Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis

Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis Kusum Kusum¹, Ashish Ashish, Royana Singh, Ravi Bhushan, Radha Chaube, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4928520/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction 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. Materials and Methods 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 0.1 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. Results 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. Discussion 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. Conclusion 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. Declarations 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. References Parasar P, Ozcan P, Terry KL. Endometriosis: Epidemiology, Diagnosis and Clinical Management. Curr Obstet Gynecol Rep. 2017;6(1):34–41. Bulun SE. Endometriosis. N Engl J Med. 2009 Jan;360(3):268–79. Nnoaham KE, Hummelshoj L, Webster P, D’Hooghe T, De Cicco Nardone F, De Cicco Nardone C, et al. Impact of endometriosis on quality of life and work productivity: A multicenter study across ten countries. Fertil Steril. 2011;96(2). Meuleman C, Vandenabeele B, Fieuws S, Spiessens C, Timmerman D, D’Hooghe T. 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Budihna M, Strojan P, Èmid L, Èkrk J, Vrhovec I, Zupevc A. Prognostic value of cathepsins B, H, L, D and their endogenous inhibitors stefins A and B in head and neck carcinoma. Biol Chem Hoppe Seyler. 1996;377(6):385–90. Hayakawa A, Saitoh M, Miyazawa K. Dual roles for epithelial splicing regulatory proteins 1 (ESRP1) and 2 (ESRP2) in cancer progression. In: Advances in Experimental Medicine and Biology [Internet]. Springer New York LLC; 2017 [cited 2021 Mar 13]. p. 33–40. Available from: https://pubmed.ncbi.nlm.nih.gov/27401076/ Tables 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 Additional Declarations No competing interests reported. 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Blue to orange gradation is for small to large changes in gene expression values.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4928520/v1/738f70d885ccbae0b05465eb.png"},{"id":65438951,"identity":"3b2024ac-b34d-42d1-b37d-01f0a877b7a2","added_by":"auto","created_at":"2024-09-27 12:24:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":607537,"visible":true,"origin":"","legend":"\u003cp\u003e(A and B) Protein-protein interaction (PPI) of DEG’s. C1, C2, C3 and C4 are four sub-networks of DEG’s: \u003cstrong\u003eC1\u003c/strong\u003e; sub-network 1, \u003cstrong\u003eC2\u003c/strong\u003e; sub-network 2, \u003cstrong\u003eC3\u003c/strong\u003e; sub-network 3, \u003cstrong\u003eC4\u003c/strong\u003e; sub-network 4. Red-circle for up and blue-circle for down-regulated genes respectively. Blue diamond for similar related genes and lines shows the correlation between genes, where thickness of lines (edges), is proportional to the combined scores.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4928520/v1/a05d30ae5688ab54579121cb.png"},{"id":65437673,"identity":"5bd2502e-234f-46b4-bfd1-1871e593d2d1","added_by":"auto","created_at":"2024-09-27 12:16:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":522014,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) enrichment analysis for up-regulated \u003cstrong\u003e(5A)\u003c/strong\u003e and down-regulated DEG’s \u003cstrong\u003e(5B)\u003c/strong\u003e, and KEGG pathway enrichment for DEG’s (5C).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4928520/v1/a4c32836d7b05cc89b04a20a.png"},{"id":66039117,"identity":"15f6a359-816c-4b42-a54e-5e4a76ec524f","added_by":"auto","created_at":"2024-10-07 05:25:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2431959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4928520/v1/df20d76a-b5a9-43af-9e83-ee01e8dbc687.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis 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 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Being an estrogen-dependent disorder, it affects approximately 5\u0026ndash;10% of reproductively active women and 20\u0026ndash;50% of women diagnosed with infertility globally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite its prevalence, the etiology and pathogenesis of endometriosis remain unclear, imposing economic and reproductive health burdens on affected women [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, extensive studies are needed to distinguish expression patterns in eutopic and ectopic endometrium [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The absence of a non-invasive diagnostic marker using serum, urine, or endometrial tissue samples highlights the urgent need for early disease detection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Transcriptomes represent the complete set of transcripts in a cell under a specific physiological condition, offering insights into the functional elements at play [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eMicroarray Data and Samples:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis microarray study received approval from the ethical committee of the institution, Banaras Hindu University, under the reference number Dean/2018/EC/936.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Processing and DEG Screening:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 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 \u0026lt; 0.05 and threshold logFC values \u0026gt; 0.1 for upregulated genes and \u0026lt; -0.1 for downregulated genes were selected [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis and Heat Map Generation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Identification of Novel Endometriosis Biomarkers:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo 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 \u003cstrong\u003eFigure 1\u003c/strong\u003e [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Protein-Protein Interaction (PPI) Network and Sub-Network Mining:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentially 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 \u0026gt; 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and Pathway Enrichment Analysis of DEGs:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene 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.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferentially Expressed Genes (DEGs):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 552 DEGs were identified with a p-value \u0026lt; 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal Component and Hierarchical Clustering Analysis of DEGs:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUniform Manifold Approximation and Projection (UMAP) plot exhibited distinct clustering of data with a neighborhood scoring of 9 \u003cstrong\u003eFigure 2\u003c/strong\u003e. 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 \u003cstrong\u003eFigure 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-Protein Interaction (PPI) Network:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the combined score calculated by STRING, a total of 297 gene pairs (combined score \u0026gt; 0.9) were found to interact, forming a primary network with 114 nodes and 237 edges (\u003cstrong\u003eFigure 4\u003c/strong\u003e). 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSub-network Extraction:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour sub-networks (C1, C2, C3, and C4) were extracted from the main network using Cytoscape\u0026nbsp;\u003cstrong\u003e(Fig. 4).\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology and Pathway Enrichment Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis was conducted, and major molecular functions, biological processes, and cellular components of DEGs with a false discovery rate (FDR) \u0026lt; 0.05 were listed in\u0026nbsp;\u003cstrong\u003eTables 1 and 2.\u003c/strong\u003e Results of GO enrichment analysis for upregulated DEGs (\u003cstrong\u003eFig. 5A\u003c/strong\u003e) 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 (\u003cstrong\u003eFig. 5B\u003c/strong\u003e). Furthermore, KEGG pathway enrichment analysis (\u003cstrong\u003eFig. 5C\u003c/strong\u003e) revealed ECM-receptor interaction and ubiquitin-mediated proteolysis as downregulated pathways. Upregulated pathways included melanogenesis, GnRH signaling pathway, and Alzheimer\u0026apos;s disease pathway.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEndometriosis, 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKey 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC3\u003c/strong\u003e (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\u0026nbsp;[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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPP\u003c/strong\u003e (Amyloid precursor protein), plays an important role in synaptic activity and neuronal plasticity, but upto this time it\u0026rsquo;s not completely revealed\u0026nbsp;[20]. APP gene has been found to be associated with down\u0026rsquo;s syndrome\u0026nbsp;[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\u0026apos;s necessary for VEGF-A growth factor responses\u0026nbsp;[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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGNG12\u0026nbsp;\u003c/strong\u003eis known as the c12 subunit of G protein, and G proteins are made by three different subunits a, b and c making heterotrimeric. \u0026nbsp;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\u0026nbsp;[24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSAP (Prosaposin,\u0026nbsp;\u003c/strong\u003ealso 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.\u0026nbsp;PSAP gene encodes a 524 amino-acid precursor protein prosaposin (pSAP), that gives 4 small glycoproteins-saposins (SapA, B, C and D)\u0026nbsp;[25].\u0026nbsp;Saposins, a sphingolipid activator protein that\u0026rsquo;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\u0026nbsp;[27]. Any defect in SapB results in MLD (metachromatic leukodystrophy)\u0026nbsp;due to impaired degradation and accumulation of cerebroside-3 sulfate (sulfatide)\u0026nbsp;[28], while SapD deficiency causes Farber disease in mice\u0026nbsp;[29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGNAQ (\u003c/strong\u003eGnaq) 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\u0026nbsp;[30]\u0026nbsp;.Gnaq gene mutation has been reported mostly in uveal melanoma\u0026nbsp;[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\u0026nbsp;[31]s, cancer and autoimmune diseases\u0026nbsp;[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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePIK3R1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePI3K enzymes are lipid kinases, having a conserved sequences and phosphorylating the \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; inositol 30-OH groups of membrane phosphoinositides (PI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClass I PI3K convert phosphoinositol bisphosphate (PIP2) \u0026nbsp;[4,5] into phosphoinositol trisphosphate (PIP3) [3,4,5] a second messenger\u0026nbsp;[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\u0026nbsp;[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\u0026nbsp;[37]. PIK3R1/p85a is isoform found abundantly, but in cancer patients its expression is reduced\u0026nbsp;[38]. It\u0026rsquo;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\u0026nbsp;[39]\u0026nbsp;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\u0026nbsp;[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\u0026nbsp;[41].\u0026nbsp;Mutation in PIK3R1 has been reported in breast, pancreatic, colon cancers\u0026nbsp;[42]\u0026nbsp;and in 8.5% cases of ovarian carcinoma\u0026nbsp;[43]\u0026nbsp;\u0026nbsp;The PIK3 pathway is downstream regulated from RTK (receptor tyrosine kinase), that\u0026rsquo;s active in cancer lineages, including endometrial cancer (EC)\u0026nbsp;[44]. PIK3 mutation has been reported in ovarian carcinoma but not in cases where patients suffer with endometriosis only[45]\u0026nbsp;.\u0026nbsp;While PI3K pathway has been found to be strongly implicated during the development of endometriosis\u0026nbsp;[46].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLPAR1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLysophosphatidic Acid, is a small phospholipid present in many mammalian cells and tissues\u0026nbsp;[47]. It is involved in cell migration, survival, proliferation, cellular interactions and cytoskeleton changes\u0026nbsp;[48]. A study\u0026nbsp;on mare, suffering with endometriosis has indicated that concentration of LPA, its receptors, PGE2/PGF2 ration and CTGF secretion is altered during endometriosis\u0026nbsp;[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\u0026nbsp;[50].\u0026nbsp;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\u0026nbsp;[51]s.\u0026nbsp;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\u0026nbsp;[52].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRAB3D\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eis a GTPase of Rab family, plays a role as a central regulator of vesicular transport\u0026nbsp;[53].A study reveals that the Rab3D is implicated in the subcellular localization and maturation of Immature Secretory granules (ISGs)\u0026nbsp;[54].\u0026nbsp;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]\u0026nbsp;.Endometriosis is also an inflammatory disease and similarly Rab can play its role in endometriosis disease progression and adhesion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGPR39,\u0026nbsp;\u003c/strong\u003eare G-protein coupled receptors present in the plasma membrane, which is distinct target for binding of extracellular Zn\u003csup\u003e+\u003c/sup\u003e ions\u0026nbsp;[56\u0026ndash;58].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eG-protein coupled receptors are a large family of seven-transmembrane proteins, which are all involved in a diverse array of extracellular stimuli\u0026nbsp;[59].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eZinc 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\u0026nbsp;[60]s.\u0026nbsp;In brain GPR39 zinc sensing receptor has a significant role in Alzheimer and Epilepsy\u0026nbsp;[61].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFurther 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\u0026nbsp;[62].\u0026nbsp;A study has revealed that GPR39 might be a direct promising target for therapy of zincergic dyshomeostasis observed in Alzheimer\u0026rsquo;s disease, but more studies are still needed over it\u0026nbsp;[63].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHOXC4\u003c/strong\u003e, is actively transcribed during the development and differentiation of Lymphoid, myeloid and erythroid cells. It helps to maintain proliferation in hematopoietic cells[64].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCystatin A (\u003cstrong\u003eCSTA\u003c/strong\u003e), 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\u0026nbsp;[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]\u0026nbsp;.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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEpithelial splicing regulatory protein 1 (\u003cstrong\u003eESRP1\u003c/strong\u003e), 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.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis 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.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to extend our sincere gratitude to Multi-Disciplinary Research Units (MRUs) Laboratory, a grant by ICMR-Department of Health Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll research procedures were approved by and in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRS, 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eParasar P, Ozcan P, Terry KL. Endometriosis: Epidemiology, Diagnosis and Clinical Management. Curr Obstet Gynecol Rep. 2017;6(1):34\u0026ndash;41. \u003c/li\u003e\n\u003cli\u003eBulun SE. Endometriosis. N Engl J Med. 2009 Jan;360(3):268\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eNnoaham KE, Hummelshoj L, Webster P, D\u0026rsquo;Hooghe T, De Cicco Nardone F, De Cicco Nardone C, et al. Impact of endometriosis on quality of life and work productivity: A multicenter study across ten countries. Fertil Steril. 2011;96(2). \u003c/li\u003e\n\u003cli\u003eMeuleman C, Vandenabeele B, Fieuws S, Spiessens C, Timmerman D, D\u0026rsquo;Hooghe T. High prevalence of endometriosis in infertile women with normal ovulation and normospermic partners. Fertil Steril. 2009;92(1):68\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eDai Y, Li X, Shi J, Leng J. A review of the risk factors, genetics and treatment of endometriosis in Chinese women: A comparative update. Vol. 15, Reproductive Health. 2018. p. 82. \u003c/li\u003e\n\u003cli\u003eMathew AG. 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Oncotarget. 2018;9(18):14084\u0026ndash;98. \u003c/li\u003e\n\u003cli\u003eBudihna M, Strojan P, \u0026Egrave;mid L, \u0026Egrave;krk J, Vrhovec I, Zupevc A. Prognostic value of cathepsins B, H, L, D and their endogenous inhibitors stefins A and B in head and neck carcinoma. Biol Chem Hoppe Seyler. 1996;377(6):385\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eHayakawa A, Saitoh M, Miyazawa K. Dual roles for epithelial splicing regulatory proteins 1 (ESRP1) and 2 (ESRP2) in cancer progression. In: Advances in Experimental Medicine and Biology [Internet]. Springer New York LLC; 2017 [cited 2021 Mar 13]. p. 33\u0026ndash;40. Available from: https://pubmed.ncbi.nlm.nih.gov/27401076/\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: GENE ONTOLOGY (GO) analysis for Up regulated DEGs.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0007242~intracellular signaling cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e1.34E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0014706~striated muscle tissue development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e5.44E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0042692~muscle cell differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e5.87E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0060537~muscle tissue development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e6.80E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0042391~regulation of membrane potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e9.31E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0007517~muscle organ development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.001183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0051146~striated muscle cell differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.001501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0010033~response to organic substance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.002202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0048878~chemical homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.00222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0010647~positive regulation of cell communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.002453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0006873~cellular ion homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.004966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0006928~cell motion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.005073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0055082~cellular chemical homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.00541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0016044~membrane organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.005487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0007519~skeletal muscle tissue development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.00605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0060538~skeletal muscle organ development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.00605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0009967~positive regulation of signal transduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.006229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0009719~response to endogenous stimulus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.007589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0050801~ion homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.007991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0032273~positive regulation of protein polymerization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.008595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0006869~lipid transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.008949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0042592~homeostatic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.009037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0048741~skeletal muscle fiber development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.010712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0044092~negative regulation of molecular function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.011126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0001765~membrane raft formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.011205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0045807~positive regulation of endocytosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.011464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0010876~lipid localization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.011736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0052547~regulation of peptidase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.012493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0010627~regulation of protein kinase cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.012994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0008092~cytoskeletal protein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.002399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0016504~peptidase activator activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.007702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0019899~enzyme binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.010389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0003779~actin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.01129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0004620~phospholipase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.012226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0008047~enzyme activator activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.012783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0008289~lipid binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.015001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0008034~lipoprotein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.017323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0019900~kinase binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.019893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0016298~lipase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.020073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0070290~NAPE-specific phospholipase D activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.028556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0042802~identical protein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.030878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0070064~proline-rich region binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.03417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0008035~high-density lipoprotein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.039752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0004630~phospholipase D activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.039752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0008139~nuclear localization sequence binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.039752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0019904~protein domain specific binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.042369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0050998~nitric-oxide synthase binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.050821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0004866~endopeptidase inhibitor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.05148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0030414~peptidase inhibitor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.058629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0030055~cell-substrate junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e4.14E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005886~plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.001132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005912~adherens junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.001789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005626~insoluble fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.002358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005925~focal adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.002595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0070161~anchoring junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.002815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005924~cell-substrate adherens junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.002983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005794~Golgi apparatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.00324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0009986~cell surface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.003371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0012505~endomembrane system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.004123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0031982~vesicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.004141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005624~membrane fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.005317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0048471~perinuclear region of cytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.00559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0016323~basolateral plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.00569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0044431~Golgi apparatus part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.006169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0030027~lamellipodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.007163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0044459~plasma membrane part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.008355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0030054~cell junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.008464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0009925~basal plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.009319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0045178~basal part of cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.012293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0031088~platelet dense granule membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.016805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0000267~cell fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.017154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0044421~extracellular region part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.017907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0000139~Golgi membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.020736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0031410~cytoplasmic vesicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.027418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005955~calcineurin complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.027853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0042827~platelet dense granule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.033332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005901~caveola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.03462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0016023~cytoplasmic membrane-bounded vesicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.034768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005615~extracellular space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.038156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0031988~membrane-bounded vesicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.040294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0042995~cell projection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.0416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0031252~cell leading edge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.042819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005856~cytoskeleton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.043217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0005576~extracellular region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.051766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22.6608%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64.6199%;\"\u003e\n \u003cp\u003eGO:0019898~extrinsic to membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.7193%;\"\u003e\n \u003cp\u003e0.059319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: GENE ONTOLOGY (GO) analysis for down regulated DEGs\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"686\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048704~embryonic skeletal system morphogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e1.89E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048706~embryonic skeletal system development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e6.00E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048705~skeletal system morphogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.002413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048562~embryonic organ morphogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.004473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048732~gland development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.004717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0009952~anterior/posterior pattern formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.005363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0043009~chordate embryonic development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.006282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0009792~embryonic development ending in birth or egg hatching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.006557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048568~embryonic organ development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.010931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0002520~immune system development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.012496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0003002~regionalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.01722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048598~embryonic morphogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.018973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0001501~skeletal system development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.021983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0060429~epithelium development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.027277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0007167~enzyme linked receptor protein signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.02859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0030097~hemopoiesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.030857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0030855~epithelial cell differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.031589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0014065~phosphoinositide 3-kinase cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.034668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0060216~definitive hemopoiesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.034668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0015031~protein transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.036558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0006508~proteolysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.036929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0007243~protein kinase cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.038188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0045184~establishment of protein localization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.038276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0006465~signal peptide processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.039523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0009057~macromolecule catabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.041345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048534~hemopoietic or lymphoid organ development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.04171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0007389~pattern specification process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.045236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0007242~intracellular signaling cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.047661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0007229~integrin-mediated signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.048307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0045022~early endosome to late endosome transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.04916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_BP_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0008380~RNA splicing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.054478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0044459~plasma membrane part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e1.39E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0005911~cell-cell junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e2.12E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0043296~apical junction complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.001043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0016327~apicolateral plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.001165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0048471~perinuclear region of cytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.001881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0030057~desmosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.003648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0005787~signal peptidase complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.013552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0070695~FHF complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.022487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0030054~cell junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.02936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0005886~plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.036058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0070161~anchoring junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.043114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0045177~apical part of cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.047573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_CC_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0030897~HOPS complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.053135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0042802~identical protein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.007236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.5335%;\"\u003e\n \u003cp\u003eGOTERM_MF_FAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.3878%;\"\u003e\n \u003cp\u003eGO:0001619~lysosphingolipid and lysophosphatidic acid receptor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0787%;\"\u003e\n \u003cp\u003e0.05657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometriosis, mRNA seq analysis, reproductive women, estrogen, aromatase","lastPublishedDoi":"10.21203/rs.3.rs-4928520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4928520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndometriosis 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\u0026ndash;10% of reproductive and 20\u0026ndash;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.\u003c/p\u003e","manuscriptTitle":"Bioinformatical Enrichment Analysis Reveals Key Differentially Expressed Genes in Endometriosis Pathogenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-27 12:16:06","doi":"10.21203/rs.3.rs-4928520/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a91a08be-0865-4d41-b75c-08290b986085","owner":[],"postedDate":"September 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38245919,"name":"Biological sciences/Genetics"},{"id":38245920,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2024-10-07T05:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-27 12:16:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4928520","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4928520","identity":"rs-4928520","version":["v1"]},"buildId":"0SHbDDIpRTBOrFPTvp6pu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}