Integrated bioinformatics analysis reveals key candidate genes and signaling pathways, and Macrphylloside D as novel therapeutic agent in ovarian cancer

Integrated bioinformatics analysis reveals key candidate genes and signaling pathways, and Macrphylloside D as novel therapeutic agent in ovarian cancer | 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 Research Article Integrated bioinformatics analysis reveals key candidate genes and signaling pathways, and Macrphylloside D as novel therapeutic agent in ovarian cancer Basavaraj Vastrad, Shivaling Pattanashetti, Chanabasayya Vastrad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7138516/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 Ovarian cancer is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways associated in ovarian cancer progression and advancement. The present study aimed to elucidate potential key genes and signaling pathways in ovarian cancer. Microarray dataset (GSE120196) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 10 ovarian cancer samples and 4 normal control samples. Differentially expressed genes (DEGs) were identified using limma R bioconductor package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis. Protein-protein interaction (PPI) network was performed based on the DEGs. The hub gene-related miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed. Subsequently, the DrugBank database was utilized to search for alternative drugs targeting ovarian cancer hub genes. Receiver operating characteristic (ROC) curve analysis was performed for hub genes. Finally, molecular docking studies and in-silico ADMET were performed. In this work, 38 DEGs, including 19 up regulated genes and 19 down regulated genes, were obtained from microarray data. GO and REACTOME pathway enrichment analyses revealed significant enrichment of these genes in cell adhesion, cell periphery, glycine N-benzoyltransferase activity and extracellular matrix organization. Five up regulated genes, COL1A1, COL1A2, F2R, VCAN and SERPINE2, and five down regulated genes, NR3C2, SELE, CXCL2, MYOM1 and TM4SF1 in the center of the PPI network were associated with ovarian cancer, and these hub genes showed high sensitivity and specificity in ROC curve analysis. Notably, hsa-miR-6515-5p, hsa-miR-6838-5p, FOXL1 and HOXA5 have been identified as promising miRNAs and TFs for regulation of hub gene expression in ovarian cancer. Drug molecules include vorapaxar, halofuginone, progesterone and ibuprofen were predicted for treatment ovarian cancer. From molecular docking studies and in-silico ADMET investigation revealed macrphylloside D is potential lead molecule for ovarian cancer treatment. This investigation could serve as a basis for further understanding the molecular pathogenesis and potential therapeutic targets of ovarian cancer. Bioinformatics gene expression omnibus (GEO) ovarian cancer receiver operating characteristic (ROC) differentially expressed genes (DEGs) molecular docking studies In-silico ADMET Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Introduction Between 1990 and 2021, the worldwide age-normalized rate of occurrence of ovarian cancer surged from 160,000 to nearly 300,000, but rates per 100,000 slightly declined [Li et al. 2025]. The frequency of ovarian cancer is increasing, and this disease is forecasted to become the seventh most common cancer in women and the eighth most common cause of cancer death worldwide [Webb and Jordan, 2017]. There are no known precaution measures and no competent screening tool [Choi and Choi, 2024]. Although confirmation suggests that the majority of women exposure a range of non-specific symptoms in the year before diagnosis, the disease it is not frequently identified until an leading stage, noted to increased mortality and morbidity [Mahoney and Pierce, 2022]. However, the survival rate is affected by several factors, and the prognosis of ovarian cancer remains extremely poor despite the adoption of multiple treatment strategies such as surgery [Nick et al. 2015], chemotherapy [Lloyd et al. 2015], targeted therapy [Coward et al. 2015], hormonal therapy [Simpkins et al. 2013], immunotherapy [Siminiak et al. 2022] and maintenance therapy [Walsh, 2020]. Thus, there is an urgent need to explore the molecular mechanisms of ovarian cancer and to identify effective biomarkers. There are several important risk factors for ovarian cancer, such as age [Maas et al. 2005], genetics [Hollis and Gourley, 2016], endometriosis [Brilhante et al. 2017], polycystic ovary syndrome [Zou et al. 2022], obesity [Liu et al. 2015], smoking [Wang et al. 2020], use of talcum powder [Saed et al. 2024], inflammation [Savant et al. 2018] and oxidative stress [Ding et al. 2021]. Therefore, the early prognosis and diagnosis of ovarian cancer remains a essential and concern for doctors and scientists, and examination of novel biomarkers and therapeutic targets ovarian cancer is imperative for doctors and patients alike. Although many biomarkers include PIK3CA [Kolasa et al. 2009], ARID1A [Kuroda et al. 2021], KRAS [Kim et al. 2020], PTEN [Martins et al. 2020] and NF1 [Su et al. 2019] have been studied as prognostic and diagnostic markers as well as therapeutic targets. Ovarian cancer has been genetically associated with signaling pathways such as PI3K/AKT/mTOR signaling pathway [Gasparri et al. 2017], RAS/RAF/MEK/ERK (MAPK) signaling pathway [Hendrikse et al. 2023], Wnt/β-catenin signaling pathway [Boone et al. 2016], notch signaling pathway [Akbarzadeh et al. 2020] and NF-κB signaling pathway [Leizer et al. 2011]. In particular, the novel molecular characteristics can be implemented in early risk assessment, the identification of better specific biomarkers for prognosis and diagnosis of ovarian cancer, and the improvement of clinic treatment and survival. Divergent from traditional research methods, the efficient application of microarray technology and the establishment of a global gene database provide broader and necessary data support for the diagnosis of ovarian cancer [Alur et al. 2019]. Meanwhile, the advancement of bioinformatics technology provides a reliable way to discover key regulatory genes and signaling pathways of cancer [Joshi et al. 2019; Alshabi et al. 2019; Vastrad et al. 2018]. On this basis, an increasing number of ovarian cancer related genes and signaling pathways have been discovered, and some of them have been proven to play an essential role in the onset and progression of cancer in subsequent validation. Our main purpose is to explore the molecular mechanism of ovarian cancer. First, we download the GSE120196 [Au-Yeung et al. 2020] dataset file in the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) [Clough and Barrett, 2016] for analysis, then use limma to draw the differentially expressed genes (DEGs) distribution map of the ovarian cancer and normal control samples in the dataset. Gene Ontology (GO) and pathway enrichment analysis of DEGs was undertaken with g:Profiler. Immediately afterward, the protein-protein interaction (PPI) network of DEGs is drawn and the hub genes are identified. Subsequently, the miRNA-hub gene regulatory network, TF-hub gene regulatory network, and drug-hub gene interaction network of hub genes is drawn and the microRNAs (miRNAs), transcription factors (TFs) and drugs are identified. The diagnostic value of hub genes was verified through receiver operating characteristic (ROC) curves analysis. molecular docking studies and in-silico ADMET were carried out. The final results will help us obtain novel targets and treatment for ovarian cancer. Methods and Materials Microarray data source GEO is a public functional genomics data repository of microarray data. The GSE120196 [Au-Yeung et al. 2020] dataset generated using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array was downloaded from GEO. The GSE120196 dataset contained 10 ovarian cancer samples and 4 normal control samples. Identification of DEGs Limma [Ritchie et al. 2014] is an R bioconductor package that allows users to determine DEGs for various experimental situations. The adjusted P-values (adj. P) and Benjamini and Hochberg false discovery rate was used to touch a balance between finding statistically important genes and limiting false positives [Green and Diggle, 2007]. The screening criteria of DEGs was set as adj.P.Val ≤ 0.05, |log2 fold change (FC) | > 0.95 for up regulated genes and |log2 fold change (FC) | < -1.1 for down regulated genes . The screening results were then presented in the form of volcano plots and heat maps. GO and pathway enrichment analyses of DEGs g:Profiler (http://biit.cs.ut.ee/gprofiler/) [Reimand et al. 2007] is an analytical website which incorporates functional enrichment analysis, gene annotation and membership search in a comprehensive portal. Gene Ontology (GO) (http://www.geneontology.org) [Thomas, 2017] is a premier bioinformatics program for high-quality functional gene annotation based on biological processes (BP), cellular components (CC) and molecular functions (MF). The REACTOME (https://reactome.org/) [Fabregat et al. 2018] is a resource of pathway database for the clarification of high-level features and effects of biological systems. P < 0.05 was considered statistically significant. Construction of the PPI network The International Molecular Exchange Consortium (IMex) (https://www.imexconsortium.org/) [Porras et cal. 2020] was used to create a PPI network of ovarian cancer DEGs to predict PPI and the functions of the DEGs. Subsequently, Cytoscape software (v3.10.3) (http://www.cytoscape.org/) [Shannon et al. 2003] was used to visualize and analyze biological networks. Then, Network Analyzer plug-in of Cytoscape were used to recognize the interaction degree of candidate gene clustering according to 4 types of algorithms include node degree [Luo et al. 2017], betweenness [Li et al. 2017], stress [Gilbert et al. 2021] and closeness [Li et al. 2020]. Construction of the miRNA-hub gene regulatory network The miRNA-hub gene regulatory network was used to investigate the regulation mechanism of the hub genes. The miRNA - hub gene interaction data were collected from miRNet database (https://www.mirnet.ca/) [Fan et al 2018]. We used 14 miRNA databases to predict the target miRNA: TarBase, miRTarBase, miRecords, miRanda, miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0. Cytoscape software [Shannon et al. 2003] was used to visualize the miRNA-hub gene regulatory network. The connectivity degrees were calculated through network statistical methods. Construction of the TF-hub gene regulatory network The TF-hub gene regulatory network was used to investigate the regulation mechanism of the hub genes. The TF - hub gene interaction data were collected from NetworkAnalyst database (https://www.networkanalyst.ca/) [Zhou et al 2019]. We used one TF database to predict the target TF: JASPAR. Cytoscape software [Shannon et al. 2003] was used to visualize the TF-hub gene regulatory network. The connectivity degrees were calculated through network statistical methods. Construction of the drug-hub gene interaction network The drug-hub gene regulatory network was used to investigate the drug molecule interaction on hub genes. The drug - hub gene interaction data were collected from NetworkAnalyst database (https://www.networkanalyst.ca/) [Zhou et al 2019]. We used one drug database to predict the target drug: DugBank. Cytoscape software [Shannon et al. 2003] was used to visualize the drug-hub gene interaction. The connectivity degrees were calculated through network statistical methods. Receiver operating characteristic curve (ROC) analysis The diagnostic performance of hub gene expression levels was subsequently evaluated using ROC curves. The pROC R bioconductor package [Robin et al 2011] was used to plot the ROC curves for the diagnostic model in both the training and validation sets and to analyze the ability of hub genes to distinguish ovarian cancer samples from normal controls samples. The area under the curve (AUC) value was determine to evaluate the model’s diagnostic performance, with an AUC value greater than 0.8 considered indicative of diagnostic value. Insilico molecular docking studies Swiss-model, RCSB PDB, Prank web, ChemDraw, Avogadro tool, Autodock 1.7.1, and Autodock Vina tools, Biovia Discovery Studio client 2021, ADMET lab 3.0 web server. Receptor and ligand preparation The RCSB Protein Data Bank (PDB) [Goodsell DS et al 2020] provided the PDB IDs that were used to retrieve the 3D crystal structure of the corresponding proteins from the corresponding genes. The selected targets with their structural information are presented in Table 1. Using the Swiss-Model web server, the missing residues were remodelled and downloaded in pdb format [Waterhouse A et al 2018]. Following the identification of the binding sites utilizing the server Prank web [Jendele L et al 2019], the protein's pdb format was entered into Software Auto Dock Tools 1.7.1, and water molecules and atoms were eliminated. The receptor has also been checked for missing amino acid residues, Kollman charges have been fixed, and only polar hydrogens have been inserted. In order to cover the entire receptor, the grid was fixed using the Autogrid program. The grid dimension file [Morris GM et al 2008] was then saved. The phytoconstituents were selected from the plant Actinodaphne angustifolia, which was previously reported by [Forid MS et al 2021]. Ligand structures were created using ChemDraw and saved as SMILES. The phytoconstituents and co-crystallized ligands were given in Fig. 1.They were then loaded into Avogadro software to convert them from 2D to 3D structures in pdb format [Hanwell MD et al 2012]. The ligand's pdb format was then entered into Auto Dock Tools 1.7.1, and the ligand molecule's root was identified and selected. Lastly, the pdbqt format was used to save the ligand molecule. Performing AutodockVina Auto Dock Vina can be executed using the command line (cmd) or the Autodock tool. The configuration file was ready for Autodock Vina to execute; the grid dimension file that was previously saved includes the protein's n-points, active site, and x, y, and z coordinates. That information was added to the configuration file, which was made to contain the protein's active site details. It also comes with an output file in pdbqt format and a log file in .txt format. The command line was used to run Autodock Vina. Vina.exe -- config config.txt was the command used to launch Auto Dock Vina. Docked coordinates were output in the pdbqt format when the program was finished. Receptor-ligand interactions were then visualized using the file, and binding affinity was ascertained using the log.txt file [Trott O et al 2010]. Visualization The Biovia Discovery Studio Client 2021 program has been used to visualize the docking results. The Discovery Studio Client 2011 program was used to import the output pdbqt file and the receptor pdbqt file format. Next, a PNG file is created from the 3D image of the docked ligand and the 2D image of the docked ligand that is attached to various amino acids [James JJ et al 2025]. In silico ADMET properties ADMET properties were predicted with the help of ADMET lab free web server, as previously reported by [ Fu L et al 2024 ] Results Identification of DEGs The DEGs in ovarian cancer samples from normal controls samples in the gene expression was analyzed by the R bioconductor package ‘limma (version 3.5.1)’. In total, 38 DEGs were identified, including 19 up regulated genes and 19 down regulated genes (Table 2). All DEGs were described by the volcano plot (Fig. 2). Volcano plot with cut-off criteria set to adj.P.Val ≤ 0.05, |log2 fold change (FC) | > 0.95 for up regulated genes and |log2 fold change (FC) | < -1.1 for down regulated genes .The heatmap shows the expression of the DEGs (Fig.3). GO and pathway enrichment analyses of DEGs GO and REACTOME pathway enrichment analyses were performed to investigate the functions of DEGs. For GO BP, DEGs were mainly enriched in cell adhesion and response to bacterium (Table 3). For CC, the obtained results indicated that proteins encoded by DEGs were mostly located in the cell periphery and endomembrane system (Table 3). For GO MF, the obtained results indicated that DEGs were significantly associated with glycine N-benzoyltransferase activity and HMG box domain binding (Table 3). The results of REACTOME pathway enrichment analysis showed that the pathways associated with extracellular matrix organization and common pathway of fibrin clot formation (Table 4). Construction of the PPI network A total of 38 DEGs were imported into the IMex database online database to construct the PPI network. In the Cytoscape platform for up regulated and down regulated genes, we found 233 nodes and 256 edges (Fig.4). After topological analysis, the most connected up regulated genes COL1A1, COL1A2, F2R, VCAN and SERPINE2, and the most connected down regulated genes NR3C2, SELE, CXCL2, MYOM1 and TM4SF1 were categorized according to their highest node degree, betweenness, stress and closeness are associated with ovarian cancer (Table 5). Construction of the miRNA-hub gene regulatory network RNA synthesis. miRNA up and down regulation deficiency are associated with ovarian cancer and they have an ability to differentiate between benign and malignant carcinomas [Zhao et al 2022] and the disease complication can be more readable by miRNA changes. The regulatory network contained 805 miRNAs and 24 hub genes with 2513 interaction (edges) (Fig.5). Further results demonstrated that COL1A1 is associated 280 miRNAs (ex; hsa-miR-6515-5p), VCAN is associated 233 miRNAs (ex; hsa-miR-518a-3p), COL1A2 is associated 197 miRNAs (ex; hsa-miR-497-5p), SERPINE2 is associated 178 miRNAs (ex; hsa-miR-146a-5p), COL3A1 is associated 139 miRNAs (ex; hsa-miR-24-3p), TM4SF1 is associated 208 miRNAs (ex; hsa-miR-6838-5p), NR3C2 is associated 129 miRNAs (ex; hsa-miR-135a-5p), THBD is associated 76 miRNAs (ex; hsa-miR-196b-5p), CXCL2 is associated 66 miRNAs (ex; hsa-miR-200a-3p) and MYOM1 is associated 65 miRNAs (ex; hsa-miR-151a-3p) (Table 6). Construction of the TF-hub gene regulatory network TFs play a critical role in cancer progression by regulating gene expression involved in cell growth, survival, angiogenesis, immune evasion, and metastasis [Li et al 2021]. TFs are frequently implicated in ovarian cancer. The regulatory network contained 56 TFs and 23 hub genes with 180 interaction (edges) (Fig.6). Further results demonstrated that SERPINE2 is associated 14 TFs (ex; FOXL1), COL3A1 is associated 8 TFs (ex; STAT1), COL1A2 is associated 8 TFs (ex; PPARG), VCAN is associated 8 TFs (ex; NFKB1), COL1A1 is associated 5 TFs (ex; SREBF1), CXCL2 is associated 11 TFs (ex; HOXA5), SELE is associated 11 TFs (ex; JUN), THBD is associated 11 TFs (ex; USF2), MYOM1 is associated 6 TFs (ex; GATA2) and TM4SF1 is associated 5 TFs (ex; NFIC) (Table 6). Construction of the drug-hub gene interaction network Drugs can alter gene expression through various mechanisms, depending on the type of drug, target pathway, and cellular context [Koussounadis et al 2014]. Drugs are frequently implicated in ovarian cancer. The drug-hub gene interaction network shown in Fig.7. Further results demonstrated that F2R is targeted with 4 drugs (ex; vorapaxar), COL1A1 is targeted with 2 drugs (ex; halofuginone), NR3C2 is targeted with 11 drugs (ex; progesterone) and THBD is targeted with 2 drugs (ex; Ibuprofen) (Table 7). Receiver operating characteristic curve (ROC) analysis ROC analysis was performed to evaluate the specificity and sensitivity of the four hub genes. The results for the ovarian cancer biomarkers were favorable, with COL1A1 (AUC = 0.931), COL1A2 (AUC = 0.910), NR3C2 (AUC = 0.934) and SELE (AUC = 0.917) exhibiting robust predictive performance (Fig.8.). In silico molecular docking studies To assess the binding affinities and interaction patterns of two specific phytoconstituents, macrophylloside D and Dichotomitin, against four important ovarian cancer-related targets, COL1A1, COL1A2, NR3C2, and SELE, molecular docking studies were performed. Thesereceptorsall hadaknownco-crystallisedligandthatwasusedasareferencepoint. Initially occupied by D-glucopyranose, the active binding pocket in chain A of the COL1A1 receptor (PDB ID: 3EJH) showed noticeably greater interactions with macrophylloside. With a binding affinity of -8.3 kcal/mol, macrophylloside connected electrostatically with TYR579, hydrophobically with VAL580, and formed hydrogen bonds with GLU577, ARG584, LYS578, and HIS581. Conversely, Dichotomitin exhibited a clear hydrogen bonding profile and fewer hydrophobic contacts while binding with a moderate affinity of -7.1 kcal/mol on average.In contrast, the native ligand D-glucopyranose showed far poorer binding at -4.8 kcal/mol, suggesting that it had little interaction with the active site. The COL1A2 receptor (PDB ID: 5CTI), which has glycerol as the co-crystallised ligand, similarly demonstrated increased binding to Macrophylloside D (-6.2 kcal/mol) in comparison to Dichotomitin (-5.5 kcal/mol) and glycerol (-2.6 kcal/mol). Along with hydrophobic interactions with ALA57 and SER53, macrophylloside also produced numerous hydrogen bonds with GLU46, ARG45, SER53, GLY70, and GLU54. A less stable complex formation with the target was suggested by the weaker and fewer interactions that dichotometin had with the target. Binding affinity is given in Table 8 and 2D, 3D images of amino acid interaction are given in Fig 9 to Fig 12. Macrophylloside once more demonstrated high binding with a binding energy of -7.7 kcal/mol for the NR3C2 receptor (PDB ID: 2AA2), a known target downregulated in MDD that complexed with aldosterone. It interacted with ASP933, SER936, HIS932, VAL971, and GLY974 via hydrogen bonds. Additionally, it developed strong hydrophobic ties with LEU939, VAL971, and PRO978 as well as electrostatic ties with SER936. With a -7.3 kcal/mol affinity, Dichotomitincame next, interacting with some residues, including TYR804 and HIS932. Even though aldosterone had the highest binding score (- 8.9 kcal/mol), it also had a more constrained interaction profile, which could compromise stability in vivo.Another downregulated target, the SELE receptor (PDB ID: 1ESL), had calcium ions as a co-crystallised ligand. In terms of docking affinity, both phytoconstituents performed noticeably better than calcium. In addition to interacting electrostatically and hydrophobically, Dichotomitin formed hydrogen bonds with GLY131, CYS122, and THR65 while bound with -7.4 kcal/mol. Following closely behind at -7.2 kcal/mol, macrophylloside D formed hydrophobic contacts with VAL56 as well as hydrogen bonds with ARG54, GLN85, and ASP89. Its limited involvement in complex formation was further confirmed by the calcium ion ligand's very weak binding energy of -1.6 kcal/mol and negligible interaction. Binding affinity is given in Table 9 and 2D, 3D images of amino acid interaction are given in Fig.13 to Fig.16. Overall, Macrophylloside demonstrated better binding compatibility and stability than Dichotomitinand the co-crystallised ligands, as evidenced by greater binding affinities and more advantageous interaction networks across all four receptors. ADMET and Drug-Likeness The drug-likeness potential of Macrophylloside and Dicomentin with the native ligands was further confirmed by the in silico ADMET study. With a bioavailability score of 0.55, macrophylloside outperformed Dichotomitin (0.08) and was on par with the common reference ligands, such as glycerol and aldosterone.Both substances demonstrated outstanding human intestinal absorption (HIA >90%) in terms of absorption characteristics; however, only aldosterone showed expected blood–brain barrier (BBB) permeability. Interestingly, unlike Dichotomitin, macrophylloside does not function as a substrate for P-glycoprotein, suggesting a lesser risk of drug efflux and improved cellular retention.CYP1A2 and CYP2C19 are not inhibited by either phytoconstituent in terms of metabolic interactions. Both were anticipated to inhibit CYP2C9 and CYP3A4, though, which may present a moderate risk of drug-drug interactions when polypharmacy is in effect. Aldosterone's metabolic compatibility was further restricted by its inhibition of CYP2D6.Macrophylloside (Fu = 0.068) and Dichotomitin (Fu = 0.26), which favour systemic availability, showed moderate to low plasma protein binding, according to the distribution profile, which is indicated by fraction unbound (Fu). The ideal total clearance demonstrated favourable excretion potential for macrophylloside (log CL = 0.582 mL/min/kg), which was significantly greater than the insignificant clearance anticipated for Dichotomitin and aldosterone. Results were given in Table 10. Both Macrophylloside and Dicomentin have moderate oral rat acute toxicity, according to the toxicity profile (LD₅₀ values of 0.46 and 0.313 mol/kg, respectively). Crucially, hepatotoxicity was anticipated for both phytoconstituents, albeit it was minimal. whereas it was anticipated that co-crystallised ligands such as D-glucopyranose and glycerol would not be hepatotoxic. Discussion Despite decades of investigation, the genetic alteration that make ovarian cancer pathogenic and relapses remain largely unknown. Aberrant levels of cell cycle pathway genes [Cunningham et al 2009], cell adhesion genes [Rafii et al 2012], and DNA methylation [Papakonstantinou et al 2021] in ovarian cancer patients have been reported in recent investigation with encouraging prospects. However, existing investigation unsuccessful to contribute enough information to explain the gene regulatory mechanism of oncoprotein expression. Therefore, an in-depth investigation of the DEGs in ovarian cancer patients is accessible to illuminate the molecular mechanism of the cancer and is expected to provide ket targets for diagnosis and treatment. Here, by comprehensive bioinformatics analyses of public microarray dataset, GSE120196, we screened the 38 DEGs (19 up regulated genes and 19 down regulated genes) between ovarian cancer samples and normal control samples and verified the diagnostic ability of genes as biomarkers for disease. Recent research suggested that SERPINE2 [Botteri et al 2024], COL1A1 [Shi et al 2025], COL1A2 [Xu et al 2024], VCAN (versican) [Wight et al 2020] and MYOM1 [Chen et al 2022] are linked with inflammation. COL1A1 [Xiao et al 2025] is linked with the proliferation and invasion of ovarian cancer cells. The functional role of VCAN (versican) [Zhou et al 2025] is regulation of ovarian cancer cell invasion and motility potential. Studies have already confirmed that high TM4SF1 expression in ovarian cancer can control ovarian cancer cell invasion and metastasis [Huang et al 2023]. COL1A1 [Druso et al 2024] and VCAN (versican) [Wu et al 2005] might be involved in the oxidative stress. These studies suggest that significant DEGs might be involved in the development of ovarian cancer. Gene Ontology (GO) and pathway enrichment analysis can help us better understand the specific molecular pathogenesis of ovarian cancer. Extracellular matrix organization [Puttock et al 2023] and cell adhesion [Elmasri et al 2009] were responsible for progression of ovarian cancer. BGN (biglycan) plays a vital role in modulating cellular adhesion, migration, cell proliferation and motility in ovarian cancer [Fang et al 2025]. COL6A3 [Ho et al 2024] has been reported to be associated tumor invasion and metastasis in ovarian cancer. COL3A1 have been associated to increased proliferation, invasion, migration and drug resistance in ovarian cancer [Yang et al 2025]. Some studies show that SPON1 plays an important role in chemoresistance in ovarian cancer [Nagasawa et al 2022]. VMP1 [Liu et al 2014] has been linked to ovarian cancer cell invasion and metastasis. SELE (selectin E) [Yang et al 2023] facilitates the adhesion of circulating ovarian cancer cell, leading to the preferential homing and retention of metastatic ovarian cancer cell. THBD (thrombomodulin) expression might control cell growth and migration in ovarian cancer cells [Chen et al 2013]. BGN (biglycan) [Guo et al 2019], COL6A3 [Gesta et al 2016], SELE (selectin E) [Yang et al 2023] and THBD (thrombomodulin) [Yang et al 2016] genes have been demonstrated to be responsible for inflammation. Previous studies have shown that the altered level of BGN (biglycan) [Szabados et al 2024], COL6A3 [Li et al 2020] and SELE (selectin E) [Zhang et al 2023] might be closely associated with oxidative stress. The identified enriched genes might provide new therapeutic targets for the cancer therapy of patients with ovarian cancer. We performed PPI network construction and analysis to investigate the hub genes related to ovarian cancer. COL1A1 [Xiao et al 2025], VCAN (versican) [Zhou et al 2025], SELE (selectin E) [Yang et al 2023], CXCL2 [Zhang et al 2021] and TM4SF1 [Huang et al 2023] promotes the development and progression of ovarian cancer. COL1A1 [Shi et al 2025], COL1A2 [Xu et al 2024], VCAN (versican) [Wight et al 2020], SERPINE2 [Botteri et al 2024], NR3C2 [Huang et al 2022], CXCL2 [Liu et al 2021] and MYOM1 [Chen et al 2022] pays a major role in the occurrence and development of inflammation. COL1A1 [Druso et al 2024], VCAN (versican) [Wu et al 2005], SELE (selectin E) [Zhang et al 2023] and CXCL2 [Pan et al 2024] are promising as a novel targets for oxidative stress. The findings from the present study indicated that the F2R gene might be a new biomarker for ovarian cancer. These findings support the potential role of hub genes as a therapeutic targets for the treatment of ovarian cancer. To further investigate the factors that might affect the expression of our hub genes, we identified miRNAs and TFs that might interacts. However, the top-ranking predicted miRNAs and TFs were hsa-miR-146a-5p [Takamizawa et al 2023], STAT1 [Yu et al 2024], PPARG [Luo et al 2015], NFKB1 [Bai et al 2022], SREBF1 [Wang et al 2021], HOXA5 [Zhao et al 2018], JUN [Eckhoff et al 2013] and GATA2 [Erceylan et al 2021] are associated with ovarian cancer. Hsa-miR-6515-5p [Son et al 2022], hsa-miR-518a-3p [Yin et al 2022], hsa-miR-135a-5p [Li et al 2020], hsa-miR-196b-5p [Zhang et al 2024], STAT1 [Ploeger et al 2022], PPARG [Geng et al 2024], NFKB1 [Cartwright et al 2016], HOXA5 [Zhu and Ma, 2021], JUN [Schonthaler et al 2011], GATA2 [Baba et al 2024] and NFIC [Zhang et al 2021] expression might be a shared target for inflammation. hsa-miR-518a-3p [Yin et al 2022], STAT1 [Totten et al 2021], PPARG [Wang et al 2022], NFKB1 [Guo et al 2021], SREBF1 [Okuno et al 2018], HOXA5 [Saijo et al 2016], JUN [Liu et al 2017] and GATA2 [Huang et al 2023] have been reported its expression in the oxidative stress. However, the roles of hsa-miR-497-5p, hsa-miR-24-3p, hsa-miR-6838-5p, hsa-miR-200a-3p, hsa-miR-151a-3p and USF2 in ovarian cancer have not been reported until now. These studies are consistent with the results of our data mining in which miRNA and TFs for ovarian cancer. Furthermore, we got the drug-hub gene interaction results from the DrugBank database. A total of 23 drugs or small molecules for ovarian cancer treatment were presented. Four targetable drugs (vorapaxar, halofuginone, progesterone and ibuprofen) were might be used for ovarian cancer treatment. Thus, these four candidate genes (F2R, COL1A1, NR3C2 and THBD) might be potential targets for ovarian cancer treatment, which is needed to be evaluated in further investigation. Important information about the binding preferences and interaction profiles of the two phytoconstituents, macrophylloside and Dichotomitin, against important targets linked to OC was uncovered by the molecular docking research. Of particular interest were interactions at the amino acid level within the binding sites. COL1A, the ligand with the highest binding affinity for COL1A1, was macrophylloside (−8.3 kcal/mol). Strong polar stabilisation within the active site is facilitated by the important hydrogen bonding interactions with GLU577, ARG584, LYS578, and HIS581 that were identified in the binding pocket. A well-fitting and complex binding orientation was also suggested by the substantial hydrophobic contact with VAL580 and the electrostatic interaction with TYR579,which was also seen. A wide variety of side chain chemistries were involved in these interactions, which enabled the ligand to be stabilised inside the pocket by advantageous electrostatic, hydrogen bonding, and van der Waals forces. In contrast, Dicomentin interacted with fewer important residues despite having a comparatively high docking score (-7.1 kcal/mol). Although it also engaged GLU565 via electrostatic interaction, its hydrogen bonding was restricted to GLU577, GLU565, and TYR579; its slightly lower binding energy may be explained by the lack of multi-type interactions with core residues such as ARG584 or HIS581. With a binding score of -4.8 kcal/mol, the co-crystallised ligand D-glucopyranose demonstrated only modest interactions, mostly with TYR578, LYS579, and TYR570, indicating its lower affinity and probably structural function rather than pharmacological significance. COL1A2, Macrophylloside once more showed good binding (−6.2 kcal/mol) and established a strong network of hydrogen bonds with GLU46, ARG45, SER53, GLY70, and GLU54. For the ligand to be anchored deeply within the binding groove, these interactions are probably essential. Through nonpolar interactions, the chemical also demonstrated hydrophobic connections with SER53 and ALA57, improving binding stability.Dicomentin only formed one hydrogen bond with GLN58 and had a lower binding energy (−5.5 kcal/mol). It also had little electrostatic interaction with GLU54. One possible explanation for its decreased affinity is the lack of more extensive polar or nonpolar interactions. Glycerol, the native ligand, only formed weak hydrogen bonds with ALA68, GLY70, and showed negligible binding (−2.6 kcal/mol). Macrophylloside had a strong and competitive binding energy (- 7.7 kcal/mol) in the instance of NR3C2, even though the co-crystallised ligand Aldosterone had the highest docking score (- 8.9 kcal/mol). With ASP933, SER936, HIS932, VAL971, and GLY974, it established a vast network of hydrogen bonds that extend across the binding pocket's entrance and core. Its binding position was further cemented by hydrophobic interactions with LEU939, VAL971, and PRO978 as well as electrostatic stabilisation via SER936, which may have improved residence time and inhibitory potential.Dichotomitin, came next (−7.3 kcal/mol), and hydrogen bonds were made with HIS932, THR880, TYR804, SER936, and GLU967. It did not, however, have electrostatic interactions, which could weaken the binding force overall. Although they weren't as strong as those of macrophylloside, hydrophobic interactions with VAL935, TYR804, ALA976, and LEU939 probably aid in pocket fitting. Upon docking with the phytoconstituents, the SELE receptor, co-crystallised with a calcium ion (binding score: −1.6 kcal/mol), showed a significant increase in affinity. A binding energy of -7.4 kcal/mol was obtained by Dichotomitin, which additionally engaged GLU135 in electrostatic contact and generated many hydrogen bonds with GLY131, CYS122, and THR65. Stable binding was additionally reinforced by hydrophobic interactions with CYS133, ALA120, and CYS122.In addition to electrostatically interacting with LYS55 and ARG54, macrophylloside demonstrated a similar docking score (-7.2 kcal/mol) and established robust hydrogen bonds with ARG54, GLN85, and ASP89. Its stabilising interactions were augmented by a crucial hydrophobic interaction with VAL56. These residues may interfere with protein-protein recognition interactions that are crucial for inflammation-related signalling in OC, as they are found near the interface of the E-selectin binding domain. ADMET and Drug-Likeness Using well-established prediction models, an in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis was conducted to assess the potential for medication development of the chosen phytoconstituents. Macrophylloside, Dichotomitin, and co-crystallised ligands, including D-glucopyranose, Glycerol, and Aldosterone, were all included in the comparative study. Bioavailability and absorption With a bioavailability score of 0.55, macrophylloside showed a significant potential for systemic availability following oral treatment. Dichotomitin, on the other hand, had a very low score (0.08), indicating either first-pass metabolism or limited absorption. Furthermore, both phytoconstituents demonstrated good permeability through the intestinal epithelium, as evidenced by their high projected Human Intestinal Absorption (HIA > 90%). The fact that macrophylloside was not anticipated to be a P-glycoprotein (P-gp) substrate points to improved intracellular retention and a decreased probability of active efflux. However, Dichotomitin, was identified as a P-gp substrate, which may jeopardise its therapeutic efficacy and intracellular concentration. Distribution Macrophylloside and Dichotomitin, both demonstrated relatively high protein binding, as indicated by their respective per cent unbound (Fu) values of 0.068 and 0.26 for plasma protein binding. A longer circulation time but less free medication accessible for action could be indicated by a lower Fu. Crucially, neither phytoconstituent was expected to penetrate the blood–brain barrier (BBB), which may restrict CNS action while lowering the possibility of neurotoxicity, a desirable property for adjuncts of antidepressants that do not target the central nervous system. Metabolism Neither chemical inhibited CYP1A2 or CYP2C19, which is favourable for reducing drug-drug interactions in terms of metabolic liability. Nevertheless, it was anticipated that both would inhibit CYP2C9 and CYP3A4, which could raise issues about metabolic competition with other isoenzyme substrates. The co-crystallised ligand for NR3C2, aldosterone, was more metabolically disruptive than the phytoconstituents since it also inhibited CYP2D6. Excretion In comparison to Dichotomitin (~0.0043), macrophylloside exhibited a comparatively high total clearance (log CL = 0.582 mL/min/kg), indicating superior excretion via hepatic or renal pathways. Its ability to prevent toxicity and long-term buildup is supported by this increased clearance rate. Toxicity Both phytoconstituents showed moderate oral acute toxicity from a toxicological point of view, with LD₅₀ values of 0.313 mol/kg for Dichotomitin and 0.46 mol/kg for macrophylloside. Macrophylloside appears to be less acutely hazardous, even if these levels are within permissible bounds for natural substances. Although hepatotoxicity was expected for both, it was characterised as having a low probability, indicating adequate margins for hepatic safety. Glycerol and D-glucopyranose, on the other hand, lacked drug-like action and binding affinity despite being projected to be non-hepatotoxic. Conclusion The present investigation identified key genes and signaling pathways which might be involved in ovarian cancer advancement through the integrated analysis of NGS dataset. These results might contribute to a better understanding of the molecular mechanisms which underlie ovarian cancer and provide a series of potential and novel biomarkers. Additionally, the majority of included investigation focused on how a single essential gene and signaling pathway contribute to the advancement of ovarian cancer, with limited investigation concerning the interaction of genes, miRNA, TFs and drug molecules. Further studies are needed to confirm our putative finding. Among all OC -associated targets, macrophylloside regularly showed the highest binding affinity. It created broad hydrogen bonds with vital active site residues and multi-type interactions (hydrophobic, electrostatic) that are necessary for precise and long-lasting binding.Although Dichotomitin was fairly active, it did not have the same density and diversity of interactions as macrophylloside. Lower or less specific binding was demonstrated by co-crystallised ligands such as D-glucopyranose, glycerol, calcium, and aldosterone, demonstrating the superiority of the chosen phytoconstituents in targeting these receptors. ADMET and Drug-Likeness Macrophylloside is a good option for oral delivery because of its good intestinal absorption, mild protein binding, and acceptable bioavailability. It's little hepatotoxicity, non-BBB permeability, and lack of P-gp substrate status all contribute to systemic safety. The risk of buildup is decreased by its increased clearance rate, and despite possible CYP inhibition, its overall metabolic profile is controllable. The pharmacokinetic and toxicological profile of Macrophylloside is the best balanced when compared to Dichotomitin and all other co-crystallised ligands. The emergence of macrophylloside as a potential multi-target phytoconstituent for further study in ovarian cancer medication discovery is facilitated by its strong receptor binding profiles and beneficial ADMET characteristics. According to its findings, it may be used as a lead chemical to create safer, more, oral bio-available and natural anticancer agents for ovarian cancer. Declarations Acknowledgement I thanks very much to Au Yeung CL, Yeung TL, Mok SC, UT MD Anderson Cancer Center, Houston, USA, the authors who deposited their microarray dataset GSE120196, into the public GEO database. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent No informed consent because this study does not contain human or animals participants. Availability of data and materials The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE120196) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120196] Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author Contributions B. V. - Writing original draft, and review and editing S.P. - Formal analysis and validation C. V. - Software and investigation Authors Basavaraj Vastrad ORCID ID: 0000-0003-2202-7637 Shivaling Pattanashetti ORCID ID: 0009-0003-9246-1604 Chanabasayya Vastrad ORCID ID: 0000-0003-3615-4450 References Akbarzadeh M, Akbarzadeh S, Majidinia M. Targeting Notch signaling pathway as an effective strategy in overcoming drug resistance in ovarian cancer. Pathol Res Pract. 2020;216(11):153158. doi:10.1016/j.prp.2020.153158 Alshabi AM, Vastrad B, Shaikh IA, Vastrad C. 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J Ovarian Res. 2022;15(1):30. doi:10.1186/s13048-022-00962-w Tables Table 1. Selected drug targets with their structural information. Drug target gene Topology of genes in MDD PDB ID Resolution (Å) Method of collection Observed R-value COL1A1 Up regulated 3EJH 2.10 X-ray diffraction 0.210 COL1A2 Up regulated 5CTI 1.90 X-ray diffraction 0.163 NR3C2 Down regulated 2AA2 1.95 X-ray diffraction 0.214 SELE Down regulated 1ESL 2.00 X-ray diffraction 0.164 Table 2 The statistical metrics for key differentially expressed genes (DEGs) Gene Symbol logFC pValue tvalue Regulation GeneName SERPINE2 2.705479 6.66E-07 8.005357 Up serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2 COL1A1 3.984305 1.25E-06 7.612776 Up collagen, type I, alpha 1 SFRP2 2.481883 3.95E-06 6.929767 Up secreted frizzled-related protein 2 COL1A2 2.592584 8.62E-06 6.485731 Up collagen, type I, alpha 2 VCAN 3.043548 1.01E-05 6.39482 Up versican SPON1 1.943975 1.06E-05 6.368547 Up spondin 1, extracellular matrix protein BGN 3.053935 1.11E-05 6.344523 Up biglycan COL6A3 1.414595 1.15E-05 6.326491 Up collagen, type VI, alpha 3 RUNX1 1.75519 1.16E-05 6.321531 Up runt-related transcription factor 1 COL3A1 1.992717 1.22E-05 6.293766 Up collagen, type III, alpha 1 TUG1 0.985688 1.94E-05 6.039396 Up taurine up-regulated 1 (non-protein coding) NT5DC2 0.970153 1.97E-05 6.03119 Up 5'-nucleotidase domain containing 2 PRRX1 1.02811 3.47E-05 5.728486 Up paired related homeobox 1 COL16A1 2.056656 5.63E-05 5.473747 Up collagen, type XVI, alpha 1 C4B_2 1.488349 6.95E-05 5.36537 Up complement component 4B (Chido blood group), copy 2 SEPT8 1.104221 7.31E-05 5.339117 Up septin 8 F2R 2.069188 7.35E-05 5.336208 Up coagulation factor II (thrombin) receptor STAB1 1.215354 7.97E-05 5.294708 Up stabilin 1 VMP1 2.212453 8.46E-05 5.263772 Up vacuole membrane protein 1 GPM6A -1.22974 4.16E-08 -9.8873 Down glycoprotein M6A TM4SF1 -1.50643 8.12E-07 -7.8812 Down transmembrane 4 L six family member 1 GLYAT -1.23944 8.81E-07 -7.83062 Down glycine-N-acyltransferase C2orf40 -2.23128 9.26E-07 -7.79952 Down chromosome 2 open reading frame 40 MYOM1 -1.23194 1.65E-06 -7.4467 Down myomesin 1 STXBP6 -1.57584 1.85E-06 -7.37721 Down syntaxin binding protein 6 (amisyn) DEFB132 -1.29844 2.57E-06 -7.18026 Down defensin, beta 132 LOC101926960 -1.54525 3.10E-06 -7.07109 Down uncharacterized LOC101926960 C8orf34 -1.17111 4.80E-06 -6.81691 Down chromosome 8 open reading frame 34 KANK4 -1.52627 6.54E-06 -6.64089 Down KN motif and ankyrin repeat domains 4 TRDN -1.66658 8.50E-06 -6.49338 Down triadin CXCL2 -2.9814 2.00E-05 -6.02196 Down chemokine (C-X-C motif) ligand 2 NR3C2 -1.3474 3.90E-05 -5.66618 Down nuclear receptor subfamily 3, group C, member 2 CSF3 -1.35389 4.09E-05 -5.64129 Down colony stimulating factor 3 (granulocyte) SELE -3.58351 4.85E-05 -5.55206 Down selectin E THBD -1.206 6.85E-05 -5.37288 Down thrombomodulin LINC00968 -1.98628 6.99E-05 -5.36194 Down long intergenic non-protein coding RNA 968 LRRN3 -1.40976 7.79E-05 -5.30646 Down leucine rich repeat neuronal 3 MAMDC2 -2.3466 8.19E-05 -5.28032 Down MAM domain containing 2 Table 3 The enriched GO terms of the up and down regulated differentially expressed genes GO ID CATEGORY GO Name adjusted_p_value negative_log10_of_adjusted_p_value Gene Count Gene GO:0007155 BP cell adhesion 0.000129946 3.886238721 13 SERPINE2,COL1A1,SFRP2,VCAN,SPON1, COL6A3,LOC100506403,COL3A1,COL16A1, STAB1,VMP1,STXBP6,SELE GO:0009617 BP response to bacterium 0.000129946 3.886238721 10 COL1A1,SFRP2,COL1A2,VCAN, BGN,LOC100506403,COL3A1,PRRX1 GO:0071944 CC cell periphery 0.000687742 3.162574496 20 SERPINE2,COL1A1,SFRP2,COL1A2, VCAN,SPON1,BGN,COL6A3,COL3A1, COL16A1,C4A,F2R,STAB1,VMP1, GPM6A,TM4SF1,TRDN,SELE,THBD,LRRN3 GO:0012505 CC endomembrane system 0.003307269 2.480530528 17 SERPINE2,COL1A1,COL1A2,VCAN,SPON1, BGN,COL6A3,COL3A1,TUG1,COL16A1, C4A,F2R,VMP1,TRDN,NR3C2,SELE,MAMDC2 GO:0047962 MF glycine N-benzoyltransferase activity 0.017435608 1.758562895 1 GLYAT GO:0071837 MF HMG box domain binding 0.11769815 0.929230363 1 PRRX1 Table 4 The enriched pathway terms of the up and down regulated differentially expressed genes Pathway ID Pathway Name adjusted_p_ value negative_log10_of_adjusted_p_value Gene Count Gene REAC:R-HSA-1474244 Extracellular matrix organization 4.05332E-05 4.392188608 7 COL1A1,COL1A2,VCAN,BGN, COL6A3,COL3A1,COL16A1 REAC:R-HSA-140875 Common Pathway of Fibrin Clot Formation 0.000174191 3.758973492 3 SERPINE2,F2R,THBD Table 5 Topology table for up and down regulated genes Regulation Node Degree Betweenness Stress Closeness Up COL1A1 36 0.393488 32896 0.382857 Up COL1A2 26 0.347574 33052 0.376404 Up F2R 19 0.198743 27946 0.304545 Up VCAN 18 0.172345 16394 0.308282 Up SERPINE2 11 0.097264 7410 0.233179 Up COL3A1 9 0.03531 5778 0.271622 Up BGN 8 1 56 1 Up NT5DC2 8 0.068607 5208 0.279944 Up STAB1 7 0.058955 3774 0.225843 Up SFRP2 6 1 30 1 Up COL16A1 4 0.024545 1818 0.232102 Up COL6A3 3 0.416667 30 0.45 Down NR3C2 23 0.204209 16614 0.29646 Down SELE 23 0.193212 28686 0.269437 Down CXCL2 11 0.097264 5750 0.237028 Down MYOM1 6 0.833333 60 0.642857 Down TM4SF1 6 0.051972 5388 0.291304 Down THBD 6 0.032551 3792 0.26378 Down CSF3 5 0.039502 2420 0.221366 Down GLYAT 5 0.039502 5148 0.192898 Down TRDN 4 1 12 1 Down C8orf34 1 0 0 0.27459 Down KANK4 1 0 0 0.27459 Down STXBP6 1 0 0 0.27459 Table 6 MiRNA - hub gene and TF – hub gene topology table Regulation Hub Genes Degree MicroRNA Regulation Hub Genes Degree TF Up COL1A1 280 hsa-miR-6515-5p Up SERPINE2 14 FOXL1 Up VCAN 233 hsa-miR-518a-3p Up COL3A1 8 STAT1 Up COL1A2 197 hsa-miR-497-5p Up COL1A2 8 PPARG Up SERPINE2 178 hsa-miR-146a-5p Up VCAN 8 NFKB1 Up COL3A1 139 hsa-miR-24-3p Up COL1A1 5 SREBF1 Up F2R 129 hsa-miR-3913-3p Up F2R 5 EGR1 Down TM4SF1 208 hsa-miR-6838-5p Down CXCL2 11 HOXA5 Down NR3C2 129 hsa-miR-135a-5p Down SELE 11 JUN Down THBD 76 hsa-miR-196b-5p Down THBD 11 USF2 Down CXCL2 66 hsa-miR-200a-3p Down MYOM1 6 GATA2 Down MYOM1 65 hsa-miR-151a-3p Down TM4SF1 5 NFIC Down SELE 21 hsa-miR-4652-3p Down NR3C2 4 RELA Table 7 Drug- hub gene topology table Category Gene Degree Drug Up F2R 4 Vorapaxar Up COL1A1 2 Halofuginone Up COL1A2 1 Collagenase clostridium histolyticum Up COL3A1 1 Collagenase clostridium histolyticum Down NR3C2 11 Progesterone Down THBD 2 Ibuprofen Down SELE 2 Carvedilol Table 8: Binding affinity and amino acid interaction of selected phytoconstituents against up-regulated genes. Compound COL1A1 Gene (pTaBLEdb:3EJH) Amino acid interactions, Active Chain is A, Co-crystallised ligand is D-glucopyranose COL1A2 Gene (pdb:5CTI) Amino acid interactions, Active chain is C, Co-crystallised ligand is Glycerol Affinity (kcal/mol) Hydrogen bonding interactions Electrostatic interactions Hydrophobic interactions Affinity (kcal/mol) Hydrogen bonding interactions Electrostatic interactions Hydrophobic interactions Macrophylloside -8.3 GLU:577, ARG:584, LYS:578, HIS:581 TYR:579 VAL:580 -6.2 GLU:46, ARG:45, SER:53, GLY:70, GLU:54 -- ALA:57, SER:53 Dichotomitin -7.1 GLU:577, GLU:565, TYR:579 GLU:565 VAL:580, TYR:579 -5.5 GLN:58 GLU:54 ALN:57 Co-crystallized ligand -4.8 TYR:578, LYS: 579, TYR: 570 GLU: 577, THR: 566 -- -2.6 ALA: 68, GLY: 70, ALA: 57 -- -- Table 9 Binding affinity and amino acid interaction of selected phytoconstituents against up-regulated genes. Compound NR3C2 Gene (pdb:2AA2) Amino acid interactions, Active Chain is A, Co-crystallised ligand is Aldosterone SELE Gene (pdb:1ESL) Amino acid interactions, Active chain is A, Co-crystallised ligand is Calcium cation Affinity (kcal/mol) Hydrogen bonding interactions Electrostatic interactions Hydrophobic interactions Affinity (kcal/mol) Hydrogen bonding interactions Electrostatic interactions Hydrophobic interactions Macrophylloside -7.7 ASP:933, SER:936, HIS:932, VAL:971, GLY:974 SER:936 LEU:939, VAL:971, PRO:978 -7.2 ARG:54, GLN:85, ASP:89 LYS:55, ARG:54 VAL:56 Dichotomitin -7.3 HIS:932, THR:880, TYR:804, SER:936, GLU:967 -- VAL:935, TYR:804, ALA:976, LEU:939, VAL:971 -7.4 GLY:131, CYS:122, THR:65 GLU:135 CYS:133, ALA:120, CYS:122 Co-crystallized ligand -8.9 ASN: 770, SER:810 ARG:817, MET:845 -- -1.6 -- SER:6 -- Table 10 ADMET properties of phytoconstituents and cocrystallized ligands Parameter Dichotomitin Macrophylloside Aldosterone Glycerol Alpha D-glucopyranose Bioavailability Score 0.08 0.55 0.55 0.55 0.29 Topological Polar Surface Area (TPSA, Ų) 321.65 107.59 91.67 60.69 110.38 Log S (ESOL) -3.43 (Moderately soluble) -3.06 (Moderately soluble) -3.52 (Moderately soluble) -1.56 (Soluble) -1.99 (Soluble) Log Po/w (XLOGP3) 0.40 2.01 1.23 -1.59 -2.19 Human Intestinal Absorption (HIA) ~98% 91% 91% 92% 84% BBB Permeant No No Yes No No P-gp Substrate Yes No No No No Fraction Unbound (Human Plasma, Fu) ~0.26 0.068 0.042 0.0769 0.1267 CYP1A2 Inhibitor No No No No No CYP2C19 Inhibitor No No No No No CYP2C9 Inhibitor No Yes Yes No No CYP2D6 Inhibitor No No Yes No No CYP3A4 Inhibitor No Yes Yes No No Total Clearance (log mL/min/kg) ~0.0043 0.582 0.0006 0.0008 0.0006 Renal OCT2 Substrate No No No No No Oral Rat Acute Toxicity (LD50) 0.313 mol/kg 0.46 mol/kg 0.387 0.015 0.04 Hepatotoxicity Yes (Predicted but low) Yes (Predicted but low) Yes (Predicted but low) No (Safe) No (Safe) Additional Declarations No competing interests reported. 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College of Pharmacy, Gadag, Karnataka 582101, India.","correspondingAuthor":false,"prefix":"","firstName":"Basavaraj","middleName":"","lastName":"Vastrad","suffix":""},{"id":486622866,"identity":"21359a8c-6d2b-446a-996e-ff57beead865","order_by":1,"name":"Shivaling Pattanashetti","email":"","orcid":"","institution":"Department of Pharmaceutical Chemistry, K.L.E. College of Pharmacy, Gadag, Karnataka 582101, India.","correspondingAuthor":false,"prefix":"","firstName":"Shivaling","middleName":"","lastName":"Pattanashetti","suffix":""},{"id":486622867,"identity":"94c6d01c-3a92-4b6d-b14f-fa3f2d2b669a","order_by":2,"name":"Chanabasayya Vastrad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCRQemw2QYGw8QKQWZpCWNJCWBpK0HAYz8Wrhn9387HFhG0Nif//5Yx9+lJ23W9t+GGhLjU00TkvuHDM3ngnUMuNGMvPMnnO3k7edSQRqOZaW24BDi4FEgpk0bxuDMcMNZmYG3rbbyWYHgFoYGw7j0ZL+DaxF/vxhZsa/beeSzc4/JKQlB2yLnMGBZGZm3rYDdmY3CNgicSOnTJrnnISc4Y1kY2aZc8kJZjeAtiTg8Qv/jPRt0jxlNjxy5w8+ZnxTZmdvdj794YMPNTY4tcAsg7MSwSoT8CtHBfakKB4Fo2AUjIKRAQDSZlsK1y3XFwAAAABJRU5ErkJggg==","orcid":"","institution":"Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India.","correspondingAuthor":true,"prefix":"","firstName":"Chanabasayya","middleName":"","lastName":"Vastrad","suffix":""}],"badges":[],"createdAt":"2025-07-16 09:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7138516/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7138516/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87163422,"identity":"d122ab81-b974-452f-b409-01fb6ad117f4","added_by":"auto","created_at":"2025-07-21 05:38:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97330,"visible":true,"origin":"","legend":"\u003cp\u003eStructures of selected drug targets\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/43037e1851cca0b8f61f508a.png"},{"id":87162836,"identity":"9b214b30-ddca-487a-9315-86f55a1cf62b","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96367,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/a05110f88c3c86dffe75f0a9.png"},{"id":87162839,"identity":"379252b2-3e9a-44cc-af4d-d671ead20d40","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153920,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A4 = Normal control samples; B1 – B10= Ovarian cancer samples)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/8b312c1b09a2352ee215a643.png"},{"id":87162842,"identity":"c36c090b-c827-4df5-8cc3-0ababe1a8727","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":280492,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of DEGs. Up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/302f13bd8472857b3d8f6be8.png"},{"id":87163424,"identity":"f75c856d-d645-4b65-9228-790661deba17","added_by":"auto","created_at":"2025-07-21 05:38:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":565444,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - miRNA regulatory network. The orange color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/c45bd01972042bac309bb859.png"},{"id":87163425,"identity":"1e93e19b-2d23-4f15-9b37-fee4df915df5","added_by":"auto","created_at":"2025-07-21 05:38:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":255080,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - TF regulatory network. The pink color triangle nodes represent the key TFs; up regulated genes are marked in dark green; down regulated genes are marked in dark red\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/37c71c06989dc6696839b281.png"},{"id":87162838,"identity":"9c8e0efc-135d-46c0-b35e-37e29914e1dc","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":126570,"visible":true,"origin":"","legend":"\u003cp\u003eDrug – hub gene interaction network. The blue color rectangle nodes represent the drug molecules; up regulated genes are marked in dark green and down regulated genes are marked in red .\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/6370b26e666147d14a079e30.png"},{"id":87163426,"identity":"ca20ec80-744c-4906-8c6e-e220422e69df","added_by":"auto","created_at":"2025-07-21 05:38:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":95079,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analyses of hub genes. A) COL1A1 B) COL1A2 C) NR3C2 D) SELE\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/55331bbc40239d79f85ea967.png"},{"id":87162851,"identity":"1408d2c0-a837-4104-a542-d45972fdee94","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":122626,"visible":true,"origin":"","legend":"\u003cp\u003e2D images of A. Dichotomitin B. Marcophylloside C. Alpha D-glucopyranose\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/a85a051750f0ea8fb700fa1e.png"},{"id":87162848,"identity":"85184646-a33d-4239-b884-82b33ee654a4","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":160614,"visible":true,"origin":"","legend":"\u003cp\u003e3D images of amino acid interaction of A. Dichotomitin, B. Macrophylloside A, C. Alpha D-Glucopyranose.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/937e753ef8f7e702bca54a8e.png"},{"id":87162847,"identity":"c41e9de1-8249-403e-b26b-85c384cb27eb","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":112235,"visible":true,"origin":"","legend":"\u003cp\u003e2D images of amino acid interaction of A. Dichotomitin, B. Macrophylloside, C. Glycerol.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/1016832376ea3bac65bdaf10.png"},{"id":87163946,"identity":"2893b1c0-f7e5-407b-a1fa-e57a1a24b45d","added_by":"auto","created_at":"2025-07-21 05:46:44","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":125329,"visible":true,"origin":"","legend":"\u003cp\u003e3D images of amino acid interaction A. Dichotomitin, B. Macrophylloside, C. Glycerol\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/ab262af2192876d967bc4e87.png"},{"id":87162845,"identity":"e83f8f44-9b26-44b8-a93d-a4a815ef0d62","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":127921,"visible":true,"origin":"","legend":"\u003cp\u003e2D images of amino acid interaction A. Dichotomitin, B. Macrophylloside, C. Aldosterone\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/ed703e75f63251d0dfb84d95.png"},{"id":87163429,"identity":"ab04d319-31c5-43d2-8b6b-cd46c879f236","added_by":"auto","created_at":"2025-07-21 05:38:44","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":150415,"visible":true,"origin":"","legend":"\u003cp\u003e3D images of amino acid interaction A. Dichotomitin, B. Macrophylloside, C. Aldosterone\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/b42eb1313ac21cd9ba370497.png"},{"id":87162843,"identity":"bff11cf2-67b1-4494-8790-2f0e169d0fe3","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":126180,"visible":true,"origin":"","legend":"\u003cp\u003e2D images of amino acid interaction A. Dichotomitin, B. Macrophylloside, C. Calcium ion\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/8297732cfaf07c9a3ca48a6c.png"},{"id":87162850,"identity":"670ecacc-0523-4324-9a82-dcde4108d041","added_by":"auto","created_at":"2025-07-21 05:30:44","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":124533,"visible":true,"origin":"","legend":"\u003cp\u003e3D images of amino acid interaction A. Dichotomitin, B. Macrophylloside, C. Calcium ion\u003c/p\u003e","description":"","filename":"image16.png","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/f413a94cee48d47dc7f8352b.png"},{"id":87164534,"identity":"f46e1cab-6e12-4e9f-96fd-6b2403beba6c","added_by":"auto","created_at":"2025-07-21 05:54:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4720010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7138516/v1/7171c629-4606-4395-ae7d-41e5b110207b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated bioinformatics analysis reveals key candidate genes and signaling pathways, and Macrphylloside D as novel therapeutic agent in ovarian cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBetween 1990 and 2021, the worldwide age-normalized rate of occurrence of ovarian cancer surged from 160,000 to nearly 300,000, but rates per 100,000 slightly declined [Li et al. 2025]. The frequency of ovarian cancer is increasing, and this disease is forecasted to become the seventh most common cancer in women and the eighth most common cause of cancer death worldwide [Webb and Jordan, 2017]. There are no known precaution measures and no competent screening tool [Choi and Choi, 2024]. Although confirmation suggests that the majority of women exposure a range of non-specific symptoms in the year before diagnosis, the disease it is not frequently identified until an leading stage, noted to increased mortality and morbidity [Mahoney and Pierce, 2022]. However, the survival rate is affected by several factors, and the prognosis of ovarian cancer remains extremely poor despite the adoption of multiple treatment strategies such as surgery [Nick et al. 2015], chemotherapy [Lloyd et al. 2015], targeted therapy [Coward et al. 2015], hormonal therapy [Simpkins et al. 2013], immunotherapy [Siminiak et al. 2022] and maintenance therapy [Walsh, 2020]. Thus, there is an urgent need to explore the molecular mechanisms of ovarian cancer and to identify effective biomarkers.\u003c/p\u003e\n\u003cp\u003eThere are several important risk factors for ovarian cancer, such as age [Maas et al. 2005], genetics [Hollis and Gourley, 2016],\u0026nbsp; endometriosis [Brilhante et al. 2017], polycystic ovary syndrome [Zou et al. 2022], obesity [Liu et al. 2015], smoking [Wang et al. 2020], \u0026nbsp;use of talcum powder [Saed et al. 2024], inflammation [Savant et al. 2018] and oxidative stress [Ding et al. 2021]. Therefore, the early prognosis and diagnosis of ovarian cancer remains a essential and concern for doctors and scientists, and examination of novel biomarkers and therapeutic targets ovarian cancer is imperative for doctors and patients alike. \u0026nbsp;\u0026nbsp;Although many biomarkers include PIK3CA [Kolasa et al. 2009], ARID1A [Kuroda et al. 2021], KRAS [Kim et al. 2020], PTEN [Martins et al. 2020] and NF1 [Su et al. 2019] have been studied as prognostic and diagnostic markers as well as therapeutic targets. Ovarian cancer has been genetically associated with signaling pathways such as PI3K/AKT/mTOR signaling pathway [Gasparri et al. 2017], RAS/RAF/MEK/ERK (MAPK) signaling pathway [Hendrikse et al. 2023], Wnt/\u0026beta;-catenin signaling pathway [Boone et al. 2016], notch signaling pathway [Akbarzadeh et al. 2020] and NF-\u0026kappa;B \u0026nbsp;signaling pathway [Leizer et al. 2011]. In particular, the novel molecular characteristics can be implemented in early risk assessment, the identification of better specific biomarkers for prognosis and diagnosis of ovarian cancer, and the improvement of clinic treatment and survival.\u003c/p\u003e\n\u003cp\u003eDivergent from traditional research methods, the efficient application of microarray technology and the establishment of a global gene database provide broader and necessary data support for the diagnosis of ovarian cancer [Alur et al. 2019]. Meanwhile, the advancement of bioinformatics technology provides a reliable way to discover key regulatory genes and signaling pathways of cancer [Joshi et al. 2019; Alshabi et al. 2019; Vastrad et al. 2018]. On this basis, an increasing number of ovarian cancer related genes and signaling pathways have been discovered, and some of them have been proven to play an essential role in the onset and progression of cancer in subsequent validation.\u003c/p\u003e\n\u003cp\u003eOur main purpose is to explore the molecular mechanism of ovarian cancer. First, we download the GSE120196 [Au-Yeung et al. 2020] dataset file in the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) [Clough and Barrett, 2016] \u0026nbsp;for analysis, then use limma to draw the differentially expressed genes (DEGs) distribution map of the ovarian cancer and normal control samples in the dataset. Gene Ontology (GO) \u0026nbsp;and pathway enrichment analysis of DEGs was undertaken with g:Profiler. Immediately afterward, the protein-protein interaction (PPI) network of DEGs is drawn and the hub genes are identified. Subsequently, the miRNA-hub gene regulatory network, TF-hub gene regulatory network, and drug-hub gene interaction network of hub genes is drawn and the microRNAs (miRNAs), transcription factors (TFs) and drugs are identified. The diagnostic value of hub genes was verified through receiver operating characteristic (ROC) curves analysis. molecular docking studies and \u003cem\u003ein-silico\u003c/em\u003e ADMET were carried out. The final results will help us obtain novel targets and treatment for ovarian cancer.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003e\u003cstrong\u003eMicroarray data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGEO\u0026nbsp; is a public functional genomics data repository of microarray data.\u0026nbsp; The GSE120196 [Au-Yeung et al. 2020] dataset generated using the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array was downloaded from GEO. The GSE120196 dataset contained 10 ovarian cancer samples and 4 normal control samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLimma [Ritchie et al. 2014] is an R bioconductor package that allows users to determine DEGs for various experimental situations. The adjusted P-values (adj. P) and Benjamini and Hochberg false discovery rate was used to touch a balance between finding statistically important genes and limiting false positives [Green and Diggle, 2007]. The screening criteria of DEGs was set as adj.P.Val\u0026thinsp;\u0026le;\u0026thinsp;0.05, |log2 fold change (FC) | \u0026nbsp;\u0026gt; 0.95 for up regulated genes \u0026nbsp;and |log2 fold change (FC) | \u0026lt; -1.1 for down regulated genes . The screening results were then presented in the form of volcano plots and heat maps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO and pathway enrichment analyses of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eg:Profiler (http://biit.cs.ut.ee/gprofiler/) [Reimand et al. 2007] \u0026nbsp;is an analytical website which incorporates functional enrichment analysis, gene annotation and membership search in a comprehensive portal. Gene Ontology (GO) (http://www.geneontology.org) [Thomas, 2017] is a premier bioinformatics program for high-quality functional gene annotation based on biological processes (BP), cellular components (CC) and molecular functions (MF). The REACTOME (https://reactome.org/) [Fabregat et al. 2018] \u0026nbsp;is a resource of pathway database for the clarification of high-level features and effects of biological systems. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the PPI network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe International Molecular Exchange Consortium (IMex)\u0026nbsp;\u0026nbsp; (https://www.imexconsortium.org/) [Porras et cal. 2020] was used to create a PPI network of ovarian cancer DEGs to predict PPI and the functions of the DEGs. Subsequently, Cytoscape software (v3.10.3) (http://www.cytoscape.org/) [Shannon et al. 2003] was used to visualize and analyze biological networks. Then, Network Analyzer plug-in of Cytoscape were used to recognize the interaction degree of candidate gene clustering according to 4 types of algorithms \u0026nbsp;include node degree [Luo et al. 2017], betweenness [Li et al. 2017], stress [Gilbert et al. 2021] and closeness [Li et al. 2020].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the miRNA-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe miRNA-hub gene regulatory network was used to investigate the regulation mechanism of the hub genes. \u0026nbsp;The miRNA - hub gene interaction data were collected from miRNet database (https://www.mirnet.ca/) [Fan et al 2018]. \u0026nbsp;\u0026nbsp;We used 14 miRNA databases to predict the target miRNA: TarBase, miRTarBase, miRecords, miRanda, miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0. Cytoscape software [Shannon et al. 2003] was used to visualize the miRNA-hub gene regulatory network. The connectivity degrees were calculated through network statistical methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the TF-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TF-hub gene regulatory network was used to investigate the regulation mechanism of the hub genes. \u0026nbsp;The TF - hub gene interaction data were collected from NetworkAnalyst database (https://www.networkanalyst.ca/) [Zhou et al 2019]. We used one TF database to predict the target TF: JASPAR. Cytoscape software [Shannon et al. 2003] was used to visualize the TF-hub gene regulatory network. The connectivity degrees were calculated through network statistical methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the drug-hub gene interaction network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe drug-hub gene regulatory network was used to investigate the drug molecule interaction on hub genes. \u0026nbsp;The drug - hub gene interaction data were collected from NetworkAnalyst database (https://www.networkanalyst.ca/) [Zhou et al 2019]. We used one drug database to predict the target drug: DugBank. Cytoscape software [Shannon et al. 2003] was used to visualize the drug-hub gene interaction. The connectivity degrees were calculated through network statistical methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curve (ROC) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic performance of hub gene expression levels was subsequently evaluated using ROC curves. The pROC R bioconductor package [Robin et al 2011] was used to plot the ROC curves for the diagnostic model in both the training and validation sets and to analyze the ability of hub genes to distinguish ovarian cancer samples from normal controls samples. The area under the curve (AUC) value was determine to evaluate the model\u0026rsquo;s diagnostic performance, with an AUC value greater than 0.8 considered indicative of diagnostic value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInsilico\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e molecular docking studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSwiss-model, RCSB PDB, Prank web, ChemDraw, Avogadro tool, Autodock 1.7.1, and Autodock Vina tools, Biovia Discovery Studio client 2021, ADMET lab 3.0 web server.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceptor and ligand preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RCSB Protein Data Bank (PDB) [Goodsell DS et al 2020] provided the PDB IDs that were used to retrieve the 3D crystal structure of the corresponding proteins from the corresponding genes. The selected targets with their structural information are presented in Table 1. Using the Swiss-Model web server, the missing residues were remodelled and downloaded in pdb format [Waterhouse A et al 2018]. Following the identification of the binding sites utilizing the server Prank web [Jendele L et al 2019], the protein's pdb format was entered into Software Auto Dock Tools 1.7.1, and water molecules and atoms were eliminated. The receptor has also been checked for missing amino acid residues, Kollman charges have been fixed, and only polar hydrogens have been inserted. In order to cover the entire receptor, the grid was fixed using the Autogrid program. The grid dimension file [Morris GM et al 2008] was then saved. The phytoconstituents were selected from the plant \u003cem\u003eActinodaphne angustifolia,\u003c/em\u003e which was previously reported by [Forid MS et al 2021]. Ligand structures were created using ChemDraw and saved as SMILES. The phytoconstituents and co-crystallized ligands were given in Fig. 1.They were then loaded into Avogadro software to convert them from 2D to 3D structures in pdb format [Hanwell MD et al 2012]. The ligand's pdb format was then entered into Auto Dock Tools 1.7.1, and the ligand molecule's root was identified and selected. Lastly, the pdbqt format was used to save the ligand molecule.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerforming AutodockVina\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuto Dock Vina can be executed using the command line (cmd) or the Autodock tool. The configuration file was ready for Autodock Vina to execute; the grid dimension file that was previously saved includes the protein's n-points, active site, and x, y, and z coordinates. That information was added to the configuration file, which was made to contain the protein's active site details. It also comes with an output file in pdbqt format and a log file in .txt format. The command line was used to run Autodock Vina. Vina.exe -- config config.txt was the command used to launch Auto Dock Vina. Docked coordinates were output in the pdbqt format when the program was finished. Receptor-ligand interactions were then visualized using the file, and binding affinity was ascertained using the log.txt file [Trott O et al 2010].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVisualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Biovia Discovery Studio Client 2021 program has been used to visualize the docking results. The Discovery Studio Client 2011 program was used to import the output pdbqt file and the receptor pdbqt file format. Next, a PNG file is created from the 3D image of the docked ligand and the 2D image of the docked ligand that is attached to various amino acids [James JJ et al 2025].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn silico\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e ADMET properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eADMET properties were predicted with the help of ADMET lab free web server, as previously reported by\u003cstrong\u003e [\u003c/strong\u003eFu L et al 2024\u003cstrong\u003e]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DEGs in ovarian cancer samples from normal controls samples in the gene expression was analyzed by the R bioconductor package \u0026lsquo;limma (version 3.5.1)\u0026rsquo;. In total, 38 DEGs were identified, including 19 up regulated genes and 19 down regulated genes (Table 2). All DEGs were described by the volcano plot (Fig. 2). Volcano plot with cut-off criteria set to adj.P.Val\u0026thinsp;\u0026le;\u0026thinsp;0.05, |log2 fold change (FC) | \u0026nbsp;\u0026gt; 0.95 for up regulated genes \u0026nbsp;and |log2 fold change (FC) | \u0026lt; -1.1 for down regulated genes .The heatmap shows the expression of the DEGs (Fig.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO and pathway enrichment analyses of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO and REACTOME pathway enrichment analyses were performed to investigate the functions of DEGs. For GO BP, DEGs were mainly enriched in cell adhesion and response to bacterium (Table 3).\u0026nbsp; For CC, the obtained results indicated that proteins encoded by DEGs were mostly located in the\u0026nbsp; cell periphery and endomembrane system (Table 3). For GO MF, the obtained results indicated that DEGs were significantly associated with glycine N-benzoyltransferase activity and HMG box domain binding (Table 3). The results of REACTOME pathway enrichment analysis showed that the pathways associated with extracellular matrix organization and common pathway of fibrin clot formation (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the PPI network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 38 DEGs were imported into the IMex database online database to construct the PPI network. In the Cytoscape platform for up regulated and down regulated genes, we found 233 nodes and 256 edges (Fig.4). After topological analysis, the most connected up regulated genes COL1A1, COL1A2, F2R, VCAN and SERPINE2, and the most connected down regulated genes NR3C2, SELE, CXCL2, MYOM1 and TM4SF1 were categorized according to their highest node degree, betweenness, stress and closeness are associated with ovarian cancer (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the miRNA-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA synthesis. miRNA up and down regulation deficiency are associated with ovarian cancer and they have an ability to differentiate between benign and malignant carcinomas [Zhao et al 2022] and the disease complication can be more readable by miRNA changes. The regulatory network contained 805 miRNAs and 24 hub genes with 2513 interaction (edges) (Fig.5). Further results demonstrated that COL1A1 is associated 280 miRNAs (ex; hsa-miR-6515-5p), VCAN is associated 233 miRNAs (ex; hsa-miR-518a-3p), COL1A2 is associated 197 miRNAs (ex; hsa-miR-497-5p), SERPINE2 is associated 178 miRNAs (ex; hsa-miR-146a-5p), COL3A1 is associated 139 miRNAs (ex; hsa-miR-24-3p), TM4SF1 is associated 208 miRNAs (ex; hsa-miR-6838-5p), NR3C2 is associated 129 miRNAs (ex; hsa-miR-135a-5p), THBD is associated 76 miRNAs (ex; hsa-miR-196b-5p), CXCL2 is associated 66 miRNAs (ex; hsa-miR-200a-3p) and MYOM1 is associated 65 miRNAs (ex; hsa-miR-151a-3p) (Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the TF-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTFs play a critical role in cancer progression by regulating gene expression involved in cell growth, survival, angiogenesis, immune evasion, and metastasis [Li et al 2021]. TFs are frequently implicated in ovarian cancer. The regulatory network contained 56 TFs and 23 hub genes with 180 interaction (edges) (Fig.6). Further results demonstrated that SERPINE2 is associated 14 TFs (ex; FOXL1), COL3A1 is associated 8 TFs (ex; STAT1), COL1A2 is associated 8 TFs (ex; PPARG), VCAN is associated 8 TFs (ex; NFKB1), COL1A1 is associated 5 TFs (ex; SREBF1), CXCL2 is associated 11 TFs (ex; HOXA5), SELE is associated 11 TFs (ex; JUN), THBD is associated 11 TFs (ex; USF2), MYOM1 is associated 6 TFs (ex; GATA2) and TM4SF1 is associated 5 TFs (ex; NFIC) (Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the drug-hub gene interaction network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrugs can alter gene expression through various mechanisms, depending on the type of drug, target pathway, and cellular context [Koussounadis et al 2014]. Drugs are frequently implicated in ovarian cancer. The drug-hub gene interaction network\u0026nbsp; shown in Fig.7. Further results demonstrated that F2R is targeted with 4 drugs (ex; vorapaxar), COL1A1 is targeted with 2 drugs (ex; halofuginone), NR3C2 is targeted with 11 drugs (ex; progesterone) and THBD is targeted with 2 drugs (ex; Ibuprofen) (Table 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curve (ROC) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC analysis was performed to evaluate the specificity and sensitivity of the four hub genes. The results for the ovarian cancer biomarkers were favorable, with COL1A1 (AUC\u0026thinsp;=\u0026thinsp;0.931), COL1A2 (AUC\u0026thinsp;=\u0026thinsp;0.910), NR3C2 (AUC\u0026thinsp;=\u0026thinsp;0.934) \u0026nbsp;\u0026nbsp;and SELE (AUC\u0026thinsp;=\u0026thinsp;0.917) exhibiting robust predictive performance (Fig.8.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn silico\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e molecular docking studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the binding affinities and interaction patterns of two specific phytoconstituents, macrophylloside D and Dichotomitin, against four important ovarian cancer-related targets, COL1A1, COL1A2, NR3C2, and SELE, molecular docking studies were performed. Thesereceptorsall hadaknownco-crystallisedligandthatwasusedasareferencepoint.\u003c/p\u003e\n\u003cp\u003eInitially occupied by D-glucopyranose, the active binding pocket in chain A of the COL1A1 receptor (PDB ID: 3EJH) showed noticeably greater interactions with macrophylloside. With a binding affinity of -8.3 kcal/mol, macrophylloside connected electrostatically with TYR579, hydrophobically with VAL580, and formed hydrogen bonds with GLU577, ARG584, LYS578, and HIS581. Conversely, Dichotomitin exhibited a clear hydrogen bonding profile and fewer hydrophobic contacts while binding with a moderate affinity of -7.1 kcal/mol on average.In contrast, the native ligand D-glucopyranose showed far poorer binding at -4.8 kcal/mol, suggesting that it had little interaction with the active site. The COL1A2 receptor (PDB ID: 5CTI), which has glycerol as the co-crystallised ligand, similarly demonstrated increased binding to Macrophylloside D (-6.2 kcal/mol) in comparison to Dichotomitin (-5.5 kcal/mol) and glycerol (-2.6 kcal/mol). Along with hydrophobic interactions with ALA57 and SER53, macrophylloside also produced numerous hydrogen bonds with GLU46, ARG45, SER53, GLY70, and GLU54. A less stable complex formation with the target was suggested by the weaker and fewer interactions that dichotometin had with the target. Binding affinity is given in Table 8 and 2D, 3D images of amino acid interaction are given in Fig 9 to Fig\u0026nbsp; 12.\u003c/p\u003e\n\u003cp\u003eMacrophylloside once more demonstrated high binding with a binding energy of -7.7 kcal/mol for the NR3C2 receptor (PDB ID: 2AA2), a known target downregulated in MDD that complexed with aldosterone. It interacted with ASP933, SER936, HIS932, VAL971, and GLY974 via hydrogen bonds. Additionally, it developed strong hydrophobic ties with LEU939, VAL971, and PRO978 as well as electrostatic ties with SER936. With a -7.3 kcal/mol affinity, Dichotomitincame next, interacting with some residues, including TYR804 and HIS932. Even though aldosterone had the highest binding score (- 8.9 kcal/mol), it also had a more constrained interaction profile, which could compromise stability in vivo.Another downregulated target, the SELE receptor (PDB ID: 1ESL), had calcium ions as a co-crystallised ligand. In terms of docking affinity, both phytoconstituents performed noticeably better than calcium. In addition to interacting electrostatically and hydrophobically, Dichotomitin formed hydrogen bonds with GLY131, CYS122, and THR65 while bound with -7.4 kcal/mol. Following closely behind at -7.2 kcal/mol, macrophylloside D formed hydrophobic contacts with VAL56 as well as hydrogen bonds with ARG54, GLN85, and ASP89. Its limited involvement in complex formation was further confirmed by the calcium ion ligand's very weak binding energy of -1.6 kcal/mol and negligible interaction. Binding affinity is given in Table 9 and 2D, 3D images of amino acid interaction are given in Fig.13 to Fig.16.\u003c/p\u003e\n\u003cp\u003eOverall, Macrophylloside demonstrated better binding compatibility and stability than Dichotomitinand the co-crystallised ligands, as evidenced by greater binding affinities and more advantageous interaction networks across all four receptors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADMET and Drug-Likeness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe drug-likeness potential of Macrophylloside and Dicomentin with the native ligands was further confirmed by the in silico ADMET study. With a bioavailability score of 0.55, macrophylloside outperformed Dichotomitin (0.08) and was on par with the common reference ligands, such as glycerol and aldosterone.Both substances demonstrated outstanding human intestinal absorption (HIA \u0026gt;90%) in terms of absorption characteristics; however, only aldosterone showed expected blood\u0026ndash;brain barrier (BBB) permeability. Interestingly, unlike Dichotomitin, macrophylloside does not function as a substrate for P-glycoprotein, suggesting a lesser risk of drug efflux and improved cellular retention.CYP1A2 and CYP2C19 are not inhibited by either phytoconstituent in terms of metabolic interactions. Both were anticipated to inhibit CYP2C9 and CYP3A4, though, which may present a moderate risk of drug-drug interactions when polypharmacy is in effect. Aldosterone's metabolic compatibility was further restricted by its inhibition of CYP2D6.Macrophylloside (Fu = 0.068) and Dichotomitin (Fu = 0.26), which favour systemic availability, showed moderate to low plasma protein binding, according to the distribution profile, which is indicated by fraction unbound (Fu). The ideal total clearance demonstrated favourable excretion potential for macrophylloside (log CL = 0.582 mL/min/kg), which was significantly greater than the insignificant clearance anticipated for Dichotomitin and aldosterone. Results were given in Table 10.\u003c/p\u003e\n\u003cp\u003eBoth Macrophylloside and Dicomentin have moderate oral rat acute toxicity, according to the toxicity profile (LD₅₀ values of 0.46 and 0.313 mol/kg, respectively). Crucially, hepatotoxicity was anticipated for both phytoconstituents, albeit it was minimal. whereas it was anticipated that co-crystallised ligands such as D-glucopyranose and glycerol would not be hepatotoxic.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite decades of investigation, the genetic alteration that make ovarian cancer pathogenic and relapses remain largely unknown. Aberrant levels of cell cycle pathway genes [Cunningham et al 2009], cell adhesion genes [Rafii et al 2012], and DNA methylation [Papakonstantinou et al 2021] in ovarian cancer patients have been reported in recent investigation with encouraging prospects. However, existing investigation unsuccessful to contribute enough information to explain the gene regulatory mechanism of oncoprotein expression. Therefore, an in-depth investigation of the DEGs in ovarian cancer patients is accessible to illuminate the molecular mechanism of the cancer and is expected to provide ket targets for diagnosis and treatment. Here, by comprehensive bioinformatics analyses of public microarray dataset, GSE120196, we screened the 38 DEGs (19 up regulated genes and 19 down regulated genes) between ovarian cancer samples and normal control samples and verified the diagnostic ability of genes as biomarkers for disease. Recent research suggested that SERPINE2 [Botteri et al 2024], COL1A1 [Shi et al 2025], COL1A2 [Xu et al 2024], VCAN (versican) [Wight et al 2020] and MYOM1 [Chen et al 2022] are linked with inflammation. COL1A1 [Xiao et al 2025] is linked with the proliferation and invasion of ovarian cancer cells. The functional role of VCAN (versican) [Zhou et al 2025] is regulation of ovarian cancer cell invasion and motility\u0026nbsp; potential. Studies have already confirmed that high TM4SF1 expression in ovarian cancer can control ovarian cancer cell invasion and metastasis [Huang et al 2023].\u0026nbsp; COL1A1 [Druso et al 2024] \u0026nbsp;and VCAN (versican)\u0026nbsp; [Wu et al 2005]\u0026nbsp; might be involved in the oxidative stress. These studies suggest that significant DEGs might be involved in the development of ovarian cancer.\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) \u0026nbsp;and pathway enrichment analysis can help us better understand the specific molecular pathogenesis of ovarian cancer.\u0026nbsp; Extracellular matrix organization [Puttock et al 2023] and cell adhesion [Elmasri et al 2009] were responsible for progression of ovarian cancer. BGN (biglycan) plays a vital role in modulating cellular adhesion, migration, cell proliferation and motility in ovarian cancer [Fang et al 2025]. COL6A3 \u0026nbsp;[Ho et al 2024] has been reported to be associated tumor invasion and metastasis in ovarian cancer. COL3A1 have been associated to increased proliferation, invasion, migration and drug resistance in ovarian cancer [Yang et al 2025]. Some studies show that SPON1 plays an important role in\u0026nbsp; chemoresistance in ovarian cancer [Nagasawa et al 2022]. VMP1 [Liu et al 2014] has been linked to ovarian cancer cell invasion and metastasis. SELE (selectin E) [Yang et al 2023] facilitates the adhesion of circulating ovarian cancer cell, leading to the preferential homing\u0026nbsp; and retention of metastatic ovarian cancer cell. THBD (thrombomodulin) expression might control cell growth and migration in ovarian cancer cells [Chen et al 2013]. BGN (biglycan) [Guo et al 2019], COL6A3 [Gesta et al 2016], SELE (selectin E) [Yang et al 2023] and THBD (thrombomodulin) [Yang et al 2016] genes have been demonstrated to be responsible for inflammation. Previous studies have shown that the altered level of BGN (biglycan) [Szabados et al 2024], COL6A3 [Li et al 2020] and SELE (selectin E)\u0026nbsp; [Zhang et al 2023] might be closely associated with oxidative stress. The identified enriched genes might provide new therapeutic targets for the cancer therapy of patients with ovarian cancer.\u003c/p\u003e\n\u003cp\u003eWe performed PPI network construction and analysis to investigate the hub genes related to ovarian cancer. COL1A1 [Xiao et al 2025], VCAN (versican) [Zhou et al 2025], \u0026nbsp;SELE (selectin E) [Yang et al 2023], CXCL2 [Zhang et al 2021] and TM4SF1 [Huang et al 2023] promotes the development and progression of ovarian cancer. COL1A1 [Shi et al 2025], COL1A2 [Xu et al 2024], VCAN (versican) [Wight et al 2020], SERPINE2 [Botteri et al 2024], NR3C2 [Huang et al 2022], \u0026nbsp;CXCL2\u0026nbsp; [Liu et al 2021] \u0026nbsp;and MYOM1 [Chen et al 2022] pays a major role in the occurrence and development of inflammation. COL1A1 [Druso et al 2024], VCAN (versican)\u0026nbsp; [Wu et al 2005],\u0026nbsp;\u0026nbsp; SELE (selectin E)\u0026nbsp; [Zhang et al 2023] \u0026nbsp;and CXCL2\u0026nbsp; [Pan et al 2024] are promising as a novel targets for oxidative stress. The findings from the present study indicated that the F2R gene might be a new biomarker for ovarian cancer. These findings support the potential role of hub genes as a therapeutic targets for the treatment of ovarian cancer.\u003c/p\u003e\n\u003cp\u003eTo further investigate the factors that might affect the expression of our hub genes, we identified miRNAs and TFs that might interacts. However, the top-ranking predicted miRNAs and TFs\u0026nbsp; were hsa-miR-146a-5p [Takamizawa et al 2023], \u0026nbsp;\u0026nbsp;STAT1 [Yu et al 2024], PPARG [Luo et al 2015], NFKB1 [Bai et al 2022], SREBF1 [Wang et al 2021], HOXA5 [Zhao et al 2018], JUN [Eckhoff et al 2013] and GATA2 [Erceylan et al 2021] are associated with ovarian cancer. Hsa-miR-6515-5p [Son et al 2022], hsa-miR-518a-3p [Yin et al 2022], hsa-miR-135a-5p [Li et al 2020], hsa-miR-196b-5p [Zhang et al 2024], STAT1 [Ploeger et al 2022], PPARG \u0026nbsp;[Geng et al 2024], NFKB1 [Cartwright et al 2016], HOXA5 [Zhu and Ma, 2021], JUN [Schonthaler et al 2011], GATA2 [Baba et al 2024] and NFIC [Zhang et al 2021] expression might be a shared target for inflammation. hsa-miR-518a-3p [Yin et al 2022],\u0026nbsp; STAT1 [Totten et al 2021], PPARG \u0026nbsp;[Wang et al 2022], NFKB1 [Guo et al 2021], SREBF1 [Okuno et al 2018], HOXA5 [Saijo et al 2016], \u0026nbsp;JUN \u0026nbsp;[Liu et al 2017] and GATA2 [Huang et al 2023] have been reported its expression in the oxidative stress. However, the roles of hsa-miR-497-5p, hsa-miR-24-3p, hsa-miR-6838-5p, hsa-miR-200a-3p, hsa-miR-151a-3p and USF2 \u0026nbsp;in ovarian cancer have not been reported until now. These studies are consistent with the results of our data mining in which miRNA and TFs for ovarian cancer.\u003c/p\u003e\n\u003cp\u003eFurthermore, we got the drug-hub gene interaction results from the DrugBank database. A total of 23 drugs or small molecules for ovarian cancer treatment were presented. Four targetable drugs (vorapaxar, halofuginone, progesterone and ibuprofen) were might be used for ovarian cancer treatment. Thus, these four candidate genes (F2R, COL1A1, NR3C2 and THBD) might be potential targets for ovarian cancer treatment, which is needed to be evaluated in further investigation.\u003c/p\u003e\n\u003cp\u003eImportant information about the binding preferences and interaction profiles of the two phytoconstituents, macrophylloside and Dichotomitin, against important targets linked to OC was uncovered by the molecular docking research. Of particular interest were interactions at the amino acid level within the binding sites. COL1A, the ligand with the highest binding affinity for COL1A1, was macrophylloside (\u0026minus;8.3 kcal/mol). Strong polar stabilisation within the active site is facilitated by the important hydrogen bonding interactions with GLU577, ARG584, LYS578, and HIS581 that were identified in the binding pocket. A well-fitting and complex binding orientation was also suggested by the substantial hydrophobic contact with VAL580 and the electrostatic interaction with TYR579,which was also seen. A wide variety of side chain chemistries were involved in these interactions, which enabled the ligand to be stabilised inside the pocket by advantageous electrostatic, hydrogen bonding, and van der Waals forces. In contrast, Dicomentin interacted with fewer important residues despite having a comparatively high docking score (-7.1 kcal/mol). Although it also engaged GLU565 via electrostatic interaction, its hydrogen bonding was restricted to GLU577, GLU565, and TYR579; its slightly lower binding energy may be explained by the lack of multi-type interactions with core residues such as ARG584 or HIS581. With a binding score of -4.8 kcal/mol, the co-crystallised ligand D-glucopyranose demonstrated only modest interactions, mostly with TYR578, LYS579, and TYR570, indicating its lower affinity and probably structural function rather than pharmacological significance.\u003c/p\u003e\n\u003cp\u003eCOL1A2, Macrophylloside once more showed good binding (\u0026minus;6.2 kcal/mol) and established a strong network of hydrogen bonds with GLU46, ARG45, SER53, GLY70, and GLU54. For the ligand to be anchored deeply within the binding groove, these interactions are probably essential. Through nonpolar interactions, the chemical also demonstrated hydrophobic connections with SER53 and ALA57, improving binding stability.Dicomentin only formed one hydrogen bond with GLN58 and had a lower binding energy (\u0026minus;5.5 kcal/mol). It also had little electrostatic interaction with GLU54. One possible explanation for its decreased affinity is the lack of more extensive polar or nonpolar interactions. Glycerol, the native ligand, only formed weak hydrogen bonds with ALA68, GLY70, and showed negligible binding (\u0026minus;2.6 kcal/mol).\u003c/p\u003e\n\u003cp\u003eMacrophylloside had a strong and competitive binding energy (- 7.7 kcal/mol) in the instance of NR3C2, even though the co-crystallised ligand Aldosterone had the highest docking score (- 8.9 kcal/mol). With ASP933, SER936, HIS932, VAL971, and GLY974, it established a vast network of hydrogen bonds that extend across the binding pocket's entrance and core. Its binding position was further cemented by hydrophobic interactions with LEU939, VAL971, and PRO978 as well as electrostatic stabilisation via SER936, which may have improved residence time and inhibitory potential.Dichotomitin,\u0026nbsp; came next (\u0026minus;7.3 kcal/mol), and hydrogen bonds were made with HIS932, THR880, TYR804, SER936, and GLU967. It did not, however, have electrostatic interactions, which could weaken the binding force overall. Although they weren't as strong as those of macrophylloside, hydrophobic interactions with VAL935, TYR804, ALA976, and LEU939 probably aid in pocket fitting.\u003c/p\u003e\n\u003cp\u003eUpon docking with the phytoconstituents, the SELE receptor, co-crystallised with a calcium ion (binding score: \u0026minus;1.6 kcal/mol), showed a significant increase in affinity. A binding energy of -7.4 kcal/mol was obtained by Dichotomitin, which additionally engaged GLU135 in electrostatic contact and generated many hydrogen bonds with GLY131, CYS122, and THR65. Stable binding was additionally reinforced by hydrophobic interactions with CYS133, ALA120, and CYS122.In addition to electrostatically interacting with LYS55 and ARG54, macrophylloside demonstrated a similar docking score (-7.2 kcal/mol) and established robust hydrogen bonds with ARG54, GLN85, and ASP89. Its stabilising interactions were augmented by a crucial hydrophobic interaction with VAL56. These residues may interfere with protein-protein recognition interactions that are crucial for inflammation-related signalling in OC, as they are found near the interface of the E-selectin binding domain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADMET and Drug-Likeness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing well-established prediction models, an \u003cem\u003ein silico\u003c/em\u003e ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis was conducted to assess the potential for medication development of the chosen phytoconstituents. Macrophylloside, Dichotomitin, and co-crystallised ligands, including D-glucopyranose, Glycerol, and Aldosterone, were all included in the comparative study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioavailability and absorption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith a bioavailability score of 0.55, macrophylloside showed a significant potential for systemic availability following oral treatment. Dichotomitin, on the other hand, had a very low score (0.08), indicating either first-pass metabolism or limited absorption. Furthermore, both phytoconstituents demonstrated good permeability through the intestinal epithelium, as evidenced by their high projected Human Intestinal Absorption (HIA \u0026gt; 90%). The fact that macrophylloside was not anticipated to be a P-glycoprotein (P-gp) substrate points to improved intracellular retention and a decreased probability of active efflux. However, Dichotomitin,\u0026nbsp; was identified as a P-gp substrate, which may jeopardise its therapeutic efficacy and intracellular concentration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMacrophylloside and Dichotomitin, both demonstrated relatively high protein binding, as indicated by their respective per cent unbound (Fu) values of 0.068 and 0.26 for plasma protein binding. A longer circulation time but less free medication accessible for action could be indicated by a lower Fu. Crucially, neither phytoconstituent was expected to penetrate the blood\u0026ndash;brain barrier (BBB), which may restrict CNS action while lowering the possibility of neurotoxicity, a desirable property for adjuncts of antidepressants that do not target the central nervous system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeither chemical inhibited CYP1A2 or CYP2C19, which is favourable for reducing drug-drug interactions in terms of metabolic liability. Nevertheless, it was anticipated that both would inhibit CYP2C9 and CYP3A4, which could raise issues about metabolic competition with other isoenzyme substrates. The co-crystallised ligand for NR3C2, aldosterone, was more metabolically disruptive than the phytoconstituents since it also inhibited CYP2D6.\u003cbr /\u003e \u003cbr /\u003e \u003cstrong\u003eExcretion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn comparison to Dichotomitin (~0.0043), macrophylloside exhibited a comparatively high total clearance (log CL = 0.582 mL/min/kg), indicating superior excretion via hepatic or renal pathways. Its ability to prevent toxicity and long-term buildup is supported by this increased clearance rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eToxicity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth phytoconstituents showed moderate oral acute toxicity from a toxicological point of view, with LD₅₀ values of 0.313 mol/kg for Dichotomitin and 0.46 mol/kg for macrophylloside. Macrophylloside appears to be less acutely hazardous, even if these levels are within permissible bounds for natural substances. Although hepatotoxicity was expected for both, it was characterised as having a low probability, indicating adequate margins for hepatic safety. Glycerol and D-glucopyranose, on the other hand, lacked drug-like action and binding affinity despite being projected to be non-hepatotoxic.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present investigation identified key genes and signaling pathways which might be involved in ovarian cancer advancement through the integrated analysis of NGS dataset. These results might contribute to a better understanding of the molecular mechanisms which underlie ovarian cancer and provide a series of potential and novel biomarkers. Additionally, the majority of included investigation focused on how a single essential gene and signaling pathway contribute to the advancement of ovarian cancer, with limited investigation concerning the interaction of genes, miRNA, TFs and drug molecules. Further studies are needed to confirm our putative finding.\u003c/p\u003e\n\u003cp\u003eAmong all OC -associated targets, macrophylloside regularly showed the highest binding affinity. It created broad hydrogen bonds with vital active site residues and multi-type interactions (hydrophobic, electrostatic) that are necessary for precise and long-lasting binding.Although Dichotomitin was fairly active, it did not have the same density and diversity of interactions as macrophylloside. Lower or less specific binding was demonstrated by co-crystallised ligands such as D-glucopyranose, glycerol, calcium, and aldosterone, demonstrating the superiority of the chosen phytoconstituents in targeting these receptors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADMET and Drug-Likeness \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMacrophylloside is a good option for oral delivery because of its good intestinal absorption, mild protein binding, and acceptable bioavailability. It's little hepatotoxicity, non-BBB permeability, and lack of P-gp substrate status all contribute to systemic safety. The risk of buildup is decreased by its increased clearance rate, and despite possible CYP inhibition, its overall metabolic profile is controllable. The pharmacokinetic and toxicological profile of Macrophylloside is the best balanced when compared to Dichotomitin and all other co-crystallised ligands.\u003c/p\u003e\n\u003cp\u003eThe emergence of macrophylloside as a potential multi-target phytoconstituent for further study in ovarian cancer\u0026nbsp;medication discovery is facilitated by its strong receptor binding profiles and beneficial ADMET characteristics. According to its findings, it may be used as a lead chemical to create safer, more, oral bio-available and natural anticancer agents for ovarian cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI thanks very much to Au Yeung CL, Yeung TL, Mok SC, UT MD Anderson Cancer Center, Houston, \u0026nbsp;\u0026nbsp;\u0026nbsp; USA, the authors who deposited their microarray dataset GSE120196, into the public GEO database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo informed consent because this study does not contain human or animals participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE120196) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120196]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. V. - Writing original draft, and review and editing\u003c/p\u003e\n\u003cp\u003eS.P.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; - Formal analysis and validation\u003c/p\u003e\n\u003cp\u003eC. V. - Software and investigation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasavaraj Vastrad\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ORCID ID: 0000-0003-2202-7637\u003c/p\u003e\n\u003cp\u003eShivaling Pattanashetti\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ORCID ID: 0009-0003-9246-1604\u003c/p\u003e\n\u003cp\u003eChanabasayya\u0026nbsp; Vastrad\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ORCID ID: 0000-0003-3615-4450\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkbarzadeh M, Akbarzadeh S, Majidinia M. 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Identification of Versican as a target gene of the transcription Factor ZNF587B in ovarian cancer. Biochem Pharmacol. 2025;237:116946. doi:10.1016/j.bcp.2025.116946\u003c/li\u003e\n\u003cli\u003eZhu Z, Ma L. Sevoflurane induces inflammation in primary hippocampal neurons by regulating Hoxa5/Gm5106/miR-27b-3p positive feedback loop. Bioengineered. 2021;12(2):12215-12226. doi:10.1080/21655979.2021.2005927\u003c/li\u003e\n\u003cli\u003eZou J, Li Y, Liao N, Liu J, Zhang Q, Luo M, Xiao J, Chen Y, Wang M, Chen K, et al. Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis. J Ovarian Res. 2022;15(1):30. doi:10.1186/s13048-022-00962-w\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eSelected drug targets with their structural information.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.184%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug target gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4208%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTopology of genes in MDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6729%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDB ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResolution (\u0026Aring;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2947%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethod of collection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1175%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObserved\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eR-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.184%;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4208%;\"\u003e\n \u003cp\u003eUp regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6729%;\"\u003e\n \u003cp\u003e3EJH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3101%;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2947%;\"\u003e\n \u003cp\u003eX-ray diffraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1175%;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.184%;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4208%;\"\u003e\n \u003cp\u003eUp regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6729%;\"\u003e\n \u003cp\u003e5CTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3101%;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2947%;\"\u003e\n \u003cp\u003eX-ray diffraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1175%;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.184%;\"\u003e\n \u003cp\u003eNR3C2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4208%;\"\u003e\n \u003cp\u003eDown regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6729%;\"\u003e\n \u003cp\u003e2AA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3101%;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2947%;\"\u003e\n \u003cp\u003eX-ray diffraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1175%;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.184%;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4208%;\"\u003e\n \u003cp\u003eDown regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6729%;\"\u003e\n \u003cp\u003e1ESL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.3101%;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.2947%;\"\u003e\n \u003cp\u003eX-ray diffraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1175%;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e The statistical metrics for key differentially expressed genes (DEGs)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"762\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogFC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e\u003cstrong\u003epValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e\u003cstrong\u003etvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e2.705479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e6.66E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e8.005357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eserpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e3.984305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.25E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e7.612776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecollagen, type I, alpha 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eSFRP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e2.481883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e3.95E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.929767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003esecreted frizzled-related protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e2.592584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e8.62E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.485731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecollagen, type I, alpha 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e3.043548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.01E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.39482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eversican\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eSPON1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.943975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.06E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.368547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003espondin 1, extracellular matrix protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eBGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e3.053935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.11E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.344523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ebiglycan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eCOL6A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.414595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.15E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.326491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecollagen, type VI, alpha 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eRUNX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.75519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.16E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.321531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003erunt-related transcription factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.992717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.22E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.293766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecollagen, type III, alpha 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eTUG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e0.985688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.94E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.039396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003etaurine up-regulated 1 (non-protein coding)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eNT5DC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e0.970153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.97E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e6.03119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003e5\u0026apos;-nucleotidase domain containing 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003ePRRX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.02811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e3.47E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e5.728486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003epaired related homeobox 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eCOL16A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e2.056656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e5.63E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e5.473747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecollagen, type XVI, alpha 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eC4B_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.488349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e6.95E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e5.36537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecomplement component 4B (Chido blood group), copy 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eSEPT8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.104221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e7.31E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e5.339117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eseptin 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e2.069188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e7.35E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e5.336208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecoagulation factor II (thrombin) receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eSTAB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e1.215354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e7.97E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e5.294708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003estabilin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eVMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e2.212453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e8.46E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e5.263772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003evacuole membrane protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eGPM6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.22974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e4.16E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-9.8873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eglycoprotein M6A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.50643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e8.12E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-7.8812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003etransmembrane 4 L six family member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eGLYAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.23944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e8.81E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-7.83062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eglycine-N-acyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eC2orf40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-2.23128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e9.26E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-7.79952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003echromosome 2 open reading frame 40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.23194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.65E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-7.4467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003emyomesin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eSTXBP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.57584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e1.85E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-7.37721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003esyntaxin binding protein 6 (amisyn)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eDEFB132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.29844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e2.57E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-7.18026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003edefensin, beta 132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eLOC101926960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.54525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e3.10E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-7.07109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003euncharacterized LOC101926960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eC8orf34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.17111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e4.80E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-6.81691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003echromosome 8 open reading frame 34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eKANK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.52627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e6.54E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-6.64089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eKN motif and ankyrin repeat domains 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eTRDN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.66658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e8.50E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-6.49338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003etriadin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-2.9814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e2.00E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-6.02196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003echemokine (C-X-C motif) ligand 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.3474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e3.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-5.66618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003enuclear receptor subfamily 3, group C, member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eCSF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.35389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e4.09E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-5.64129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ecolony stimulating factor 3 (granulocyte)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-3.58351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e4.85E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-5.55206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eselectin E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e6.85E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-5.37288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003ethrombomodulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eLINC00968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.98628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e6.99E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-5.36194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003elong intergenic non-protein coding RNA 968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eLRRN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-1.40976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e7.79E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-5.30646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eleucine rich repeat neuronal 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8489%;\"\u003e\n \u003cp\u003eMAMDC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-2.3466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.35874%;\"\u003e\n \u003cp\u003e8.19E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.19842%;\"\u003e\n \u003cp\u003e-5.28032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4389%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52.9566%;\"\u003e\n \u003cp\u003eMAM domain containing 2\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\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e The enriched GO terms of the up and down regulated differentially expressed genes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0313%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCATEGORY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO \u0026nbsp; \u0026nbsp; Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eadjusted_p_value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enegative_log10_of_adjusted_p_value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.6949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.5385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eGO:0007155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0313%;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003ecell adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e0.000129946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0625%;\"\u003e\n \u003cp\u003e3.886238721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.6949%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.5385%;\"\u003e\n \u003cp\u003eSERPINE2,COL1A1,SFRP2,VCAN,SPON1,\u003cbr\u003eCOL6A3,LOC100506403,COL3A1,COL16A1,\u003cbr\u003eSTAB1,VMP1,STXBP6,SELE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eGO:0009617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0313%;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003eresponse to bacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e0.000129946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0625%;\"\u003e\n \u003cp\u003e3.886238721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.6949%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.5385%;\"\u003e\n \u003cp\u003eCOL1A1,SFRP2,COL1A2,VCAN,\u003cbr\u003eBGN,LOC100506403,COL3A1,PRRX1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eGO:0071944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0313%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003ecell periphery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e0.000687742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0625%;\"\u003e\n \u003cp\u003e3.162574496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.6949%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.5385%;\"\u003e\n \u003cp\u003eSERPINE2,COL1A1,SFRP2,COL1A2,\u003cbr\u003eVCAN,SPON1,BGN,COL6A3,COL3A1,\u003cbr\u003eCOL16A1,C4A,F2R,STAB1,VMP1,\u003cbr\u003eGPM6A,TM4SF1,TRDN,SELE,THBD,LRRN3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eGO:0012505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0313%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003eendomembrane system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e0.003307269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0625%;\"\u003e\n \u003cp\u003e2.480530528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.6949%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.5385%;\"\u003e\n \u003cp\u003eSERPINE2,COL1A1,COL1A2,VCAN,SPON1,\u003cbr\u003eBGN,COL6A3,COL3A1,TUG1,COL16A1,\u003cbr\u003eC4A,F2R,VMP1,TRDN,NR3C2,SELE,MAMDC2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eGO:0047962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0313%;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003eglycine N-benzoyltransferase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e0.017435608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0625%;\"\u003e\n \u003cp\u003e1.758562895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.6949%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.5385%;\"\u003e\n \u003cp\u003eGLYAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eGO:0071837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0313%;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003eHMG box domain binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6583%;\"\u003e\n \u003cp\u003e0.11769815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0625%;\"\u003e\n \u003cp\u003e0.929230363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 1.6949%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60.5385%;\"\u003e\n \u003cp\u003ePRRX1\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\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e The enriched pathway terms of the up and down regulated differentially expressed genes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8438%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.875%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eadjusted_p_\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enegative_log10_of_adjusted_p_value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.90625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.8438%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9062%;\"\u003e\n \u003cp\u003eREAC:R-HSA-1474244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8438%;\"\u003e\n \u003cp\u003eExtracellular matrix organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.875%;\"\u003e\n \u003cp\u003e4.05332E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.625%;\"\u003e\n \u003cp\u003e4.392188608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.90625%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.8438%;\"\u003e\n \u003cp\u003eCOL1A1,COL1A2,VCAN,BGN,\u003cbr\u003eCOL6A3,COL3A1,COL16A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9062%;\"\u003e\n \u003cp\u003eREAC:R-HSA-140875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8438%;\"\u003e\n \u003cp\u003eCommon Pathway of Fibrin Clot Formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.875%;\"\u003e\n \u003cp\u003e0.000174191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.625%;\"\u003e\n \u003cp\u003e3.758973492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.90625%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.8438%;\"\u003e\n \u003cp\u003eSERPINE2,F2R,THBD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e\u0026nbsp; Topology table for up and down regulated genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetweenness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCloseness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.393488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e32896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.382857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.347574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e33052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.376404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.198743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e27946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.304545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.172345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e16394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.308282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.097264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e7410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.233179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.03531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.271622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eBGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNT5DC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.068607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.279944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSTAB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.058955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e3774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.225843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSFRP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOL16A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.024545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.232102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCOL6A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.416667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.204209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e16614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.29646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.193212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e28686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.269437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.097264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.237028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.833333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.642857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.051972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.291304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.032551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e3792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.26378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCSF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.039502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e2420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.221366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eGLYAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0.039502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.192898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eTRDN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eC8orf34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.27459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eKANK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.27459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.8466%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eSTXBP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0118%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.1416%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.27459\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\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e\u0026nbsp; MiRNA - hub gene and TF \u0026ndash; hub gene \u0026nbsp;topology table\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHub Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHub Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-6515-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eFOXL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-518a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-146a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-24-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eSREBF1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-3913-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eEGR1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-6838-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eHOXA5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-135a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eJUN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-196b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eUSF2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eGATA2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-151a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eNFIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4796%;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.815%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8903%;\"\u003e\n \u003cp\u003ehsa-miR-4652-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.442%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3824%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2257%;\"\u003e\n \u003cp\u003eRELA\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\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e\u0026nbsp; Drug- hub gene topology table\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"412\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003eVorapaxar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003eHalofuginone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003eCollagenase clostridium histolyticum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003eCollagenase clostridium histolyticum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003eProgesterone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003eIbuprofen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.534%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53.3981%;\"\u003e\n \u003cp\u003eCarvedilol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8:\u0026nbsp;\u003c/strong\u003eBinding affinity and amino acid interaction of selected phytoconstituents against up-regulated genes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1040\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 455px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOL1A1 Gene (pTaBLEdb:3EJH) Amino acid interactions, Active Chain is A, Co-crystallised ligand is D-glucopyranose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 474px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOL1A2 Gene (pdb:5CTI) Amino acid interactions, Active chain is C, Co-crystallised ligand is Glycerol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffinity (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonding interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrostatic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophobic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffinity (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonding interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrostatic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eHydrophobic interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eMacrophylloside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eGLU:577, ARG:584, LYS:578, HIS:581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eTYR:579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eVAL:580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eGLU:46, ARG:45, SER:53, GLY:70, GLU:54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eALA:57, SER:53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eDichotomitin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eGLU:577, GLU:565, TYR:579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eGLU:565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eVAL:580, TYR:579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e-5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eGLN:58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eGLU:54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eALN:57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eCo-crystallized ligand\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eTYR:578, LYS: 579, TYR: 570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eGLU: 577, THR: 566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e-2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eALA: 68, GLY: 70, ALA: 57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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\u003e\u003cstrong\u003eTable 9\u0026nbsp;\u003c/strong\u003eBinding affinity and amino acid interaction of selected phytoconstituents against up-regulated genes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1042\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 456px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNR3C2 Gene (pdb:2AA2) Amino acid interactions, Active Chain is A, Co-crystallised ligand is Aldosterone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 475px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSELE Gene (pdb:1ESL) Amino acid interactions, Active chain is A, Co-crystallised ligand is Calcium cation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffinity (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonding interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrostatic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophobic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffinity (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonding interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrostatic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eHydrophobic interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eMacrophylloside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eASP:933, SER:936, HIS:932, VAL:971, GLY:974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eSER:936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eLEU:939, VAL:971, PRO:978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eARG:54, GLN:85, ASP:89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eLYS:55, ARG:54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eVAL:56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eDichotomitin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eHIS:932, THR:880, TYR:804, SER:936, GLU:967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eVAL:935, TYR:804, ALA:976, LEU:939, VAL:971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e-7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eGLY:131, CYS:122, THR:65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eGLU:135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eCYS:133, ALA:120, CYS:122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eCo-crystallized ligand\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eASN: 770, SER:810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eARG:817, MET:845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eSER:6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10\u0026nbsp;\u003c/strong\u003eADMET properties of phytoconstituents and cocrystallized ligands\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDichotomitin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacrophylloside\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAldosterone \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlycerol\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlpha D-glucopyranose\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTopological Polar Surface Area (TPSA, \u0026Aring;\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e321.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e107.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e91.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e60.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e110.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLog S (ESOL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e-3.43 (Moderately soluble)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e-3.06 (Moderately soluble)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-3.52\u003c/p\u003e\n \u003cp\u003e(Moderately soluble)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-1.56 (Soluble)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.99 (Soluble)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLog Po/w (XLOGP3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHuman Intestinal Absorption (HIA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e~98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e84%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eBBB Permeant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eP-gp Substrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eFraction Unbound (Human Plasma, Fu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e~0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.1267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCYP1A2 Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCYP2C19 Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCYP2C9 Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCYP2D6 Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCYP3A4 Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTotal Clearance (log mL/min/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e~0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eRenal OCT2 Substrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eOral Rat Acute Toxicity (LD50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.313 mol/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e0.46 mol/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHepatotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eYes (Predicted but low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eYes (Predicted but low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eYes (Predicted but low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eNo (Safe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNo (Safe)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Bioinformatics, gene expression omnibus (GEO), ovarian cancer, receiver operating characteristic (ROC), differentially expressed genes (DEGs), molecular docking studies, In-silico ADMET ","lastPublishedDoi":"10.21203/rs.3.rs-7138516/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7138516/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Ovarian cancer is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways associated in ovarian cancer progression and advancement. The present study aimed to elucidate potential key genes and signaling pathways in ovarian cancer. Microarray dataset (GSE120196) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 10 ovarian cancer samples and 4 normal control samples. Differentially expressed genes (DEGs) were identified using limma R bioconductor package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis. Protein-protein interaction (PPI) network was performed based on the DEGs. The hub gene-related miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed. Subsequently, the DrugBank database was utilized to search for alternative drugs targeting ovarian cancer hub genes. Receiver operating characteristic (ROC) curve analysis was performed for hub genes. Finally, molecular docking studies and in-silico ADMET were performed. In this work, 38 DEGs, including 19 up regulated genes and 19 down regulated genes, were obtained from microarray data. GO and REACTOME pathway enrichment analyses revealed significant enrichment of these genes in cell adhesion, cell periphery, glycine N-benzoyltransferase activity and extracellular matrix organization. Five up regulated genes, COL1A1, COL1A2, F2R, VCAN and SERPINE2, and five down regulated genes, NR3C2, SELE, CXCL2, MYOM1 and TM4SF1 in the center of the PPI network were associated with ovarian cancer, and these hub genes showed high sensitivity and specificity in ROC curve analysis. Notably, hsa-miR-6515-5p, hsa-miR-6838-5p, FOXL1 and HOXA5 have been identified as promising miRNAs and TFs for regulation of hub gene expression in ovarian cancer. Drug molecules include vorapaxar, halofuginone, progesterone and ibuprofen were predicted for treatment ovarian cancer. From molecular docking studies and in-silico ADMET investigation revealed macrphylloside D is potential lead molecule for ovarian cancer treatment. This investigation could serve as a basis for further understanding the molecular pathogenesis and potential therapeutic targets of ovarian cancer.","manuscriptTitle":"Integrated bioinformatics analysis reveals key candidate genes and signaling pathways, and Macrphylloside D as novel therapeutic agent in ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 05:29:58","doi":"10.21203/rs.3.rs-7138516/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":"a01d12c8-5612-4567-869c-61a82031e855","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T00:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-21 05:29:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7138516","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7138516","identity":"rs-7138516","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}