Integrated Bioinformatic Analysis Reveals the Gene Signatures, Epigenetic Roles, and Regulatory Networks in Endometriosis

doi:10.21203/rs.3.rs-4923357/v1

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Integrated Bioinformatic Analysis Reveals the Gene Signatures, Epigenetic Roles, and Regulatory Networks in Endometriosis

https://doi.org/10.21203/rs.3.rs-4923357/v1

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Objectives: Endometriosis is a common gynecological disease with a significant economic burden. Growing evidence has suggested the role of aberrant gene expression and epigenetic mechanisms in the pathogenesis of endometriosis. This study aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using an integrated bioinformatic approach.

Methods: Six microarray and RNA-sequencing datasets (GSE23339, GSE7305, GSE25628, GSE51981, GSE120103, GSE87809) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of each dataset were analyzed using the GEO2R tool, and their mRNA, miRNA, and lncRNA components were identified subsequently. The common DEGs between datasets were combined, and the Gene ontology (GO) and pathway enrichment were analyzed using the ShinyGo. The protein-protein interaction (PPI) network of differentially expressed genes, miRNA, and lncRNA was constructed using STRING and Cytoscape, then the top 15 hub genes in the PPI network were identified using the CytoHubba.

Results: A total of 551 common DEGs were identified among four or more studies, including 292 upregulated and 259 downregulated genes. Besides alterations in protein-coding genes (mRNA), 16 miRNA were identified from all studies, along with 12 lncRNA that were common in at least three studies. Enriched DEGs were mainly associated with extracellular matrix (ECM) interaction, P53 signaling pathway, and focal adhesion, which are suggested to play vital roles in the pathogenesis of endometriosis. Through PPI network construction of common DEGs, 178 nodes and 683 edges were obtained, from which 15 hub genes were identified, including CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG.

Conclusions: Our in-depth bioinformatics analysis reveals the critical molecular basis underlying endometriosis. The identified hub genes, miRNA, and lncRNA may also serve as potential biomarkers to predict the occurrence and prognosis of endometriosis.

Obstetrics & Gynecology

Bioinformatic

Differentially expressed genes

endometriosis

lncRNA

miRNA

Endometriosis is a systemic and multifactorial disease with most gynecological afflictions, affecting the quality of life of women in reproductive age [1, 2]. Histologically, it is defined by the presence of endometrial-like tissue outside the uterus, which involves a complex interaction between pain, hormonal dependence, ovarian function disruption, and compromised endometrial receptivity [1, 3]. The estimated prevalence of endometriosis varied from 0.2–71.4%, as the heterogeneity of diagnostic and selection bias overwhelmingly accounts for its variability [4].

Despite advancements in contemporary medicine, there is no common ground in understanding the pathogenesis of endometriosis [2]. Its pathogenomic architecture comprises complex genetic and epigenetic features, including gene polymorphisms, interactions of gene nets and functional protein modules, and different metabolic pathways that are altered by biological process imbalances [5]. These variations are responsible for the aberrant modulation of steroidogenesis, inflammation, apoptosis, adhesion, angiogenesis, proliferation, and hormone signaling. Therefore, determining the molecular basis of endometriosis may encourage early detection, better disease management, and a new approach to treatment strategies [6, 7].

Genetic studies provide a critical way to better understand the process behind the development of endometriosis as a debilitating illness [8]. In the last decade, advances in high-throughput genotyping technology and DNA sequencing have made significant progress in discovering the gene signatures of endometriosis. In the recent meta-analysis, these cutting-edge methods reported a 2% and 5% variation among all cases and severe cases of endometriosis, respectively [9]. However, defining potential genes for endometriosis is difficult due to a limited understanding of etiopathogenetic mechanisms, which may involve multiple genes and pathways. Numerous variables have also raised consequential concerns about candidate gene research, including study power, effect size, negative results bias, statistical and technological challenges, and experimental design flaws [8, 10]. The present study therein aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using a non-biased bioinformatic approach and a large sample from six combined studies.

Data extraction

Gene expression datasets of endometriosis were collected from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/). Studies on endometriosis-associated gene expression alterations were searched using the terms “Endometriosis” and “Homo Sapiens”. The inclusion criteria were as follows: (1) studies evaluating ectopic or eutopic tissue of endometriosis; (2) studies containing at least four cases and four normal controls (non-endometriosis); and (3) studies providing publicly available annotation data in the GEO database. Six studies were subjected to data collection and analysis, including GSE23339 [11], GSE7305 [12], GSE25628 [13], GSE51981 [14], GSE120103 [15], GSE87809 [16] (Table 1). All datasets were analyzed with the GEO2R tool, available at the GEO repository. The samples were divided into endometriosis vs control groups, and the following settings were applied: P-value adjustment was performed using the Benjamini & Hochberg with a significance cutoff of 0.05, and no forces normalization or log transformation was applied. The study’s flow chart is presented in Fig. 1.

Table 1

Characteristics of studies selected for bioinformatic analysis
Study	Dataset	Samples (n)		Tissue	Type of endometriosis	Protocol design
Study	Dataset	Total	Group	Tissue	Type of endometriosis	Protocol design
Hawkins et al. [11]	GSE23339	19	10 EN, 9 N-EN	Ovary and endometrial	Ovarian endometrioma	Illumina’s Human WG-6 2.0 arrays
Hever et al. [12]	GSE7305	20	10 EN, 10 N-EN	Ovary	Ovarian endometrioma	Affymetrix HU133 Plus 2.0 arrays
Crispi et al. [13]	GSE25628	13	7 EN, 6 N-EN	Ectopic endometrial	Deep filtrating endometriosis	Affymetrix HGU133A 2.0 arrays
Tamaresis et al. [14]	GSE51981	65	43 EN, 22 N-EN	Endometrial	-	Affymetrix HU133 Plus 2.0 arrays
Bhat et al. [15]	GSE120103	15	9 EN, 9 N-EN	Endometrial	Ovarian endometrioma	Illumina HiSeq2500 RNA-seq
Yotova et al. [16]	GSE87809	9	4 EN, 5-N-EN	Ovary	Ovarian endometrioma	Illumina HiSeq2000 RNA-seq
EN individuals with endometriosis, N-EN individuals without endometriosis

Identification of DEGs

Differentially expressed genes (DEGs) were analyzed between the endometriosis and normal endometrium tissues in each dataset with a determined |log2FC| > 1 and the P-value < 0.05. After removing the inconsistent DEGs, the overlapped genes of all datasets were analyzed using InteractiVenn (http://www.interactivenn.net/) to find the common DEGs. In addition, the SR Plot tool (https://bioinformatics.com.cn/plot_basic_cluster_heatmap_plot_024_en) was employed to generate the fold-change heatmap of the shared DEGs under the following settings: Euclidean distance method, complete cluster method, and bidirectional cluster orientation.

Identification of mRNA, miRNA, and lncRNA

The ShinyGO (http://bioinformatics.sdstate.edu/go/) online tool was used to identify the RNA components (mRNA, lncRNA, miRNA) of each dataset. After removing the inconsistent data, the shared miRNA and lncRNA of all datasets were integrated using InteractiVenn (http://www.interactivenn.net/). To generate the miRNA network, miRNA and their respective targets were identified using the MIENTURNET online tools (http://userver.bio.uniroma1.it/apps/mienturnet/). Furthermore, analysis of lncRNA and their targets was performed using the LncRRIsearch (http://rtools.cbrc.jp/LncRRIsearch/) with a determined energy threshold of -20 kcal/mol. The targets with a P-value < 0.05 was considered significant and subjected to further analysis.

Functional and pathway enrichment analysis

Gene ontology (GO) and Kyoto Encyclopaedia Genes and Genomes (KEGG) pathway enrichment analysis were performed using The ShinyGO (http://bioinformatics.sdstate.edu/go/). The GO terms, including Biological process (BP), Cellular components (CC), and Molecular function (MF) of common DEGs, were ranked according to the fold enrichment and the P value in each term. The results were then presented as bubble charts generated by the SR Plot (https://bioinformatics.com.cn/en?keywords=bubble).

Network construction and hub genes analysis

The STRING database (http://string-db.org/) was used to predict the protein-protein interaction (PPI) between the common DEGs, with the confidence interaction score ≥ 0.9. After removing the unconnected nodes, the network obtained from the STRING was downloaded and analyzed. All the interaction networks between common DEGs, miRNA-targets, and lncRNA-targets were visualized using the Cytoscape software version 3.10 (https://cytoscape.org/). Additionally, the CytoHubba plugin of the Cytoscape was employed to screen the top 15 hub genes of the PPI network using the degree topology method.

Included studies

A total of 6 studies, including GSE23339 [11], GSE7305 [12], GSE25628 [13], GSE51981 [14], GSE120103 [15], and GSE87809 [16], were selected for data extraction based on the mentioned inclusion criteria. The collective number of endometriosis and control subjects from all datasets was 83 and 61, respectively. The studies were conducted either by microarray (N = 4) or RNA-sequencing (N = 2) and predominantly evaluated stage IV of ovarian endometriosis. Based on the study design, the whole dataset was analyzed only in three studies, as the subjects in the remaining studies were grouped based on several criteria, including severity, fertility status, or tissue source. Notably, moderate/severe endometriosis, fertile patients, and ectopic tissue were selected from those remaining studies. Detailed information on all studies is shown in Table 1.

Differentially expressed genes in endometriosis

We independently analyzed the endometriosis-associated DEGs from each study, which varied from 1213 to 4680 genes. The RNA components were primarily identified as protein-coding genes (mRNA), marking ~ 96.3% of the DEGs in all studies. Moreover, about 0.1% and 2.8% altered genes corresponded as miRNA and lncRNA, indicating the particular role of this regulatory RNA in endometriosis pathogenesis. DEGs component in each study is presented in Table 2.

Table 2

RNA compositions of DEGs present in each study
Dataset	mRNA		lncRNA		miRNA		Total
Dataset	N	%	N	%	N	%	Total
GSE23339	1213	100%	0	0%	0	0	1213
GSE7305	1551	97.7%	33	2.07%	1	0.06%	1585
GSE25628	2796	99.6%	10	0.35%	1	0.035%	2807
GSE51981	4317	98.7%	52	1.18%	1	0.02%	4370
GSE120103	4447	94.9%	232	4.95%	1	0.02%	4680
GSE87809	2201	90.7%	212	8.74%	12	0.49%	2425
mRNA messenger RNA, lncRNA long noncoding RNA, miRNA microRNA

We further analyzed the overlapping DEGs from all datasets, resulting in 551 common DEGs among four or more studies (Fig. 2A). The combined log fold change from these common DEGs yielded 292 upregulated and 259 downregulated genes associated with endometriosis. The expression pattern of the shared DEGs between studies is presented by a heatmap graph in Fig. 3.

miRNA and lncRNA expression altered in endometriosis

In total, we found 16 differentially expressed miRNAs in five studies, from which the GSE87809 [16] dataset marked the highest number of alterations (Table 2, Fig. 2B). The targets of these miRNAs were identified using MIENTURNET online tools. Furthermore, the top 5 enriched miRNAs with at least three interactions revealed ~ 60 target genes, which were then used to construct the miRNAs-target network (Fig. 7). Those five enriched miRNAs are hsa-miR-25, hsa-miR-503, hsa-miR-302b, hsa-miR-424, and hsa-miR-10a.

Alteration of gene expressions was also observed in 539 lncRNAs, including 12 lncRNA that were common to at least in 3 studies (Table 2, Fig. 2C). The targets of these miRNAs were identified using lncRRIsearch database, resulting in ~ 202 target genes for the nine enriched lncRNAs, denoted as MALAT1, DLEU2, MSC-AS1, HAND-AS1, MIR99AHG, GATA2-AS1, LINC01140, KLF3-AS1, and MAGI2-AS3.

GO functional and KEGG pathway enrichment

GO functional annotation and KEGG pathway analyses were performed using the ShinyGo online tool. The biological processes (BP) of the common DEGs were found to be associated with morphogenesis and development, cellular and chemical responses, and regulation of cell signaling and communication. For the cellular components (CC), the extracellular parts were found to be predominantly altered in endometriosis. Moreover, the common DEGs' molecular functions (MF) represent several molecular binding roles, including cell adhesion, cytoskeletal protein, kinase, and signaling receptor (Fig. 4).

We conducted a KEGG pathway enrichment analysis of the shared DEGs and enriched miRNAs. As shown in Fig. 5A and Table 3, the interaction of extracellular matrix receptor, cell cycle, and focal adhesion, along with several signaling pathways (p53, P13K-Akt, and MAPK), were the enriched pathways of the shared DEGs. Moreover, the selected miRNA showed the highest number of hits on nucleocytoplasmic transport, carcinogenesis receptor activation, and several signaling pathways (Hippo, Hedgehog, prolactin, p53, and FoxO) (Fig. 5B).

Table 3

KEGG pathway of common DEGs
Pathway	n Genes	Genes
ECM-receptor interaction	16	COL4A3 COL6A2 COMP ITGA11 LAMA1 HMMR TNC ITGA6 ITGB8 LAMA4 LAMC2 RELN THBS2 FRAS1 ITGA8 CD44
p53 signaling pathway	12	CHEK1 SFN IGF1 FAS GADD45B SERPINE1 PMAIP1 RRM2 CCNB1 CCND2 CCNB2 CCNE2
Focal adhesion	23	MYL9 COL4A3 COL6A2 COMP DOCK1 ITGA11 FYN LAMA1 TNC IGF1 ITGA6 ITGB8 LAMA4 LAMC2 MET MYLK PPP1R12B MAPK8 RELN THBS2 PIP5K1B ITGA8 CCND2
Cell cycle	16	CHEK1 ORC6 SFN MAD2L1 GADD45B BUB1 BUB1B TTK CCNA2 CCNA1 CCNB1 CCND2 CCNB2 CCNE2 CDK1 CDC6
Pathways in cancer	45	COL4A3 CSF1R DAPK1 AGTR1 EPAS1 MECOM FGFR1 FGFR3 PLCB1 HEY1 HEY2 FOS LPAR3 LAMA1 GSTM3 IGF1 FAS IL2RA IL6 IL6ST CXCL8 ITGA6 JAK3 LAMA4 LAMC2 MET MMP1 GADD45B PMAIP1 MAPK8 PTCH1 PTGER3 PTGER4 RAD51 CXCL12 SP1 WNT5A WNT2B ZBTB16 PAX8 FZD4 CCNA2 CCNA1 CCND2 CCNE2
PI3K-Akt signaling pathway	30	COL4A3 COL6A2 COMP CSF1R ERBB3 FGFR1 FGFR3 ITGA11 LPAR3 LAMA1 NR4A1 TNC IGF1 IL2RA IL6 ITGA6 ITGB8 JAK3 LAMA4 LAMC2 MET NGF NTRK2 PPP2R2C RELN BRCA1 THBS2 ITGA8 CCND2 CCNE2
Cellular senescence	14	CHEK1 IL6 CXCL8 ITPR1 GADD45B SERPINE1 MAP2K6 CCNA2 CCNA1 CCNB1 CCND2 CCNB2 CCNE2 CDK1
Cell adhesion molecules	14	CDH3 VCAN CADM1 ICAM1 ITGA6 ITGB8 NRCAM JAM2 CLDN5 VCAM1 VTCN1 ITGA8 CLDN1 CD4

PPI network and hub genes identification

The protein-protein interaction (PPI) network of DEGs was analyzed using the STRING database with a confidence interaction score ≥ 0.9. After removing the unconnected nodes, the network construction by Cytoscape exhibited 178 nodes and 683 edges, comprising upregulated and downregulated genes (Fig. 6A). To further identify endometriosis-associated hub genes, CytoHubba was used to rank the top 15 hub genes in the PPI network based on the degree score. Those 15 hub genes denoted as CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, and NCAPG (Table 4, Fig. 6B). In addition, the enriched miRNA and lncRNA network with their respective targets were presented in Fig. 7 and Fig. 8, respectively.

Table 4

Top 15 hub genes with the highest score, analyzed with degree method
Rank	Symbol	Gene name	Score
1	CDK1	Cyclin-dependent kinase 1	47
2	CCNB1	Cyclin B1	39
3	KIF11	Kinesin family member 11	38
4	CCNA2	Cyclin A2	38
5	BUB1B	BUB1B mitotic checkpoint serine/threonine kinase B	36
6	DLGAP5	DLG-associated protein 5	34
7	BUB1	BUB1 mitotic checkpoint serine/threonine kinase	34
8	TOP2A	Topoisomerase (DNA) II alpha	33
9	ASPM	Abnormal spindle microtubule assembly	33
10	CEP55	Centrosomal protein 55	31
11	CENPF	Centromere protein F	31
12	TPX2	TPX2, microtubule nucleation factor	31
13	CCNB2	Cyclin B2	30
14	KIF2C	Kinesin family member C1	29
15	NCAPG	Non-SMC condensin I complex subunit G	29

Endometriosis pathophysiology is still enigmatic and far from being elucidated, as no single theory could explain all reported observations [17]. However, the constituent of genetic and epigenetic alterations in endometriosis is also a rapidly emerging research [18]. As proposed by Konickx et al., the genetic/epigenetic theory enhances the previously known theories of endometriosis by adding current knowledge of the cumulative genetic and epigenetic modifications transmitted at birth and acquired throughout life [17].

Accordingly, transcriptomic studies have shed light on the molecular basis of endometriosis through Genome-Wide Association Studies (GWAS). However, a shortcoming of recent GWAS is the relatively narrow approach, disregarding the potential impacts of long-range regulatory elements. Thus, linking GWAS with transcriptional analysis should help enlighten the essential genes associated with endometriosis [18]. Despite the massive data provided by different studies, constraints and challenges continue to affect the results, from the sample size to technical issues [19]. Combining larger samples from several studies is a promising approach to better comprehend the endometriosis-linked mechanism.

In the present study, we identified several endometriosis-associated features, including differentially expressed genes (DEGs), miRNA, and lncRNA. Expectedly, protein-coding genes accounted for the predominantly altered DEGs (~ 96,3%), of which 551 genes were common in at least four or more studies. As presented in Fig. 6A, the constructed network of DEGs (confidence score > 0.9) clearly differentiates the clusters of up and downregulated genes with comparable proportions. These results signify how gene alterations influence one another, resulting in intricate relationships to arouse and cease the essential process of endometriosis progression. These genes were linked to cellular and chemical responses and play a particular role in cell signaling and communication. Moreover, we pointed out that the alteration of extracellular components was primarily affected, and various molecular binding roles, such as cell adhesion, cytoskeletal protein, and kinase, marked the molecular function of gene ontology.

Intriguingly, we also found that Extracellular matrix (ECM) receptor interaction is the most enriched pathway, as reported by previous studies [20]. ECM components play a critical function in cellular networks. Klemmt et al. reported that the stromal cells of women with endometriosis display increased DNA synthesis, attachment, and proliferative capacity in response to soluble ECM components [21]. Accordingly, our findings also pinpoint the focal adhesion and cell cycle as the enriched pathways, which may explain the aftereffects of ECM alterations. Moreover, several integrin family genes were aberrated, including ITGA11, ITGA6, ITGB8, and ITGA8 (Table 3). It is known that integrins mediate the adhesion of cells to ECM components, such as collagen types I and IV, fibronectin, and laminin [22]. This environment was supported by our findings, of which COL4A3, COL6A2, LAMA4, LAMC2, and LAMA1 were altered in endometriosis patients (Table 3).

Given the growing evidence of interrelation between endometriosis and cancer, our results may confirm their shared connection. About 45 genes were associated with the pathway in cancer (Table 3), marking the highest number of hits of the KEGG pathway of studied DEGs. Similarly, Ni et al. reported that 571 DEGs overlapped between endometriosis and ovarian cancer [23], and several cancer mutations were harbored in 79% of endometriosis patients, as reported by Anglesio et al. [24]. However, our obtained hub genes may contradict the profound mechanism, as we found all downregulation of the following genes: CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG (Fig. 6, Table 4). These genes control cell cycle machinery, and their unscheduled up-regulation or down-regulation can hinder the cell death sentence, leading to uncontrolled cell proliferation and malignant transformation [25]. Therefore, we suggest that certain inhibitory mechanisms and other intricate processes were involved in endometriosis progression.

Regarding the epigenetic roles and mechanisms, we further analyzed the shared miRNAs and lncRNAs from all datasets. MicroRNAs are highly stable non-coding RNAs that post-transcriptionally regulate gene expression, affecting cell death and proliferation, inflammation, and other pathological mechanism [26]. Among 16 identified miRNAs, the network of five miRNA-target with highest interaction were analyzed, namely miR-25, miR-503, miR-302b, miR-424 and miR-10a (Fig. 7). Previous study demonstrated a significant decrease in miR-25 in ectopic and eutopic endometrium, and miR-503 suppression were reported to regulate CD97 expression and related JAK2/STAT3 pathway [27, 28]. Moreover, a study by Lin et al. showed that miR-302 induces demethylation of overall genomic DNA, activating several transcription factors such as Oct4, Sox2, Nanog, and Lin28. miR-302 has also been known for its inhibitory roles in various cancers through cyclin D1 inhibition [29].

We identified 9 enriched lncRNA and their respective target, namely MALAT1, DLEU2, MSC-AS1, HAND-AS1, MIR99AHG, GATA2-AS1, LINC01140, KLF3-AS1, and MAGI2-AS3 (Fig. 8). lncRNAs, as well as miRNA, exhibit pivotal role in endometriosis etiology. GATA2-AS1 is a 2358 bp lncRNA that performed as a tumor suppressor for inhibit proliferation. This lncRNA is controlling HOXB4 and ALDH1A2 expression, which associated with greater invasiveness of ectopic implants in ovarian endometriosis [30]. On the other hand, study showed that HAND2-AS1 expression leads to an increase in DNA methylation of HAND2, and lncRNA MALAT1 were reported to inhibit apoptosis of endometrial stromal cells through miR-126-5p-CREB1 axis by activating PI3K-AKT pathway [31].

Given the drawbacks of the present study, the significant findings should be interpreted deliberately. First, the heterogeneity of individual donors in terms of their specific pathophysiological condition must generate the disparities between the expression pattern of the endometriosis and control group, necessitating experimental validation of our findings. In addition, limitations associated with the variability between studies, namely the experimental protocols (microarray or RNA-sequencing), tissue and endometriosis types, and timely sample collection (in regards to the menstrual cycle), can bias the obtained results. Therefore, a comparative experimental study with homogeneous clinical characteristics is required to validate the results. Notwithstanding, our in-depth bioinformatic analysis favorably integrates the transcriptomic data from publicly available studies, emphasizing molecular targets associated with endometriosis progression.

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The authors declare no competing interests.

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Integrated Bioinformatic Analysis Reveals the Gene Signatures, Epigenetic Roles, and Regulatory Networks in Endometriosis

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Abstract

Figures

Introduction

Methods

Data extraction

Identification of DEGs

Identification of mRNA, miRNA, and lncRNA

Functional and pathway enrichment analysis

Network construction and hub genes analysis

Results

Included studies

Differentially expressed genes in endometriosis

miRNA and lncRNA expression altered in endometriosis

GO functional and KEGG pathway enrichment

PPI network and hub genes identification

Discussions

References

Additional Declarations

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