Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis

doi:10.21203/rs.3.rs-4928520/v1

Endometriosis is a gynecological disease characterized by the presence of uterine (eutopic) endometrial glands and tissues outside the intra-uterine locations, in ectopic regions such as the pelvic peritoneum, fallopian tubes, or ovaries. Approximately 5–10% of reproductive and 20–50% of infertile women are affected by endometriosis. The pathogenesis of endometriosis involves various factors, including hormonal, environmental, genetic, and immune system components, directly or indirectly altering estrogen levels and impacting women's reproductive health. This study aimed to identify novel and potential biomarkers for endometriosis using mRNA seq analysis. Differentially expressed genes (DEGs) were identified from raw gene expression profiles, and their functional analysis was subsequently conducted. A total of 552 DEGs (312 upregulated and 240 downregulated) were identified in samples from women with endometriosis compared to control subjects. Major DEGs, such as C3, PSAP, APP, GNG12, were identified as hub nodes and found to be involved in various functions, including epithelial cell differentiation and development, proteolysis, gland development, muscle fiber development, and response to hormone stimulus. These DEGs may play a direct or indirect role in the pathogenesis of endometriosis, serving as potential biomarkers for ectopic endometrium. While this study provides a preliminary insight into the mechanism of endometriosis, further detailed studies are necessary to fully understand its path of action.

Biological sciences/Genetics

Health sciences/Biomarkers/Diagnostic markers

Endometriosis

mRNA seq analysis

reproductive women

estrogen

aromatase

Endometriosis is a pronounced gynecological disease that significantly impacts women's health. This disorder is characterized by the presence of endometrial glands and stromal tissues outside the uterine endometrium (eutopic region), extending to ectopic regions such as the pelvic peritoneum, fallopian tubes, or ovaries [1–3]. Being an estrogen-dependent disorder, it affects approximately 5–10% of reproductively active women and 20–50% of women diagnosed with infertility globally [4]. Despite its prevalence, the etiology and pathogenesis of endometriosis remain unclear, imposing economic and reproductive health burdens on affected women [5]. Various theories have been proposed to explain its occurrence, with Sampson's theory of retrograde menstruation suggesting the transportation of endometrial cells from eutopic regions to ectopic regions, leading to endometriosis [6]. However, extensive studies are needed to distinguish expression patterns in eutopic and ectopic endometrium [7]. The absence of a non-invasive diagnostic marker using serum, urine, or endometrial tissue samples highlights the urgent need for early disease detection [8]. The precise mechanisms affecting the overall pathology of endometriosis remain unknown. The recent development of RNA-Seq analysis, a deep-sequencing technology, provides a novel approach to studying transcriptomes [9]. Transcriptomes represent the complete set of transcripts in a cell under a specific physiological condition, offering insights into the functional elements at play [10]. Molecular analysis comparing the endometrium of women with endometriosis to normal endometrium as a control holds promise for understanding the disease's pathophysiology and identifying specific biomarkers for diagnosis.

Microarray Data and Samples:

Raw gene expression profiles were retrieved from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) under the dataset ID GSE7305 [11]. The dataset comprised samples from normal eutopic endometrium, as well as ectopic and eutopic endometrium from patients with endometriosis.

Study Design:

The study focused on analyzing genes associated with both eutopic and ectopic endometrium in samples from control subjects and patients with endometriosis. Bioinformatic tools were employed for a comprehensive analysis of the microarray data.

Ethical Approval:

This microarray study received approval from the ethical committee of the institution, Banaras Hindu University, under the reference number Dean/2018/EC/936.

Data Processing and DEG Screening:

In processing raw gene expression datasets, probe-specific expression values were averaged to derive gene expression values. The BiGGEsTs software analysis tool was then utilized to identify up and downregulated genes. Subsequently, GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) was employed to convert probe-level symbols into gene-level symbols. All differentially expressed genes (DEGs) with p-values < 0.05 and threshold logFC values > 0.1 for upregulated genes and < -0.1 for downregulated genes were selected [12].

Principal Component Analysis and Heat Map Generation:

Principal component analysis (PCA) was conducted using the online tool ClustVis [13], specifically for DEGs. Due to size limitations of ClustVis (supporting file sizes up to 2MB), a PCA plot for the total gene expression was not feasible.

Identification of Novel Endometriosis Biomarkers:

To identify novel biomarkers associated with endometriosis, the list of differentially expressed genes was compared against reported gene lists obtained from OMIM (https://www.omim.org/) and Gene Cards (https://www.genecards.org/) [14]. Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) was utilized for comparison and construction of Venn diagrams Figure 1 [15].

Construction of Protein-Protein Interaction (PPI) Network and Sub-Network Mining:

Differentially expressed genes (DEGs) were identified and uploaded to STRING v 10.5 (http://www.string-db.org/) [16], an online database predicting functional interactions between proteins. A combined score > 0.4 served as the baseline criteria for protein-protein interaction (PPI) among gene pairs. Subsequently, the network and sub-network were constructed using Cytoscape v 3.2.1 (http://www.cytoscape.org/) [17], a software for visualizing and analyzing biological networks. The criteria for network construction included clustering coefficient and edge betweenness.

Functional and Pathway Enrichment Analysis of DEGs:

Gene ontology (GO) enrichment analysis, encompassing biological processes (BP), molecular functions (MF), and cellular components (CC), was performed using DAVID v 6.8 (database for annotation, visualization, and integrated discovery, http://david.abcc.ncifcrf.gov/) [18]. This program integrates a comprehensive set of functional annotations for a large gene list. Based on hypergeometric distribution, DAVID considers genes with similar or related functions as a whole set for enrichment analysis.

Differentially Expressed Genes (DEGs):

A total of 552 DEGs were identified with a p-value < 0.05, comprising 312 up-regulated and 240 down-regulated genes. Within this set, 148 DEGs were novel, consisting of 79 up-regulated and 69 down-regulated DEGs, selected based on their average gene expression values.

Principal Component and Hierarchical Clustering Analysis of DEGs:

Uniform Manifold Approximation and Projection (UMAP) plot exhibited distinct clustering of data with a neighborhood scoring of 9 Figure 2. The heat map, constructed for DEGs, visually represents the data matrix, where up-regulated DEGs are depicted in orange and down-regulated in blue. The color gradation from blue to orange signifies the numeric differences in gene expressions, ranging from small to large Figure 3.

Protein-Protein Interaction (PPI) Network:

Based on the combined score calculated by STRING, a total of 297 gene pairs (combined score > 0.9) were found to interact, forming a primary network with 114 nodes and 237 edges (Figure 4). Additionally, four sub-networks were extracted. Hub nodes in the network included up-regulated DEGs such as C3, APP, GNG12, PSAP, GNAQ, and down-regulated DEGs like LPAR1, RAB3D, PIK3R1, TRIM32, CMTM6. Selection of hub nodes was based on clustering coefficient and edge betweenness criteria.

Sub-network Extraction:

Four sub-networks (C1, C2, C3, and C4) were extracted from the main network using Cytoscape (Fig. 4). In sub-network C4, all genes were down-regulated, whereas in sub-networks C1, C2, and C3, 31, 12, and 25 genes were up-regulated, respectively, and 9, 5, and 3 genes were down-regulated.

Gene Ontology and Pathway Enrichment Analysis:

Functional enrichment analysis was conducted, and major molecular functions, biological processes, and cellular components of DEGs with a false discovery rate (FDR) < 0.05 were listed in Tables 1 and 2. Results of GO enrichment analysis for upregulated DEGs (Fig. 5A) identified hormone-mediated signaling, muscle fiber development, intracellular signaling cascade, and cellular protein complex assembly as major significant biological processes. Processes such as proteolysis, immune system development, epithelial cell differentiation, and development emerged as major significant biological processes related to downregulated DEGs (Fig. 5B). Furthermore, KEGG pathway enrichment analysis (Fig. 5C) revealed ECM-receptor interaction and ubiquitin-mediated proteolysis as downregulated pathways. Upregulated pathways included melanogenesis, GnRH signaling pathway, and Alzheimer's disease pathway.

Endometriosis, a complex gynecological disease, significantly impacts the reproductive health of women. Our study aimed to unravel the molecular mechanisms underlying the progression of endometriosis, utilizing comprehensive analysis of gene expression datasets through various bioinformatics tools.

The examination of gene expression profiles led to the identification of 552 differentially expressed genes (DEGs), including 312 up-regulated and 240 down-regulated genes. The up-regulated DEGs were found to be associated with essential biological processes such as cytokine-mediated signaling pathway, cellular response to stress, hormone-mediated signaling, lipid transport, and response to endogenous stimuli. Molecular functions of these up-regulated DEGs included lipase, kinase, lipoprotein, enzyme and cytoskeleton protein binding, enzyme activator, and phospholipase activity. They were also integral components of cellular structures such as the cytoskeleton, cytoplasmic vesicle, cell junctions, endomembrane system, Golgi apparatus, and plasma membrane.

Conversely, down-regulated DEGs were implicated in biological processes like proteolysis, gland and immune system development, integrin-mediated signaling, intracellular signaling, and protein kinase cascade. Their molecular functions involved signal peptidase, apical junction, cell-cell junction complex, perinuclear regions, and plasma membrane, with cellular components related to lysosphingolipid, lysophosphatidic acid, and identical protein binding.

Key hub nodes in the protein-protein interaction network were identified, with up-regulated DEGs including C3, APP, GNG12, PSAP, and GNAQ, while LPAR1, RAB3D, PIK3R1, TRIM32, and CMTM6 represented down-regulated DEGs. These hub nodes play crucial roles in the intricate molecular landscape of endometriosis.

C3 (Complement Component 3) is one of the important complement proteins out of 30 recognized till date. C3 has intensive and pivotal role in complement activation in both classical and alternative pathways. The alternative pathway is independent of antigen-antibody complexes and can directly be induced by components of cell wall of bacteria or present on the surface of damaged host cell via C3 unlike classical and lectin pathway [19]. Altered immune system is among the various risk factors which are involved in pathophysiology of endometriosis and hence deregulated C3 (which is an important player of immune system) might be involved in the progression of endometriosis and can be considered as a potent biomarker.

APP (Amyloid precursor protein), plays an important role in synaptic activity and neuronal plasticity, but upto this time it’s not completely revealed [20]. APP gene has been found to be associated with down’s syndrome [21]. Mutation in APP gene were also found to be associated with dementia and Alzimers disease including amyloid deposition, neurofibrillary tangle formation and cerebral amyloid angiopathy (CAA)[22]. APP is involved in the proliferation, migration and adhesion of endothelial cells. It mediates stability to focal adhesion and cell-cell junctions, while it's necessary for VEGF-A growth factor responses [23].Thus, it can be said that APP may be a factor to be involved in the adhesion and cellular junction formation of endometrial tissues during endometriosis.

GNG12 is known as the c12 subunit of G protein, and G proteins are made by three different subunits a, b and c making heterotrimeric. The different subunits are interchangeable, making possible combinations and wide array of effects. GNG12 is highly conserved, as and its homologs are present in human, rat, cow, frog, chicken and zebrafish. C12 is expressed differentially in mammalian brain, where its localized in glial cells and expressed in reactive astrocytes [24].

PSAP (Prosaposin, also known as SGP-1) is an intriguing multifunctional protein that plays roles intracellularly in regulation of lysosomal enzyme functions, and extracellularly, as a secretary factor having both neuroprotective and glioprotective effects. PSAP gene encodes a 524 amino-acid precursor protein prosaposin (pSAP), that gives 4 small glycoproteins-saposins (SapA, B, C and D) [25]. Saposins, a sphingolipid activator protein that’s required for the function of lysosomal hydrolases. Mutation in PSAP gene (two null alleles) in an individual have pSap deficiency and suffer with fatal infantile lysosomal storage disease[26]. Defects of Sap A and SapC leads to atypical Krabbe and Gaucher disease [27]. Any defect in SapB results in MLD (metachromatic leukodystrophy) due to impaired degradation and accumulation of cerebroside-3 sulfate (sulfatide) [28], while SapD deficiency causes Farber disease in mice [29].

GNAQ (Gnaq) is a member of guanine nucleotide binding protein (G protein) subunits and is found abundantly in brain. Gnaq gene knocking shows serious nervous dysfunction and endocrine system in mice [30] .Gnaq gene mutation has been reported mostly in uveal melanoma [31]. Gnaq, Gq protein a-subunit, encoded by GNAQ gene, is a member of Gq/11subfamily of heterotrimeric G proteins and its ubiquitously expressed in mammalian cells [30,32]. GNAQ has role in cardiovascular system [31]s, cancer and autoimmune diseases [33]. It is found to be coupled with GPCRs in viral infections[34], but no any expression or mutation studies has been yet reported in endometriosis, adenomyosis or ovarian carcinoma.

PIK3R1

PI3K enzymes are lipid kinases, having a conserved sequences and phosphorylating the

inositol 30-OH groups of membrane phosphoinositides (PI).

Class I PI3K convert phosphoinositol bisphosphate (PIP2) [4,5] into phosphoinositol trisphosphate (PIP3) [3,4,5] a second messenger [35]. Class IA PI3K is composed of a heterodimer having a p110 catalytic subunit and a p85 regulatory subunit. Out of four PI3K catalytic subunit isoforms (PI3Ka, PI3Kb, PI3Kg, and PI3Kd), only PI3Ka and PI3Kb are ubiquitously expressed in the body and are frequently altered in cancer disease [36]. Three different genes PIK3R1, PIK3R2, and PIK3R3, encode p85-type subunits; p85a, p85b and p55g, respectively. The two of PIK3R1 and PIK3R2 are widely expressed in the body, whereas the third one PIK3R3 is expressed only in testis and brain of adults [37]. PIK3R1/p85a is isoform found abundantly, but in cancer patients its expression is reduced [38]. It’s a Tumor-Suppressor Gene and the most striking difference between p85a and p85b is that PIK3R1/p85a acts as a tumor suppressor while, PIK3R2/p85b is a cancer driver. The recent finding say that p85a subunit restrains the catalytic activity of PI3K [39] encourage testing the consequences of reducing p85a levels. Other study tells deletion of Pik3r1 led to a gradual change in hepatocyte morphology (liver) and with over time, mice develops hepatocellular carcinoma [40]s. These two observations confirm the deletion of Pik3r1 gene is a cause of tumor development, as similarly observed for tumor suppressors. Pik3r1 loss in mouse accelerates HER2/neu-induced mammary cancer development, in cultured human epithelial cells PIK3R1 knockdown cause transformational changes, while hemi-zygous deletion is a frequent event in breast cancer samples [41]. Mutation in PIK3R1 has been reported in breast, pancreatic, colon cancers [42] and in 8.5% cases of ovarian carcinoma [43] The PIK3 pathway is downstream regulated from RTK (receptor tyrosine kinase), that’s active in cancer lineages, including endometrial cancer (EC) [44]. PIK3 mutation has been reported in ovarian carcinoma but not in cases where patients suffer with endometriosis only[45] . While PI3K pathway has been found to be strongly implicated during the development of endometriosis [46].

LPAR1

Lysophosphatidic Acid, is a small phospholipid present in many mammalian cells and tissues [47]. It is involved in cell migration, survival, proliferation, cellular interactions and cytoskeleton changes [48]. A study on mare, suffering with endometriosis has indicated that concentration of LPA, its receptors, PGE2/PGF2 ration and CTGF secretion is altered during endometriosis [49]. LPA induces IL-8 (Interleukin-8) expression via LPA-1 receptor, Gi protein, MAPK/p38 and NF-kB signalling pathway. Where IL-8 protein stimulates endometrial cell migration, permeability, capillary tube formation and proliferation leading to angiogenesis during pregnancy [50]. Overexpression of all LPARs and enzymes occur responsible for LPA synthesis, during endometrial cancers, showing a positive correlation with myoinvasion and FIGO (International Federation of Gynecology and Obstetrics) stage [51]s. Recent study has demonstrated that LPA, acts as a mitogen and pro-invasive stimulus for endometrial and endometriotic cells acts via LPAR1 and LPAR3 (Lysophosphatidic Acid Receptors). It has demonstrated that LPA-dependent stimulus causes secretion of cathepsin B, a protease, which acts as a factor for endometriotic invasion [52].

RAB3D, is a GTPase of Rab family, plays a role as a central regulator of vesicular transport [53].A study reveals that the Rab3D is implicated in the subcellular localization and maturation of Immature Secretory granules (ISGs) [54]. Rabs are involved in membrane trafficking, cellular signalling, growth and development. Rabs and their effectors has been found to be overexpressed or undergone for loss of function in many disease including cancers and causes the disease progression[55] .Endometriosis is also an inflammatory disease and similarly Rab can play its role in endometriosis disease progression and adhesion.

GPR39, are G-protein coupled receptors present in the plasma membrane, which is distinct target for binding of extracellular Zn⁺ ions [56–58]. G-protein coupled receptors are a large family of seven-transmembrane proteins, which are all involved in a diverse array of extracellular stimuli [59]. Zinc is an important component of enzymes and proteins. It is also required for intracellular message transmission, protein synthesis, cell membrane maintenance and transport, regulation of neuronal, endocrinal system [60]s. In brain GPR39 zinc sensing receptor has a significant role in Alzheimer and Epilepsy [61]. Further investigations showed that GPR39 contributes its part in skin wound healing, thus having a positive role in the area of dermatology and stem cell therapy [62]. A study has revealed that GPR39 might be a direct promising target for therapy of zincergic dyshomeostasis observed in Alzheimer’s disease, but more studies are still needed over it [63].

HOXC4, is actively transcribed during the development and differentiation of Lymphoid, myeloid and erythroid cells. It helps to maintain proliferation in hematopoietic cells[64].

Cystatin A (CSTA), is a type 1 cystatin super-family member, and is expressed mainly in epithelial and lymphoid tissues. It prevents the proteolysis of cytoplasmic and cytoskeletal proteins in cells. It acts a tumor suppressor in esophageal and lung cancer [65]. The risk of disease recurrence and death are found to be higher in patients suffering with squamous cell carcinoma of head and neck, having low CSTA[66] .Here expression analysis of DEGs in endometriosis, CSTA was found to be downregulated but detailed studies for the role of CSTA in endometriosis disease progression is still has to be done.

Epithelial splicing regulatory protein 1 (ESRP1), plays crucial role during organogenesis of craniofacial and epidermal development, branching morphogenesis in the lungs and salivary glands. It also plays role during cancer progression, and its expression found to be low in normal epithelium but upregulated during carcinoma [67]s.

This study has unveiled a diverse array of genes that likely play pivotal roles, directly or indirectly, in the pathophysiology of endometriosis. The identified genes contribute to the intricate molecular landscape involved in the progression of the disease. It is evident that multiple genes collaboratively participate in shaping the trajectory of endometriosis. While this study provides valuable insights into the physiological aspects of the disease, it is important to acknowledge the complexity of endometriosis and the likelihood of numerous genes collectively influencing its progression. The findings serve as a stepping stone towards a better understanding of the disease, yet a more detailed and comprehensive study is imperative to unravel the nuanced mechanisms underlying endometriosis fully. This study lays the groundwork for further exploration, encouraging future investigations to delve deeper into the roles and interactions of these genes. Such endeavors will contribute to the development of targeted therapeutic interventions and more accurate diagnostic strategies for endometriosis.

Acknowledgement

We want to extend our sincere gratitude to Multi-Disciplinary Research Units (MRUs) Laboratory, a grant by ICMR-Department of Health Research.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics statement

The studies involving human participants were reviewed and approved by Ethics Committee of the Institutional Ethical committee before starting the study (No. Dean/2018/EC/936).

Consent to Participate

All research procedures were approved by and in accordance with relevant guidelines and regulations.

Consent for publication

Not available.

Data Availability

The detailed datasets analyzed during the current study are available with the corresponding author. In the future, it will be made available on reasonable request. Few data were used under license for the current study. Therefore, it is not publicly available. Data are however available from the corresponding author upon reasonable request.

Funding

Not available.

Author contributions

RS, RC and SR conceived and designed the project. KK and AA performed all operations. RB analyzed the data and drew the figures. KK and RB wrote the manuscript. AA, KK, RB and RS revised the manuscript. All authors contributed to the article and approved the submitted version.

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Table 1: GENE ONTOLOGY (GO) analysis for Up regulated DEGs.

Category	Term	P-Value
GOTERM_BP_FAT	GO:0007242~intracellular signaling cascade	1.34E-05
GOTERM_BP_FAT	GO:0014706~striated muscle tissue development	5.44E-04
GOTERM_BP_FAT	GO:0042692~muscle cell differentiation	5.87E-04
GOTERM_BP_FAT	GO:0060537~muscle tissue development	6.80E-04
GOTERM_BP_FAT	GO:0042391~regulation of membrane potential	9.31E-04
GOTERM_BP_FAT	GO:0007517~muscle organ development	0.001183
GOTERM_BP_FAT	GO:0051146~striated muscle cell differentiation	0.001501
GOTERM_BP_FAT	GO:0010033~response to organic substance	0.002202
GOTERM_BP_FAT	GO:0048878~chemical homeostasis	0.00222
GOTERM_BP_FAT	GO:0010647~positive regulation of cell communication	0.002453
GOTERM_BP_FAT	GO:0006873~cellular ion homeostasis	0.004966
GOTERM_BP_FAT	GO:0006928~cell motion	0.005073
GOTERM_BP_FAT	GO:0055082~cellular chemical homeostasis	0.00541
GOTERM_BP_FAT	GO:0016044~membrane organization	0.005487
GOTERM_BP_FAT	GO:0007519~skeletal muscle tissue development	0.00605
GOTERM_BP_FAT	GO:0060538~skeletal muscle organ development	0.00605
GOTERM_BP_FAT	GO:0009967~positive regulation of signal transduction	0.006229
GOTERM_BP_FAT	GO:0009719~response to endogenous stimulus	0.007589
GOTERM_BP_FAT	GO:0050801~ion homeostasis	0.007991
GOTERM_BP_FAT	GO:0032273~positive regulation of protein polymerization	0.008595
GOTERM_BP_FAT	GO:0006869~lipid transport	0.008949
GOTERM_BP_FAT	GO:0042592~homeostatic process	0.009037
GOTERM_BP_FAT	GO:0048741~skeletal muscle fiber development	0.010712
GOTERM_BP_FAT	GO:0044092~negative regulation of molecular function	0.011126
GOTERM_BP_FAT	GO:0001765~membrane raft formation	0.011205
GOTERM_BP_FAT	GO:0045807~positive regulation of endocytosis	0.011464
GOTERM_BP_FAT	GO:0010876~lipid localization	0.011736
GOTERM_BP_FAT	GO:0052547~regulation of peptidase activity	0.012493
GOTERM_BP_FAT	GO:0010627~regulation of protein kinase cascade	0.012994
GOTERM_MF_FAT	GO:0008092~cytoskeletal protein binding	0.002399
GOTERM_MF_FAT	GO:0016504~peptidase activator activity	0.007702
GOTERM_MF_FAT	GO:0019899~enzyme binding	0.010389
GOTERM_MF_FAT	GO:0003779~actin binding	0.01129
GOTERM_MF_FAT	GO:0004620~phospholipase activity	0.012226
GOTERM_MF_FAT	GO:0008047~enzyme activator activity	0.012783
GOTERM_MF_FAT	GO:0008289~lipid binding	0.015001
GOTERM_MF_FAT	GO:0008034~lipoprotein binding	0.017323
GOTERM_MF_FAT	GO:0019900~kinase binding	0.019893
GOTERM_MF_FAT	GO:0016298~lipase activity	0.020073
GOTERM_MF_FAT	GO:0070290~NAPE-specific phospholipase D activity	0.028556
GOTERM_MF_FAT	GO:0042802~identical protein binding	0.030878
GOTERM_MF_FAT	GO:0070064~proline-rich region binding	0.03417
GOTERM_MF_FAT	GO:0008035~high-density lipoprotein binding	0.039752
GOTERM_MF_FAT	GO:0004630~phospholipase D activity	0.039752
GOTERM_MF_FAT	GO:0008139~nuclear localization sequence binding	0.039752
GOTERM_MF_FAT	GO:0019904~protein domain specific binding	0.042369
GOTERM_MF_FAT	GO:0050998~nitric-oxide synthase binding	0.050821
GOTERM_MF_FAT	GO:0004866~endopeptidase inhibitor activity	0.05148
GOTERM_MF_FAT	GO:0030414~peptidase inhibitor activity	0.058629
GOTERM_CC_FAT	GO:0030055~cell-substrate junction	4.14E-04
GOTERM_CC_FAT	GO:0005886~plasma membrane	0.001132
GOTERM_CC_FAT	GO:0005912~adherens junction	0.001789
GOTERM_CC_FAT	GO:0005626~insoluble fraction	0.002358
GOTERM_CC_FAT	GO:0005925~focal adhesion	0.002595
GOTERM_CC_FAT	GO:0070161~anchoring junction	0.002815
GOTERM_CC_FAT	GO:0005924~cell-substrate adherens junction	0.002983
GOTERM_CC_FAT	GO:0005794~Golgi apparatus	0.00324
GOTERM_CC_FAT	GO:0009986~cell surface	0.003371
GOTERM_CC_FAT	GO:0012505~endomembrane system	0.004123
GOTERM_CC_FAT	GO:0031982~vesicle	0.004141
GOTERM_CC_FAT	GO:0005624~membrane fraction	0.005317
GOTERM_CC_FAT	GO:0048471~perinuclear region of cytoplasm	0.00559
GOTERM_CC_FAT	GO:0016323~basolateral plasma membrane	0.00569
GOTERM_CC_FAT	GO:0044431~Golgi apparatus part	0.006169
GOTERM_CC_FAT	GO:0030027~lamellipodium	0.007163
GOTERM_CC_FAT	GO:0044459~plasma membrane part	0.008355
GOTERM_CC_FAT	GO:0030054~cell junction	0.008464
GOTERM_CC_FAT	GO:0009925~basal plasma membrane	0.009319
GOTERM_CC_FAT	GO:0045178~basal part of cell	0.012293
GOTERM_CC_FAT	GO:0031088~platelet dense granule membrane	0.016805
GOTERM_CC_FAT	GO:0000267~cell fraction	0.017154
GOTERM_CC_FAT	GO:0044421~extracellular region part	0.017907
GOTERM_CC_FAT	GO:0000139~Golgi membrane	0.020736
GOTERM_CC_FAT	GO:0031410~cytoplasmic vesicle	0.027418
GOTERM_CC_FAT	GO:0005955~calcineurin complex	0.027853
GOTERM_CC_FAT	GO:0042827~platelet dense granule	0.033332
GOTERM_CC_FAT	GO:0005901~caveola	0.03462
GOTERM_CC_FAT	GO:0016023~cytoplasmic membrane-bounded vesicle	0.034768
GOTERM_CC_FAT	GO:0005615~extracellular space	0.038156
GOTERM_CC_FAT	GO:0031988~membrane-bounded vesicle	0.040294
GOTERM_CC_FAT	GO:0042995~cell projection	0.0416
GOTERM_CC_FAT	GO:0031252~cell leading edge	0.042819
GOTERM_CC_FAT	GO:0005856~cytoskeleton	0.043217
GOTERM_CC_FAT	GO:0005576~extracellular region	0.051766
GOTERM_CC_FAT	GO:0019898~extrinsic to membrane	0.059319

Table 2: GENE ONTOLOGY (GO) analysis for down regulated DEGs

Category	Term	P-Value
GOTERM_BP_FAT	GO:0048704~embryonic skeletal system morphogenesis	1.89E-04
GOTERM_BP_FAT	GO:0048706~embryonic skeletal system development	6.00E-04
GOTERM_BP_FAT	GO:0048705~skeletal system morphogenesis	0.002413
GOTERM_BP_FAT	GO:0048562~embryonic organ morphogenesis	0.004473
GOTERM_BP_FAT	GO:0048732~gland development	0.004717
GOTERM_BP_FAT	GO:0009952~anterior/posterior pattern formation	0.005363
GOTERM_BP_FAT	GO:0043009~chordate embryonic development	0.006282
GOTERM_BP_FAT	GO:0009792~embryonic development ending in birth or egg hatching	0.006557
GOTERM_BP_FAT	GO:0048568~embryonic organ development	0.010931
GOTERM_BP_FAT	GO:0002520~immune system development	0.012496
GOTERM_BP_FAT	GO:0003002~regionalization	0.01722
GOTERM_BP_FAT	GO:0048598~embryonic morphogenesis	0.018973
GOTERM_BP_FAT	GO:0001501~skeletal system development	0.021983
GOTERM_BP_FAT	GO:0060429~epithelium development	0.027277
GOTERM_BP_FAT	GO:0007167~enzyme linked receptor protein signaling pathway	0.02859
GOTERM_BP_FAT	GO:0030097~hemopoiesis	0.030857
GOTERM_BP_FAT	GO:0030855~epithelial cell differentiation	0.031589
GOTERM_BP_FAT	GO:0014065~phosphoinositide 3-kinase cascade	0.034668
GOTERM_BP_FAT	GO:0060216~definitive hemopoiesis	0.034668
GOTERM_BP_FAT	GO:0015031~protein transport	0.036558
GOTERM_BP_FAT	GO:0006508~proteolysis	0.036929
GOTERM_BP_FAT	GO:0007243~protein kinase cascade	0.038188
GOTERM_BP_FAT	GO:0045184~establishment of protein localization	0.038276
GOTERM_BP_FAT	GO:0006465~signal peptide processing	0.039523
GOTERM_BP_FAT	GO:0009057~macromolecule catabolic process	0.041345
GOTERM_BP_FAT	GO:0048534~hemopoietic or lymphoid organ development	0.04171
GOTERM_BP_FAT	GO:0007389~pattern specification process	0.045236
GOTERM_BP_FAT	GO:0007242~intracellular signaling cascade	0.047661
GOTERM_BP_FAT	GO:0007229~integrin-mediated signaling pathway	0.048307
GOTERM_BP_FAT	GO:0045022~early endosome to late endosome transport	0.04916
GOTERM_BP_FAT	GO:0008380~RNA splicing	0.054478
GOTERM_CC_FAT	GO:0044459~plasma membrane part	1.39E-04
GOTERM_CC_FAT	GO:0005911~cell-cell junction	2.12E-04
GOTERM_CC_FAT	GO:0043296~apical junction complex	0.001043
GOTERM_CC_FAT	GO:0016327~apicolateral plasma membrane	0.001165
GOTERM_CC_FAT	GO:0048471~perinuclear region of cytoplasm	0.001881
GOTERM_CC_FAT	GO:0030057~desmosome	0.003648
GOTERM_CC_FAT	GO:0005787~signal peptidase complex	0.013552
GOTERM_CC_FAT	GO:0070695~FHF complex	0.022487
GOTERM_CC_FAT	GO:0030054~cell junction	0.02936
GOTERM_CC_FAT	GO:0005886~plasma membrane	0.036058
GOTERM_CC_FAT	GO:0070161~anchoring junction	0.043114
GOTERM_CC_FAT	GO:0045177~apical part of cell	0.047573
GOTERM_CC_FAT	GO:0030897~HOPS complex	0.053135
GOTERM_MF_FAT	GO:0042802~identical protein binding	0.007236
GOTERM_MF_FAT	GO:0001619~lysosphingolipid and lysophosphatidic acid receptor activity	0.05657

No competing interests reported.

Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis

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Abstract

Figures

Introduction

Materials and Methods

Results

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

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