a PCOS mouse model and closely associated with the development of PCOS (6). Moreover, microarray and bioinformatic analysis have been utilized to unravel the molecular mechanisms of PCOS and yielded considerable results. For example, dysregulated circulating miRNAs have been predicted to be associated with several signaling pathways, such as immune, angiogenesis and p53 signaling in PCOS (7). In addition, there is evidence that Notch signaling and mitogen activated protein kinase (MAPK) pathways may be involved in the progression of PCOS (8). Furthermore, it has been suggested that the dysregulated genes between PCOS patients with and without insulin resistance may play roles in PCOS-related metabolic abnormalities and follicular growth arrest (9). Liu et al applied a sub-pathway method to identify candidate agents for PCOS treatment (10), and studied the transcription factor-microRNA synergistic regulatory network in PCOS (11) based on the transcript profile GSE34526. Additionally, this dataset was used by Bohler et al to collaborate the WikiPathways and Reactome as a new analysis tool of different omics datasets (12). Despite of these achievements, the molecular mechanisms of PCOS remain unclear.

It has been demonstrated that network-based data could offer an integrated view of the genes or proteins in the network and facilitate a better understanding of the molecular mechanisms linked to phenotypes of interest (13). Thus, the present study not only identified differentially expressed genes (DEGs), and DEG-related pathways in PCOS, but also constructed a Reactome function interaction (FI) network based on the interactions between DEGs. Moreover, pathway enrichment analysis was performed for the network modules extracted from the FI network. Furthermore, quantitative RT-PCR was used to detect expression of DEGs which may be important candidate genes in PCOS. The study may shed new light on the molecular mechanisms of PCOS.

Materials and methods

Preprocessing of microarray data

It was a secondary study of the microarray dataset GSE34526 (9) which was obtained from the Gene Expression Omnibus (GEO) database (14) (http://www.ncbi.nlm.nih.gov/geo/), and based on the Affymetrix Human Genome U133 Plus 2.0 Array platform (15). The microarray dataset consisted of 7 granulosa cell samples from 7 women with PCOS undergoing in vitro fertilization and 3 control granulosa cell samples from 3 normal women undergoing in vitro fertilization. For data preprocessing, the probe-level data in CEL files were converted into expression measures by using the affy package in R language (16), and then was subjected to background correction and quartile data normalization by using robust multiarray average (RMA) algorithm. Each probe was mapped to its corresponding gene using Biconductor annotation function (17) of R language. The probes corresponding to no gene or more than one gene were deleted. When there were several probes for one gene, the averaged expression value of these probes was used as the expression value of the gene. The standardized expression value is shown in a box figure (Fig. 1). It was depicted that the median gene expression value of normal samples is as high as that of PCOS samples, suggesting a marked degree of standardization of the data after preprocessing.

Figure 1 A boxplot of the gene expression profile across samples after preprocessing. Horizontal axis represents sample names; vertical axis represents gene expression value. Blue box stands for normal sample; pink box stands for polycystic ovary syndrome (PCOS) sample. Black horizontal line residing in the box stands for the median of the sample expression value. It shows that the median expression value of normal samples is as high as that of PCOS samples.

Determination and hierarchical clustering analysis of DEGs

Linear Models for Microarray Analysis package in R language (18) was employed to screen DEGs between PCOS samples and control normal samples. The strict thresholds were set at fold-change (|log2FC|) ≥1 and P-value <0.05. The screened DEGs underwent two-way hierarchical clustering analysis by using the pheatmap package (19) in R language (http://cran.fhcrc.org/web/packages/pheatmap/index.html).

Pathway enrichment analysis

In order to unveil the pathways that may be associated with the identified DEGs, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using ClueGO plug-in and CluePedia plugin of Cytoscape software. ClueGO plug-in (http://www.ici.upmc.fr/cluego/cluegoDownload.shtml) can extrapolate the biological function of large gene lists by identifying significant gene ontology (GO) terms and KEGG pathways, and functionally categorize the GO terms and KEGG pathways (20). The CluePedia plugin (http://www.ici.upmc.fr/cluepedia/) is used to search for pathway-associated markers and can offer an extensive view of a pathway by studying experimental information and in silico data (21). In this study, a right-side hypergeometric test was used for calculation of the P-value, followed by the multiple test correction [Benjamini-Hochberg adjustment (22)]. A pathway with adjusted P-value <0.05 was considered significant.

Based on the Kappa score threshold (≥0.4), these significant pathways were functionally divided into several groups and exhibited in a network, in which a node represented a KEGG pathway, and an edge between two nodes indicated that the two pathways shared common genes. Significant KEGG pathways enriched with the DEGs in the present study were visualized using R/Bioconductor package path-view which is a useful tool to map user data onto relevant pathways and offers graphs of the pathways mapped by the user data (23).

Construction of a Reactome FI network and analysis of the network modules

It has been shown that complicated disease phenotypes are better related to changes in networks, instead of a single gene or gene product (24). Therefore, a Reactome FI network was constructed with the DEGs by using ReactomeFIViz. ReactomeFIViz (http://wiki.reactome.org.sci-hub.org/index.php/Reactome_FI_Cytoscape_Plugin) is a Cytoscape app which can construct a Reactome FI network for pathway and network-based analysis of high throughout experimental data, and extract pathway and network patterns associated with diseases (13). In the network, nodes represent genes. An edge between two nodes stands for the interaction between two genes in the network. The edge weight corresponds to the Pearson's correlation coefficient between the two genes.

Network clustering analysis was then performed for the Reactome FI network by using Markov clustering algorithm (25), and highly connected network modules (min module size ≥7; average correlation ≥0.25) were extracted from the network. The hub gene in a module referred to the gene that had the most interactions. In order to identify association of the network modules with sample phenotype, the Pearson' correlation coefficient of the genes in each module to the sample phenotype (normal and PCOS samples) was calculated. The mean Pearson's correlation coefficient of all genes included in each module was regarded as the module significance. Moreover, pathway enrichment analysis was also performed for genes in each module using the reactomeFIVZ (P<0.05) based on the following publicly available databases: Reactome (R) database (26), KEGG (K), National Cancer Institute Pathway Interaction Database (NCI-PID) (N) (27), BioCarta in NCI-PID (B), and PantherDB (P). (28)

Quantitative RT-PCR

Expression of identified DEGs in granulosa cell samples was tested using quantitative RT-PCR. A total of 12 follicular fluid samples were collected for the experiment, including 5 samples from 5 normal women and 7 samples from 7 patients with PCOS, who were undergoing in vitro fertilization. Demographic and clinical characteristics of each subject were collected from medical records, including age, follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin (PRL), estradiol (E2), thyroid (T), fasting blood glucose (FBG), fasting plasma insulin (FINS), homeostasis model assessment of insulin resistance (HOMA-IR) levels, height, body weight, body mass index (BMI), and number of antral follicles (left and right).

The experiment was approved by the Ethics Committee of Obstetrics and Gynecology Hospital of Fudan University and informed consent was obtained before the use of the samples. Granulosa cells were extracted from the follicular fluid using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). cDNA was synthetized as recommended by the manufacturer using a reverse transcription kit (Takara, Tokyo, Japan). Thermal cycle profiles used in this study were: denaturing for 10 min at 95°C, annealing for 15 sec at 95°C, and extension for 10 sec at 72°C. PCR was carried out for 30 cycles. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal reference. The set of primers used in the study is shown in Table I.

Table I Primers used for the RT-PCR experiment in the study.

Table I

Primers used for the RT-PCR experiment in the study.

Gene	Primer sequences
MYH9	F: GCCAAGACCGTGAAGAAT
	R: CCAGACAGGAGATAATAGAAGA
LYN	F: TGAAGCCAGGAACTATGTC
	R: TGTACTCGGTGATGATGTAA
ARHGAP4	F: GGATGAGGTGGCTGAGAT
	R: GCTGGTCTGGAAGGAATC
ARHGAP9	F: GGACGCTGCTTCTACATAA
	R: GACATCATTGTTCCTCTTCAG
ACTB	F: TCATGAAGTGTGACGTGGACATC
	R: CAGGAGGAGCAATGATCTTGATCT
RHOG	F: CTGCTCATCTGCTACACAA
	R: CCACAGGTTCAGGTTCAC
GAPDH	F: TGACAACTTTGGTATCGTGGAAGG
	R: AGGCAGGGATGATGTTCTGGAGAG

[i] F, forward; R, reverse.

Statistical analysis

SPSS version 19.0 (IBM Corp., Armonk, NY, USA) was used for data analysis. Continuous variables between two groups were compared using Student's t-test. ANOVA was used to analyze the differences between 3 groups, followed by pairwise comparison using least significant difference test. Differences with P-value <0.05 were considered significant.

Results

Identification and hierarchical clustering analysis of DEGs

Between PCOS and control samples, 674 DEGs were screened, including 506 upregulated genes and 168 downregulated genes. The result of the hierarchical clustering analysis of the DEGs is exhibited in a heatmap plot (Fig. 2). The majority of genes in the PCOS samples was upregulated as compared to those in the control samples.

Figure 2 A heatmap plot of the differentially expressed genes (DEGs) of all samples. Horizontal axis represents sample name; vertical axis represents the fold-change of DEG expression. The red denotes expression of upregulated DEGs; the green denotes expression of downregulated DEGs. Color bar above indicates sample type: pink bar for polycystic ovary syndrome (PCOS) samples and blue bar for normal samples.

Pathway enrichment analysis

KEGG pathway enrichment analysis was performed for the DEGs with a view to determining which pathways possibly involved the obtained DEGs. Collectively, 41 pathways were significantly enriched with the DEGs. As shown in Fig. 3, 30 pathways were functionally categorized into 5 groups based on the Kappa score (≥0.4). The other 11 pathways were not functionally related to other pathways. These significant pathways were primarily associated with immune and inflammation. Each significant pathway was visualized by the Pathview software. One example was the Cytokine-cytokine receptor interaction pathway (adjusted P=1.67E−05) (Fig. 4) which was enriched with 31 genes, such as chemokine (C-X-C motif) ligand (CXCL) subfamily members (CXCL3, CXCL6, CXCL10, CXCL16 and CXCL4) and chemokine (C-C motif) ligand (CCL) subfamily members (CCL2, CCL8, CCR5, CCR7 and CCR1).

Figure 3 Grouping of significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the differentially expressed genes (DEGs). A node represents a KEGG pathway, and the node size is negatively correlated with the adjusted P-value of the pathway. An edge between two nodes denotes that the two pathways share common genes. The edge width is positively correlated with the number of common genes. Significant KEGG pathways of DEGs are classified into several functional groups based on κ value. Nodes of a group are labeled in the same color. Grey nodes do not share genes with other pathways. The nodes of two colors are shared by two groups.

Figure 4 Pathview of the Cytokine-cytokine receptor interaction pathway. Node color change reflects log fold-change (FC) range.

Analysis of the Reactome FI network and the network modules

For the purpose of evaluating the associations between the identified DEGs, a Reactome FI network was constructed with the DEGs, and 8 highly connected network nodules were extracted from the network (Table II). Fig. 5 shows that among the 8 modules, module 0 had the largest size, including 13 genes with hub gene RAS-related C3 botulinum substrate 2 (RAC2). Both module 1 and 2 had 11 genes. The hub genes were actin-β (ACTB) and Fc fragment of IgG, low affinity IIc, receptor for (CD32) (FCGR2C), respectively. Additionally, 10 genes were included in module 3 with LYN proto-oncogene, Src family tyrosine kinase (LYN) as the hub genes.

Figure 5 Eight network modules extracted from the Reactome function interaction network. A node stands for a differentially expressed gene (DEG); the link between two nodes reflects the interaction of the two DEGs. Nodes of one module are shown in the same color. The node that has the most links in a module is defined as the hub gene of the module.

Table II Components of 8 network modules.

Table II

Components of 8 network modules.

Module	No of genes	Average correlation	Gene list
Module 0	13	0.8196	ARAP1, ARHGAP26, ARHGAP4, ARHGAP9, CORO1A, HMHA1, NCKAP1L, PIP5K1B, PKN1, PREX1, RAC2, RHOG, STARD8
Module 1	11	0.8695	ACTB, ARPC1B, CFL2, COL8A2, EPB41L3, MYH9, MYO1G, P2RX7, PARVG, PSTPIP1, ZYX
Module 2	11	0.8501	BLNK, CD72, CLEC7A, FCGR2B, FCGR2C, LILRA2, LILRA5, LILRA6, OSCAR, SLA, SYK
Module 3	10	0.8006	CD24, CD53, DAB2, FCER1G, FGR, HCK, LPAR3, LYN, SHC4, TNFRSF11A
Module 4	8	0.8748	COL15A1, FERMT3, FLNA, ICAM3, ITGB2, RASSF5, SELPLG, TGFBI
Module 5	8	0.7448	CXCL16, CXCL3, CXCL6, GNAI2, NPY2R, NUCB1, P2RY13, SUCNR1
Module 6	8	0.6387	AP1S2, HLA-DMA, HLA-DPA1, HLA-DQA1, HLA-DQB1, HLA-DRA, LGMN, RACGAP1
Module 7	7	0.6262	CD14, CD180, LY86, LY96, TLR1, TLR4, TNFRSF1B

[i] Average correlation: average absolute edge weight of edges in a module.

The module significance to sample phenotype (normal and PCOS samples) was calculated to explore the degree of the association of each module with sample phenotype. As shown in Fig. 6, the module significance ranged from 0.702 to 0.81. This suggested that these modules were well associated with the sample phenotype. Module 3 had the highest module significance (0.81).

Figure 6 Module significance. Horizontal axis indicates module name; vertical axis indicates module significance. The mean Pearson's correlation coefficient of all genes in each module to sample phenotype is defined as module significance.

Pathway enrichment analysis was also performed for the genes in each module. Top 5 significant pathways for each network module were identified based on the P-value (Table III). Module 0 was significantly enriched with the Regulation of RhoA activity, Signaling by Rho GTPases, Regulation of RAC1 activity, GPVI-mediated activation cascade and RhoA signaling pathways. Rho GTPase activating protein (ARHGAP)4 and ARHGAP9 are involved in the Regulation of RhoA activity and Signaling by Rho GTPase pathways. In addition, Ras homolog family member G (RHOG) was related to Signaling by Rho GTPases and GlycoProtein (GP)VI-mediated activation cascade pathways. Module 1 was associated with the Regulation of actin cytoskeleton, Salmonella infection, Nicotinic acetyl-choline receptor signaling, and Tight junction pathways. ACTB and myosin, heavy chain 9, non-muscle (MYH9) were enriched in the 4 pathways. Module 2 was related to the Tuberculosis, Osteoclast differentiation, B cell receptor signaling, BCR signaling and Phagosome signaling pathways. Module 3 was related to Thromboxane A2 receptor signaling, Signaling events mediated by PTP1B, Chemokine signaling, CXCR4-mediated signaling events and ephrin B reverse signaling pathways. LYN, hub gene of module 3 was enriched in each of these pathways. Notably, module 6 was related to MHC class II antigen presentation pathway which was enriched with several human leukocyte antigen (HLA) genes, such as HLA-DQB1, HLA-DPA1, HLA-DMA, HLA-DQA1 and HLA-DRA. Module 7 was linked to Toll-like receptor-related pathways.

Table III Top 5 significant KEGG pathways for each network module.

Table III

Top 5 significant KEGG pathways for each network module.

Module	Pathway	No. of genes.	P-value	Gene list
Module 0	Regulation of RhoA activity	3	0	ARHGAP4, ARAP1, ARHGAP9
Module 0	Signaling by Rho GTPases	8	0	ARHGAP26, ARHGAP4, HMHA1, RAC2, STARD8, ARAP1, RHOG, ARHGAP9
Module 0	Regulation of RAC1 activity	2	0.0012	PREX1, ARHGAP9
Module 0	GPVI-mediated activation cascade	2	0.0015	RAC2, RHOG
Module 0	RhoA signaling pathway	2	0.0016	PIP5K1B, PKN1
Module 1	Regulation of actin cytoskeleton	4	0	ACTB, ARPC1B, CFL2, MYH9
Module 1	Salmonella infection	3	0.0001	ACTB, ARPC1B, MYH9
Module 1	Nicotinic acetylcholine receptor signaling pathway	2	0.0002	ACTB, MYH9
Module 1	Tight junction	3	0.0003	ACTB, EPB41L3, MYH9
Module 1	Integrin signaling pathway	3	0.0005	ACTB, ARPC1B, COL8A2
Module 2	Tuberculosis	4	0	FCGR2B, FCGR2C, CLEC7A, SYK
Module 2	Osteoclast differentiation	8	0	FCGR2B, LILRA2, FCGR2C, OSCAR, LILRA5, LILRA6, BLNK, SYK
Module 2	B cell receptor signaling pathway	4	0	FCGR2B, CD72, BLNK, SYK
Module 2	BCR signaling pathway	4	0	FCGR2B, CD72, BLNK, SYK
Module 2	Phagosome	3	0.0006	FCGR2B, FCGR2C, CLEC7A
Module 3	Thromboxane A2 receptor signaling	3	0	FGR, LYN, HCK
Module 3	Signaling events mediated by PTP1B	3	0	FGR, LYN, HCK
Module 3	Chemokine signaling pathway	4	0	FGR, LYN, HCK, SHC4
Module 3	CXCR4-mediated signaling events	3	0	FGR, LYN, HCK
Module 3	Ephrin B reverse signaling	3	0	FGR, LYN, HCK
Module 4	β2 integrin cell surface interactions	3	0	ICAM3, TGFBI, ITGB2
Module 4	Cell adhesion molecules (CAMs)	3	0.0002	ICAM3, ITGB2, SELPLG
Module 4	Integrin signaling pathway	3	0.0002	COL15A1, ITGB2, FLNA
Module 4	amb2 integrin signaling	2	0.0003	ITGB2, SELPLG
Module 4	Extracellular matrix organization	3	0.0008	ICAM3, COL15A1, ITGB2
Module 5	GPCR ligand binding	6	0	P2RY13, CXCL16, CXCL3, NPY2R, SUCNR1, CXCL6
Module 5	Chemokine signaling pathway	4	0	GNAI2, CXCL16, CXCL3, CXCL6
Module 5	GPCR downstream signaling	7	0	P2RY13, GNAI2, CXCL16, CXCL3, NPY2R, SUCNR1, CXCL6
Module 5	Cytokine-cytokine receptor interaction	3	0.0006	CXCL16, CXCL3, CXCL6
Module 5	Pertussis	2	0.0012	GNAI2, CXCL6
Module 6	Epstein-Barr virus infection	4	0	HLA-DQB1, HLA-DPA1, HLA-DQA1, HLA-DRA
Module 6	MHC class II antigen presentation	8	0	HLA-DQB1, AP1S2, LGMN, HLA-DPA1, RACGAP1, HLA-DMA, HLA-DQA1, HLA-DRA
Module 6	Viral myocarditis	5	0	HLA-DQB1, HLA-DPA1, HLA-DMA, HLA-DQA1, HLA-DRA
Module 6	Staphylococcus aureus infection	5	0	HLA-DQB1, HLA-DPA1, HLA-DMA, HLA-DQA1, HLA-DRA
Module 6	Autoimmune thyroid disease	5	0	HLA-DQB1, HLA-DPA1, HLA-DMA, HLA-DQA1, HLA-DRA
Module 7	Pertussis	3	0	LY96, TLR4, CD14
Module 7	NF-κB signaling pathway	3	0	LY96, TLR4, CD14
Module 7	Toll-like receptors cascades	6	0	LY96, LY86, TLR1, TLR4, CD14, CD180
Module 7	Pathogenic Escherichia coli infection	3	0	LY96, TLR4, CD14
Module 7	Toll-like receptor signaling pathway	4	0	LY96, TLR1, TLR4, CD14

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes.

RT-PCR analysis

Bioinformatic analysis showed that ARHGAP4, ARHGAP9, RHOG, ACTB, MYH9 and LYN were upregulated DEGs in PCOS samples relative to normal samples. In order to verify these results, quantitative RT-PCR was used to measure the mRNA expression of these genes in granulosa cell samples of PCOS patients and normal controls. With regard to demographic and clinical characteristics (Table IV), PCOS patients and normal controls were not significantly different in regards to age, FSH, LH, PRL, E2, T and height. Yet, PCOS patients had markedly increased FBG, FINS, HOMA-IR, body weight, BMI and number of antral follicles. As shown in Fig. 7, ARHGAP4, ARHGAP9, RHOG and LYN at the mRNA level were significantly increased in PCOS compared to levels in the normal controls (P<0.05). Although increased mRNA expression of ACTB and MYH9 was also observed in PCOS, the difference was not significant (P>0.05).

Figure 7 mRNA levels of ARHGAP4, ARHGAP9, RHOG, ACTB, MYH9 and LYN in granulosa cell samples of polycystic ovary syndrome (PCOS) patients and normal controls as detected using RT-PCR. *P<0.05 compared to control. PCOS, polycystic ovary syndrome; ARHGAP, Rho GTPase activating protein; RHOG, Ras homolog family member G; ACTB, actin-β; MYH9, myosin, heavy chain 9, non-muscle; LYN, LYN proto-oncogene, Src family tyrosine kinase.

Table IV Demographic and clinical characteristics of controls and PCOS patients for PCR analysis.

Table IV

Demographic and clinical characteristics of controls and PCOS patients for PCR analysis.

Variable	Control group (n=5)	PCOS group (n=7)	P-value
Age (years)	27.000±2.121	28.857±4.670	0.379
FSH (mIU/ml)	5.438±0.514	5.267±0.404	0.532
LH (mIU/ml)	4.196±0.641	4.174±1.988	0.982
PRL(ng/ml)	14.988±2.296	19.986±4.676	0.054
E2 (pg/ml)	29.200±8.044	32.857±13.031	0.593
T (ng/ml)	0.268±0.040	0.294±0.157	0.725
FBG (mmol/l)	5.200±0.158	5.443±0.288	0.092
FINS (mIU/l)	5.980±0.858	10.829±2.780	0.004
HOMA-IR	1.380±0.191	2.637±0.759	0.005
Height (m)	1.584±0.054	1.607±0.049	0.466
Body weight (kg)	54.000±8.602	70.286±9.656	0.013
BMI	21.467±2.704	27.204±3.372	0.011
Number of antral follicles (L)	6.800±1.304	21.714±5.851	<0.001
Number of antral follicles (R)	7.800±1.304	25.714±2.870	<0.001

[i] Differences between two groups were analyzed using Student's t-test. P-values marked in bold are <0.05. PCOS, polycystic ovary syndrome; FSH, follicle-stimulating hormone; LH, luteinizing hormone; PRL, prolactin; E2, estradiol; T, thyroid; FBG, fasting blood glucose; FINS, fasting plasma insulin; HOMA-IR, homeostasis model assessment of insulin resistance; BMI, body mass index.

Furthermore, the 7 PCOS samples were divided into two groups: PCOS non-IR (n=3) and PCOS IR (n=4) groups. The demographic and clinical characteristic analysis revealed that LH and E2 were significantly decreased, and FINS, HOMA-IR and BMI were significantly increased in PCOS IR patients relative to PCOS non-IR patients (Table V). Results of RT-PCR found that the PCOS IR group had markedly elevated expression of ARHGAP4, ARHGAP9, RHOG and LYN when compared to these parameters in the PCOS non-IR group (Fig. 8).

Figure 8 mRNA levels of ARHGAP4, ARHGAP9, RHOG, ACTB, MYH9 and LYN in granulosa cell samples of polycystic ovary syndrome (PCOS) non-IR patients, PCOS IR patients and normal controls as detected using RT-PCR. *P<0.05 compared to the control; #P<0.05 compared to PCOS non-IR patients. IR, insulin, resistance.

Table V Demographic and clinical characteristics of controls, PCOS non-IR patients and PCOS IR patients for PCR analysis.

Table V

Demographic and clinical characteristics of controls, PCOS non-IR patients and PCOS IR patients for PCR analysis.

Variable	Control group (n=5)	PCOS non-IR group (n=3)	PCOS IR group (n=4)	F-value	P-value
Age (years)	27.000±2.121	30.000±5.657	28.400±4.879	0.425	0.666
FSH (mIU/ml)	5.438±0.514	5.705±0.092	5.092±0.330	1.866	0.210
LH (mIU/ml)	4.196±0.641	6.560±0.750a	3.220±1.343b	7.615	0.012
PRL (ng/ml)	14.988±2.296	17.260±1.485	21.076±5.200	3.206	0.089
E2 (pg/ml)	29.200±8.044	50.000±4.243a	26.000±6.671b	8.528	0.008
T (ng/ml)	0.268±0.040	0.450±0.212	0.232±0.093	3.651	0.069
FBG (mmol/l)	5.200±0.158	5.200±0.000	5.540±0.288	3.512	0.075
FINS (mIU/l)	5.980±0.858	7.500±1.980	12.160±1.689a,b	24.516	<0.001
HOMA-IR	1.380±0.191	1.733±0.458	2.999±0.490a,b	23.613	<0.001
Height (m)	1.584±0.054	1.635±0.007	1.596±0.055	0.709	0.517
Body weight (kg)	54.000±8.602	61.000±7.071	74.000±8.185a	7.439	0.012
BMI	21.467±2.704	22.808±2.448	28.962±1.425a,b	15.581	0.001
Number of antral follicles (L)	6.800±1.304	23.000±9.899a	21.200±5.070a	14.163	0.002
Number of antral follicles (R)	7.800±1.304	25.500±0.707	25.800±3.493a	75.092	<0.001

{ label (or @symbol) needed for fn[@id='tfn5-ijmm-40-05-1385'] } ANOVA is applied to analyze the differences between 3 groups, followed by pairwise comparison using least significant difference test. P-values marked in bold are <0.05.

a P<0.05 compared to the control group;

b P<0.05 compared to the PCOS non-IR group. PCOS, polycystic ovary syndrome; FSH, follicle-stimulating hormone; LH, luteinizing hormone; PRL, prolactin; E2, estradiol; T, thyroid; FBG, fasting blood glucose; FINS, fasting plasma insulin; HOMA-IR, homeostasis model assessment of insulin resistance; BMI, body mass index.

Discussion

PCOS is a complex disease characterized by variable clinical features. It is largely ascribed to hormone imbalance and results in a heavy health and economic burden. The present study identified a list of genes and pathways that may be involved in the pathogenesis of PCOS by a series of microarray analyses. A total of 674 DEGs were screened between PCOS and control samples. Pathway enrichment analysis revealed significant enrichment of 41 pathways with the DEGs. These pathways were predominately related to immune and inflammation. In line with the finding, it has been reported that inflammation is enhanced, and production of interleukins and chemokines is increased in PCOS (29,30). Our result confirms the crucial role of inflammation and immune in the development of PCOS.

In the present study, 8 highly connected network modules were extracted from the Reactome FI network. Among these network modules, module 3 had the highest module signifi-cance. LYN was the hub gene of module 3. Notably, microarray analysis revealed that LYN was an upregulated DEG in PCOS, which was in accordance with the result of RT-PCR. LYN encodes tyrosine-protein Lyn which is a member of the src family tyrosine kinase. There is in vivo evidence that Src family kinases are essential for generation of the inflammatory environment (31). Furthermore, research has established that Lyn plays both a positive and a negative regulatory role in neutrophils and macrophages (32). Lyn plays a prominent role in the activation of innate immune response mediated by nuclear factor-κB in human mononuclear cells (33). These findings suggest that Lyn is closely related to inflammation and immune response. In the present study, pathway analysis for module 3 revealed that LYN was significantly enriched in the Inflammation and immune-related TXA2 receptor signaling, Chemokine signaling, and CXCR4-mediated signaling events pathways, which was in concordance with previous findings. Dysregulated LYN may participate in PCOS-related inflammation and immune response. Furthermore, inflammation is an important mechanism underlying insulin resistance (34), which is prevalent in PCOS patients (35). By using RT-PCR, the study found that LYN expression was significantly increased in PCOS IR patients compared with PCOS non-IR patients. This indicates that upregulated LYN may be associated with PCOS-related insulin resistance as well.

Pathway enrichment analysis revealed that module 0 was significantly associated with Regulation of RhoA activity, Signaling by Rho GTPases and RhoA signaling pathways. RhoA is a member of Rho family of GTPases involved in regulating intracellular actin dynamics (36). It has been demonstrated that RhoA participates in regulating secretion of insulin (37), and is associated with insulin resistance via phosphorylation of insulin receptor substrate-1 (38). Moreover, it also mediates adrenocorticotropin-stimulated cortisol biosynthesis in human adrenocortical cells (39). These observations indicate that Regulation of RhoA activity, Signaling by Rho GTPases and RhoA signaling pathways may be associated with PCOS-related hormone imbalance, in particular insulin resistance. Furthermore, the present study also found that Regulation of RhoA activity and Signaling by Rho GTPases pathways shared 2 DEGs: ARHGAP4 and ARHGAP9. They are two members of the Rho-GAP family of GTPase activating proteins participating in the regulation of the function of Rho GTPases (40). Moreover, results of the microarray analysis and RT-PCR reached an agreement that ARHGAP4 and ARHGAP9 were significantly upregulated in PCOS. It has been speculated that dysregulated ARHGAP4 and ARHGAP9 may play a role in PCOS-related hormone imbalance, particular insulin resistance, by regulating Regulation of RhoA activity and Signaling by Rho GTPases pathways. Consistently, the study found that expression of ARHGAP4 and ARHGAP9 were markedly increased in PCOS IR patients relative to PCOS non-IR patients by using RT-PCR.

RhoG encoded by gene RHOG is a small G protein and belongs to the Rac subfamily of the Rho family. Recently, it has been reported that RhoG protein may be implicated in glycoprotein VI-Fc receptor γ-chain complex-mediated platelet activation (41). In agreement with the finding, the study found that RHOG in module 0 was significantly enriched in the GP VI-mediated activation cascade pathway. Furthermore, it has been established that impaired platelet function plays a part in the pathogenesis of PCOS (42). These observations indicate that RHOG and GP VI-mediated activation cascade pathway may be implicated in PCOS-related platelet dysfunction. Moreover, the study found an obviously increased expression of RHOG in PCOS IR patients relative to PCOS non-IR patients. Platelets are important players in inflammation (43), which is a critical mechanism of insulin resistance. Upregulated RHOG may play a role in insulin resistance by affecting platelet dysfunction.

The study found that ACTB and MYH9 in module 1 were upregulated DEGs in PCOS, and significantly related to the Regulation of actin cytoskeleton and Tight junction pathways. These pathways and genes are related to cytoskeleton formation. However, RT-PCR measurement of the two genes revealed that the differences in mRNA expression of ACTB and MYH9 did not reach significance between PCOS and normal samples. This suggests that results of the two genes may be false-positive results of the microarray data analysis. Nevertheless, the abnormal expression of cytoskeletal proteins has been reported in PCOS (44,45). Therefore, more studies are needed to clarify the role of ACTB and MYH9 in PCOS.

HLA genes encode antigen-presenting proteins located on the cell surface in the human body. There is evidence that possession of HLA-DQA1*0501, HLA-A11 and HLA-DRB1*0403 are risk factors of PCOS (46,47). This study also found that the HLA genes were closely associated with PCOS, as evidenced by the fact that a group of HLA genes (HLA-DQB1, HLA-DPA1, HLA-DMA, HLA-DQA1 and HLA-DRA) were included in module 6 and enriched in MHC class II antigen presentation pathway. Moreover, serum HLA-G in women with PCOS has been reported to be associated with oxidative stress, ovarian hyperandrogenism and insulin resistance (48). It is speculated that HLA-DQB1, HLA-DPA1, HLA-DMA, HLA-DQA1 and HLA-DRA may play a role in PCOS-related oxidative stress, ovarian hyperandrogenism and insulin resistance by affecting the MHC class II antigen presentation pathway. In the present study, genes in module 7 were linked to Toll-like receptor-related pathways. Toll-like receptors are important participants of the immune system. Toll-like receptor 3 affects β-cell insulin secretion and glucose homeostasis (49), and activation of Toll-like receptor signaling pathways aid in inducing insulin resistance (50). These findings lead to a speculation that Toll-like receptor signaling pathways are possibly implicated in PCOS-related insulin resistance and immune abnormality.

The study has some limitations. Firstly, its sample size is small. Follicular fluid samples are not easy to collect and many patients may not agree with the study. Secondly, the microarray dataset GSE34526 did not provide clear information on whether the PCOS samples in the dataset were resistant to insulin or not, thus differentially expressed genes between PCOS IR and the PCOS non-IR samples were not distinguished. Thirdly, it is necessary to validate the results of microarray data analysis using experimental methods. In this preliminary study, expression of 6 DEGs in granulosa cell samples of PCOS patients and normal controls were measured using RT-PCR. Further studies with more experiments, such as western blotting, and a large cohort of samples are in process to verify and extend the findings of the present study.

In conclusion, the present study confirmed the important role of inflammation and immune in PCOS. The Regulation of RhoA activity, Signaling by Rho GTPases, and GP VI-mediated activation cascade pathways may be associated with PCOS-related hormone imbalance and platelet dysfunction. LYN, ARHGAP4, ARHGAP9 and RHOG are promising candidate genes in PCOS, and may be recommended as possible therapeutic targets for PCOS. Further experimental studies are warranted to verify the results of the present study.

Acknowledgments

The present study was supported by the Shanghai Municipal Commission of Health and Family Planning (grant no. 201540214), and accomplished in Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases.

References