Integrated bioinformatics analysis reveals key candidate genes, signaling pathways and therapeutic molecules in ovarian cancer

Integrated bioinformatics analysis reveals key candidate genes, signaling pathways and therapeutic molecules 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, signaling pathways and therapeutic molecules in ovarian cancer Basavaraj Mallikarjunayya Vastrad, Shivaling Pattanashetti, Chanabasayya Vastrad This is a preprint; it has not been peer reviewed by a journal.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-7090018/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. Finally, receiver operating characteristic (ROC) curve analysis was performed for hub genes. 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. This investigation could serve as a basis for further understanding the molecular pathogenesis and potential therapeutic targets of ovarian cancer. Bioinformatics Cancer Biology bioinformatics gene expression omnibus (GEO) ovarian cancer receiver operating characteristic (ROC) differentially expressed genes (DEGs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 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. The final results will help us obtain novel treatment targets 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. 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 1). All DEGs were described by the volcano plot (Fig. 1). 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.2). 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 2). For CC, the obtained results indicated that proteins encoded by DEGs were mostly located in the cell periphery and endomembrane system (Table 2). For GO MF, the obtained results indicated that DEGs were significantly associated with glycine N-benzoyltransferase activity and HMG box domain binding (Table 2). The results of REACTOME pathway enrichment analysis showed that the pathways associated with extracellular matrix organization and common pathway of fibrin clot formation (Table 3). 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.3). 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 4). 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.4). 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 5). 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.5). 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 5). 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.6. 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 6). 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.6.). 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. In 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. 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. Identification of important invasion and proliferation related genes in adrenocortical carcinoma. Med Oncol. 2019;36(9):73. doi:10.1007/s12032-019-1296-7 Alur VC, Raju V, Vastrad B, Vastrad C. Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics. Diagnostics (Basel). 2019;9(2):39. doi:10.3390/diagnostics9020039 Au-Yeung CL, Yeung TL, Achreja A, Zhao H, Yip KP, Kwan SY, Onstad M, Sheng J, Zhu Y, Baluya DL, et al. ITLN1 modulates invasive potential and metabolic reprogramming of ovarian cancer cells in omental microenvironment. Nat Commun. 2020;11(1):3546. doi:10.1038/s41467-020-17383-2 Baba H, Kimura N, Kanegane H, Miya F, Kosaki K, Morio T, Koike R. GATA2 deficiency of a novel missense variant with multiorgan inflammation. Rheumatology (Oxford). 2024;63(8):e226-e228. doi:10.1093/rheumatology/keae062 Bai Y, Ren C, Wang B, Xue J, Li F, Liu J, Yang L. LncRNA MAFG-AS1 promotes the malignant phenotype of ovarian cancer by upregulating NFKB1-dependent IGF1. Cancer Gene Ther. 2022;29(3-4):277-291. doi:10.1038/s41417-021-00306-8 Boone JD, Arend RC, Johnston BE, Cooper SJ, Gilchrist SA, Oelschlager DK, Grizzle WE, McGwin G Jr, Gangrade A, Straughn JM Jr, et al. Targeting the Wnt/β-catenin pathway in primary ovarian cancer with the porcupine inhibitor WNT974. Lab Invest. 2016;96(2):249-259. doi:10.1038/labinvest.2015.150 Botteri E, Borroni E, Sloan EK, Bagnardi V, Bosetti C, Peveri G, Santucci C, Specchia C, van den Brandt P, Gallus S. Serine protease inhibitor E2 protects against cartilage tissue destruction and inflammation in osteoarthritis by targeting NF-κB signalling. Rheumatology (Oxford). 2024;63(11):3172-3183. doi:10.1093/rheumatology/keae452 Brilhante AV, Augusto KL, Portela MC, Sucupira LC, Oliveira LA, Pouchaim AJ, Nóbrega LR, Magalhães TF, Sobreira LR. Endometriosis and Ovarian Cancer: an Integrative Review (Endometriosis and Ovarian Cancer). Asian Pac J Cancer Prev. 2017;18(1):11-16. doi:10.22034/APJCP.2017.18.1.11 Cartwright T, Perkins ND, L Wilson C. NFKB1: a suppressor of inflammation, ageing and cancer. FEBS J. 2016;283(10):1812-1822. doi:10.1111/febs.13627 Chen LM, Wang W, Lee JC, Chiu FH, Wu CT, Tai CJ, Wang CK, Tai CJ, Huang MT, Chang YJ. Thrombomodulin mediates the progression of epithelial ovarian cancer cells. Tumour Biol. 2013;34(6):3743-3751. doi:10.1007/s13277-013-0958-x Chen S, Zhang H, Li H. MiR-135a is highly expressed and aggravates inflammatory response in sepsis by targeting MYOM1. Acta Biochim Pol. 2022;69(3):587-592. doi:10.18388/abp.2020_5882 Choi SY, Choi JH. Ovarian Cancer and the Microbiome: Connecting the Dots for Early Diagnosis and Therapeutic Innovations-A Review. Medicina (Kaunas). 2024;60(3):516. doi:10.3390/medicina60030516 Clough E, Barrett T. The Gene Expression Omnibus Database. Methods Mol Biol. 2016;1418:93-110. doi:10.1007/978-1-4939-3578-9_5 Coward JI, Middleton K, Murphy F. New perspectives on targeted therapy in ovarian cancer. Int J Womens Health. 2015;7:189-203. doi:10.2147/IJWH.S52379 Cunningham JM, Vierkant RA, Sellers TA, Phelan C, Rider DN, Liebow M, Schildkraut J, Berchuck A, Couch FJ, Wang X, et al. Cell cycle genes and ovarian cancer susceptibility: a tagSNP analysis. Br J Cancer. 2009;101(8):1461-1468. doi:10.1038/sj.bjc.6605284 Ding DN, Xie LZ, Shen Y, Li J, Guo Y, Fu Y, Liu FY, Han FJ. Insights into the Role of Oxidative Stress in Ovarian Cancer. Oxid Med Cell Longev. 2021;2021:8388258. doi:10.1155/2021/8388258 Druso JE, MacPherson MB, Chia SB, Elko E, Aboushousha R, Seward DJ, Abdelhamid H, Erickson C, Corteselli E, Tarte M, et al. Endoplasmic Reticulum Oxidative Stress Promotes Glutathione-Dependent Oxidation of Collagen-1A1 and Promotes Lung Fibroblast Activation. Am J Respir Cell Mol Biol. 2024;71(5):589-602. doi:10.1165/rcmb.2023-0379OC Eckhoff K, Flurschütz R, Trillsch F, Mahner S, Jänicke F, Milde-Langosch K. The prognostic significance of Jun transcription factors in ovarian cancer. J Cancer Res Clin Oncol. 2013;139(10):1673-1680. doi:10.1007/s00432-013-1489-y Elmasri WM, Casagrande G, Hoskins E, Kimm D, Kohn EC. Cell adhesion in ovarian cancer. Cancer Treat Res. 2009;149:297-318. doi:10.1007/978-0-387-98094-2_14 Erceylan ÖF, Savaş A, Göv E. Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer. Turk J Biol. 2021;45(2):127-137. doi:10.3906/biy-2010-39 Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B et al The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649–D655. doi:10.1093/nar/gkx1132 Fan Y, Xia J. miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol Biol. 2018; 1819:215-233. doi:10.1007/978-1-4939-8618-7_10 Fang SY, Zhang XM, Chen XP. Biglycan promotes proliferation and metastasis of ovarian cancer. Int J Clin Exp Pathol. 2025;18(4):166-172. doi:10.62347/DOZK6884 Gasparri ML, Bardhi E, Ruscito I, Papadia A, Farooqi AA, Marchetti C, Bogani G, Ceccacci I, Mueller MD, Benedetti Panici P. PI3K/AKT/mTOR Pathway in Ovarian Cancer Treatment: Are We on the Right Track?. Geburtshilfe Frauenheilkd. 2017;77(10):1095-1103. doi:10.1055/s-0043-118907 Geng Q, Xu J, Cao X, Wang Z, Jiao Y, Diao W, Wang X, Wang Z, Zhang M, Zhao L, et al. PPARG-mediated autophagy activation alleviates inflammation in rheumatoid arthritis. J Autoimmun. 2024;146:103214. doi:10.1016/j.jaut.2024.103214 Gesta S, Guntur K, Majumdar ID, Akella S, Vishnudas VK, Sarangarajan R, Narain NR. Reduced expression of collagen VI alpha 3 (COL6A3) confers resistance to inflammation-induced MCP1 expression in adipocytes. Obesity (Silver Spring). 2016;24(8):1695-1703. doi:10.1002/oby.21565 Gilbert M, Li Z, Wu XN, Rohr L, Gombos S, Harter K, Schulze WX. Comparison of path-based centrality measures in protein-protein interaction networks revealed proteins with phenotypic relevance during adaptation to changing nitrogen environments. J Proteomics. 2021;235:104114. doi:10.1016/j.jprot.2021.104114 Green GH, Diggle PJ. On the operational characteristics of the Benjamini and Hochberg False Discovery Rate procedure. Stat Appl Genet Mol Biol. 2007;6:Article27. doi:10.2202/1544-6115.1302 Guo D, Zhang W, Yang H, Bi J, Xie Y, Cheng B, Wang Y, Chen S. Celastrol Induces Necroptosis and Ameliorates Inflammation via Targeting Biglycan in Human Gastric Carcinoma. Int J Mol Sci. 2019;20(22):5716. doi:10.3390/ijms20225716 Guo Y, Liu Z, Wang M. NFKB1-mediated downregulation of microRNA-106a promotes oxidative stress injury and insulin resistance in mice with gestational hypertension. Cytotechnology. 2021;73(1):115-126. doi:10.1007/s10616-020-00448-x Hendrikse CSE, Theelen PMM, van der Ploeg P, Westgeest HM, Boere IA, Thijs AMJ, Ottevanger PB, van de Stolpe A, Lambrechts S, Bekkers RLM, et al. The potential of RAS/RAF/MEK/ERK (MAPK) signaling pathway inhibitors in ovarian cancer: A systematic review and meta-analysis. Gynecol Oncol. 2023;171:83-94. doi:10.1016/j.ygyno.2023.01.038 Ho CM, Yen TL, Chang TH, Huang SH. COL6A3 Exosomes Promote Tumor Dissemination and Metastasis in Epithelial Ovarian Cancer. Int J Mol Sci. 2024;25(15):8121. doi:10.3390/ijms25158121 Hollis RL, Gourley C. Genetic and molecular changes in ovarian cancer. Cancer Biol Med. 2016;13(2):236-247. doi:10.20892/j.issn.2095-3941.2016.0024 Huang D, Chen D, Hu T, Liang H. GATA2 promotes oxidative stress to aggravate renal ischemia-reperfusion injury by up-regulating Redd1. Mol Immunol. 2023;153:75-84. doi:10.1016/j.molimm.2022.09.012 Huang Y, Wang Y, Ouyang Y. Elevated microRNA-135b-5p relieves neuronal injury and inflammation in post-stroke cognitive impairment by targeting NR3C2. Int J Neurosci. 2022;132(1):58-66. doi:10.1080/00207454.2020.1802265 Huang Z, Yao H, Yang Z. Prognostic significance of TM4SF1 and DDR1 expression in epithelial ovarian cancer. Oncol Lett. 2023;26(4):448. doi:10.3892/ol.2023.14035 Joshi H, Vastrad B, Vastrad C. Identification of Important Invasion-Related Genes in Non-functional Pituitary Adenomas. J Mol Neurosci. 2019;68(4):565-589. doi:10.1007/s12031-019-01318-8 Kim MJ, Lee SJ, Ryu JH, Kim SH, Kwon IC, Roberts TM. Combination of KRAS gene silencing and PI3K inhibition for ovarian cancer treatment. J Control Release. 2020;318:98-108. doi:10.1016/j.jconrel.2019.12.019 Kolasa IK, Rembiszewska A, Felisiak A, Ziolkowska-Seta I, Murawska M, Moes J, Timorek A, Dansonka-Mieszkowska A, Kupryjanczyk J. PIK3CA amplification associates with resistance to chemotherapy in ovarian cancer patients. Cancer Biol Ther. 2009;8(1):21-26. doi:10.4161/cbt.8.1.7209 Koussounadis A, Langdon SP, Harrison DJ, Smith VA. Chemotherapy-induced dynamic gene expression changes in vivo are prognostic in ovarian cancer. Br J Cancer. 2014;110(12):2975-2984. doi:10.1038/bjc.2014.258 Kuroda Y, Chiyoda T, Kawaida M, Nakamura K, Aimono E, Yoshimura T, Takahashi M, Saotome K, Yoshihama T, Iwasa N, et al. ARID1A mutation/ARID1A loss is associated with a high immunogenic profile in clear cell ovarian cancer. Gynecol Oncol. 2021;162(3):679-685. doi:10.1016/j.ygyno.2021.07.005 Leizer AL, Alvero AB, Fu HH, Holmberg JC, Cheng YC, Silasi DA, Rutherford T, Mor G. Regulation of inflammation by the NF-κB pathway in ovarian cancer stem cells. Am J Reprod Immunol. 2011;65(4):438-447. doi:10.1111/j.1600-0897.2010.00914.x Li C, Zhang S, Chen X, Ji J, Yang W, Gui T, Gai Z, Li Y. Farnesoid X receptor activation inhibits TGFBR1/TAK1-mediated vascular inflammation and calcification via miR-135a-5p. Commun Biol. 2020;3(1):327. doi:10.1038/s42003-020-1058-2 Li G, Li M, Wang J, Li Y, Pan Y. United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins. IEEE/ACM Trans Comput Biol Bioinform. 2020;17(4):1451-1458. doi:10.1109/TCBB.2018.2889978 Li H, Wu N, Liu ZY, Chen YC, Cheng Q, Wang J. Development of a novel transcription factors-related prognostic signature for serous ovarian cancer. Sci Rep. 2021;11(1):7207. doi:10.1038/s41598-021-86294-z Li T, Zhang H, Lian M, He Q, Lv M, Zhai L, Zhou J, Wu K, Yi M. Global status and attributable risk factors of breast, cervical, ovarian, and uterine cancers from 1990 to 2021. J Hematol Oncol. 2025;18(1):5. doi:10.1186/s13045-025-01660-y Li Y, Li W, Tan Y, Liu F, Cao Y, Lee KY. Hierarchical Decomposition for Betweenness Centrality Measure of Complex Networks. Sci Rep. 2017;7:46491.. doi:10.1038/srep46491 Li Y, Zhang J, Dai Y, Fan Y, Xu J. Novel Mutations in COL6A3 That Associated With Peters' Anomaly Caused Abnormal Intracellular Protein Retention and Decreased Cellular Resistance to Oxidative Stress. Front Cell Dev Biol. 2020;8:531986. doi:10.3389/fcell.2020.531986 Liu S, Liu J, Yang X, Jiang M, Wang Q, Zhang L, Ma Y, Shen Z, Tian Z, Cao X. Cis-acting lnc-Cxcl2 restrains neutrophil-mediated lung inflammation by inhibiting epithelial cell CXCL2 expression in virus infection. Proc Natl Acad Sci U S A. 2021;118(41):e2108276118. doi:10.1073/pnas.2108276118 Liu T, Zhao L, Chen W, Li Z, Hou H, Ding L, Li X. Inactivation of von Hippel-Lindau increases ovarian cancer cell aggressiveness through the HIF1α/miR-210/VMP1 signaling pathway. Int J Mol Med. 2014;33(5):1236-1242. doi:10.3892/ijmm.2014.1661 Liu Y, Wang Z, Xie W, Gu Z, Xu Q, Su L. Oxidative stress regulates mitogen‑activated protein kinases and c‑Jun activation involved in heat stress and lipopolysaccharide‑induced intestinal epithelial cell apoptosis. Mol Med Rep. 2017;16(3):2579-2587. doi:10.3892/mmr.2017.6859 Liu Z, Zhang TT, Zhao JJ, Qi SF, Du P, Liu DW, Tian QB. The association between overweight, obesity and ovarian cancer: a meta-analysis. Jpn J Clin Oncol. 2015;45(12):1107-1115. doi:10.1093/jjco/hyv150 Lloyd KL, Cree IA, Savage RS. Prediction of resistance to chemotherapy in ovarian cancer: a systematic review. BMC Cancer. 2015;15:117. doi:10.1186/s12885-015-1101-8 Luo S, Wang J, Ma Y, Yao Z, Pan H. PPARγ inhibits ovarian cancer cells proliferation through upregulation of miR-125b. Biochem Biophys Res Commun. 2015;462(2):85-90. doi:10.1016/j.bbrc.2015.04.023 Luo X, Guo L, Dai XJ, Wang Q, Zhu W, Miao X, Gong H. Abnormal intrinsic functional hubs in alcohol dependence: evidence from a voxelwise degree centrality analysis. Neuropsychiatr Dis Treat. 2017;13:2011-2020. doi:10.2147/NDT.S142742 Maas HA, Kruitwagen RF, Lemmens VE, Goey SH, Janssen-Heijnen ML. The influence of age and co-morbidity on treatment and prognosis of ovarian cancer: a population-based study. Gynecol Oncol. 2005;97(1):104-109. doi:10.1016/j.ygyno.2004.12.026 Mahoney DE, Pierce JD. Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and Management. Clin J Oncol Nurs. 2022;26(5):533-542. doi:10.1188/22.CJON.533-542 Martins FC, Couturier DL, Paterson A, Karnezis AN, Chow C, Nazeran TM, Odunsi A, Gentry-Maharaj A, Vrvilo A, Hein A, Talhouk A, et al. Clinical and pathological associations of PTEN expression in ovarian cancer: a multicentre study from the Ovarian Tumour Tissue Analysis Consortium. Br J Cancer. 2020;123(5):793-802. doi:10.1038/s41416-020-0900-0 Nagasawa S, Ikeda K, Shintani D, Yang C, Takeda S, Hasegawa K, Horie K, Inoue S. Identification of a Novel Oncogenic Fusion Gene SPON1-TRIM29 in Clinical Ovarian Cancer That Promotes Cell and Tumor Growth and Enhances Chemoresistance in A2780 Cells. Int J Mol Sci. 2022;23(2):689. doi:10.3390/ijms23020689 Nick AM, Coleman RL, Ramirez PT, Sood AK. A framework for a personalized surgical approach to ovarian cancer. Nat Rev Clin Oncol. 2015;12(4):239-245. doi:10.1038/nrclinonc.2015.26 Okuno Y, Fukuhara A, Hashimoto E, Kobayashi H, Kobayashi S, Otsuki M, Shimomura I. Oxidative Stress Inhibits Healthy Adipose Expansion Through Suppression of SREBF1-Mediated Lipogenic Pathway. Diabetes. 2018;67(6):1113-1127. doi:10.2337/db17-1032 Pan X, Yang L, Wang S, Liu Y, Yue L, Chen S. Semaglutide ameliorates obesity-induced cardiac inflammation and oxidative stress mediated via reduction of neutrophil Cxcl2, S100a8, and S100a9 expression. Mol Cell Biochem. 2024;479(5):1133-1147. doi:10.1007/s11010-023-04784-2 Papakonstantinou E, Androutsopoulos G, Logotheti S, Adonakis G, Maroulis I, Tzelepi V. DNA Methylation in Epithelial Ovarian Cancer: Current Data and Future Perspectives. Curr Mol Pharmacol. 2021;14(6):1013-1027. doi:10.2174/1874467213666200810141858 Ploeger C, Schreck J, Huth T, Fraas A, Albrecht T, Charbel A, Ji J, Singer S, Breuhahn K, Pusch S, et al. STAT1 and STAT3 Exhibit a Crosstalk and Are Associated with Increased Inflammation in Hepatocellular Carcinoma. Cancers (Basel). 2022;14(5):1154. doi:10.3390/cancers14051154 Porras P, Barrera E, Bridge A, Del-Toro N, Cesareni G, Duesbury M, Hermjakob H, Iannuccelli M, Jurisica I, Kotlyar M, et al. Towards a unified open access dataset of molecular interactions. Nat Commun. 2020;11(1):6144. doi:10.1038/s41467-020-19942-z Puttock EH, Tyler EJ, Manni M, Maniati E, Butterworth C, Burger Ramos M, Peerani E, Hirani P, Gauthier V, Liu Y, et al. Extracellular matrix educates an immunoregulatory tumor macrophage phenotype found in ovarian cancer metastasis. Nat Commun. 2023;14(1):2514. doi:10.1038/s41467-023-38093-5 Rafii A, Halabi NM, Malek JA. High-prevalence and broad spectrum of Cell Adhesion and Extracellular Matrix gene pathway mutations in epithelial ovarian cancer. J Clin Bioinforma. 2012;2(1):15. doi:10.1186/2043-9113-2-15 Reimand J, Kull M, Peterson H, Hansen J, Vilo J. g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007;35(Web Server issue):W193-W200. doi:10.1093/nar/gkm226 Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:10.1093/nar/gkv007 Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77. doi:10.1186/1471-2105-12-77 Saed GM. Is there a link between talcum powder, oxidative stress, and ovarian cancer risk?. Expert Rev Anticancer Ther. 2024;24(7):485-491. doi:10.1080/14737140.2024.2352506 Saijo H, Hirohashi Y, Torigoe T, Horibe R, Takaya A, Murai A, Kubo T, Kajiwara T, Tanaka T, Shionoya Y, et al. Plasticity of lung cancer stem-like cells is regulated by the transcription factor HOXA5 that is induced by oxidative stress. Oncotarget. 2016;7(31):50043-50056. doi:10.18632/oncotarget.10571 Savant SS, Sriramkumar S, O'Hagan HM. The Role of Inflammation and Inflammatory Mediators in the Development, Progression, Metastasis, and Chemoresistance of Epithelial Ovarian Cancer. Cancers (Basel). 2018;10(8):251. doi:10.3390/cancers10080251 Schonthaler HB, Guinea-Viniegra J, Wagner EF. Targeting inflammation by modulating the Jun/AP-1 pathway. Ann Rheum Dis. 2011;70 Suppl 1:i109-i112. doi:10.1136/ard.2010.140533 Shi W, Sun H, Yao Q, Liu H, Zhang L, Han W. Phellodendrine ameliorates intestinal inflammation and protects mucosal barrier via modulating COL1A1, VCAM1 and IL-17 a. Int Immunopharmacol. 2025;152:114403. doi:10.1016/j.intimp.2025.114403 Siminiak N, Czepczyński R, Zaborowski MP, Iżycki D. Immunotherapy in Ovarian Cancer. Arch Immunol Ther Exp (Warsz). 2022;70(1):19. doi:10.1007/s00005-022-00655-8 Simpkins F, Garcia-Soto A, Slingerland J. New insights on the role of hormonal therapy in ovarian cancer. Steroids. 2013;78(6):530-537. doi:10.1016/j.steroids.2013.01.008 Son ES, Ko UW, Jeong HY, Park SY, Lee YE, Park JW, Jeong SH, Kim SH, Kyung SY. miRNA-6515-5p regulates particulate matter-induced inflammatory responses by targeting CSF3 in human bronchial epithelial cells. Toxicol In Vitro. 2022;84:105428. doi:10.1016/j.tiv.2022.105428 Su J, Ruan S, Dai S, Mi J, Chen W, Jiang S. NF1 regulates apoptosis in ovarian cancer cells by targeting MCL1 via miR-142-5p. Pharmacogenomics. 2019;20(3):155-165. doi:10.2217/pgs-2018-0161 Szabados T, Molnár A, Kenyeres É, Gömöri K, Pipis J, Pósa B, Makkos A, Ágg B, Giricz Z, Ferdinandy P, et al. Identification of New, Translatable ProtectomiRs against Myocardial Ischemia/Reperfusion Injury and Oxidative Stress: The Role of MMP/Biglycan Signaling Pathways. Antioxidants (Basel). 2024;13(6):674. doi:10.3390/antiox13060674 Takamizawa S, Kojima J, Umezu T, Kuroda M, Hayashi S, Maruta T, Okamoto A, Nishi H. miR‑146a‑5p and miR‑191‑5p as novel diagnostic marker candidates for ovarian clear cell carcinoma. Mol Clin Oncol. 2023;20(2):14. doi:10.3892/mco.2023.2712 Thomas PD. The Gene Ontology and the Meaning of Biological Function. Methods Mol Biol. 2017;1446:15‐24. doi:10.1007/978-1-4939-3743-1_2 Totten SP, Im YK, Cepeda Cañedo E, Najyb O, Nguyen A, Hébert S, Ahn R, Lewis K, Lebeau B, La Selva R, et al. STAT1 potentiates oxidative stress revealing a targetable vulnerability that increases phenformin efficacy in breast cancer. Nat Commun. 2021;12(1):3299. doi:10.1038/s41467-021-23396-2 Vastrad B, Vastrad C, Tengli A, Iliger S. Identification of differentially expressed genes regulated by molecular signature in breast cancer-associated fibroblasts by bioinformatics analysis. Arch Gynecol Obstet. 2018;297(1):161-183. doi:10.1007/s00404-017-4562-y Walsh CS. Latest clinical evidence of maintenance therapy in ovarian cancer. Curr Opin Obstet Gynecol. 2020;32(1):15-21. doi:10.1097/GCO.0000000000000592 Wang F, Niu Y, Chen K, Yuan X, Qin Y, Zheng F, Cui Z, Lu W, Wu Y, Xia D. Extracellular Vesicle-Packaged circATP2B4 Mediates M2 Macrophage Polarization via miR-532-3p/SREBF1 Axis to Promote Epithelial Ovarian Cancer Metastasis. Cancer Immunol Res. 2023;11(2):199-216. doi:10.1158/2326-6066.CIR-22-0410 Wang T, Townsend MK, Simmons V, Terry KL, Matulonis UA, Tworoger SS. Prediagnosis and postdiagnosis smoking and survival following diagnosis with ovarian cancer. Int J Cancer. 2020;147(3):736-746. doi:10.1002/ijc.32773 Wang X, Zhu M, Loor JJ, Jiang Q, Zhu Y, Li W, Du X, Song Y, Gao W, Lei L, et al. Propionate alleviates fatty acid-induced mitochondrial dysfunction, oxidative stress, and apoptosis by upregulating PPARG coactivator 1 alpha in hepatocytes. J Dairy Sci. 2022;105(5):4581-4592. doi:10.3168/jds.2021-21198 Webb PM, Jordan SJ. Epidemiology of epithelial ovarian cancer. Best Pract Res Clin Obstet Gynaecol. 2017;41:3-14. doi:10.1016/j.bpobgyn.2016.08.006 Wight TN, Kang I, Evanko SP, Harten IA, Chang MY, Pearce OMT, Allen CE, Frevert CW. Versican-A Critical Extracellular Matrix Regulator of Immunity and Inflammation. Front Immunol. 2020;11:512. doi:10.3389/fimmu.2020.00512 Wu Y, Wu J, Lee DY, Yee A, Cao L, Zhang Y, Kiani C, Yang BB. Versican protects cells from oxidative stress-induced apoptosis. Matrix Biol. 2005;24(1):3-13. doi:10.1016/j.matbio.2004.11.007 Xiao X, Long F, Yu S, Wu W, Nie D, Ren X, Li W, Wang X, Yu L, Wang P, et al. Col1A1 as a new decoder of clinical features and immune microenvironment in ovarian cancer. Front Immunol. 2025;15:1496090. doi:10.3389/fimmu.2024.1496090 Xu J, Zhou K, Gu H, Zhang Y, Wu L, Bian C, Huang Z, Chen G, Cheng X, Yin X. Exosome miR-4738-3p-mediated regulation of COL1A2 through the NF-κB and inflammation signaling pathway alleviates osteoarthritis low-grade inflammation symptoms. Biomol Biomed. 2024;24(3):520-536. doi:10.17305/bb.2023.9921 Yang B, Yin S, Zhou Z, Huang L, Xi M. Inflammation Control and Tumor Growth Inhibition of Ovarian Cancer by Targeting Adhesion Molecules of E-Selectin. Cancers (Basel). 2023;15(7):2136. doi:10.3390/cancers15072136 Yang J, He R, Zhang X, Wang X, Liu M, Liu X, Li Y. KLC3 activates PI3K/AKT signaling and promotes ovarian cancer cell proliferation and migration through COL3A1. Oncol Rep. 2025;53(6):67. doi:10.3892/or.2025.8900 Yang SM, Ka SM, Wu HL, Yeh YC, Kuo CH, Hua KF, Shi GY, Hung YJ, Hsiao FC, Yang SS, et al. Thrombomodulin domain 1 ameliorates diabetic nephropathy in mice via anti-NF-κB/NLRP3 inflammasome-mediated inflammation, enhancement of NRF2 antioxidant activity and inhibition of apoptosis. Diabetologia. 2014;57(2):424-434. doi:10.1007/s00125-013-3115-6 Yin X, Wang X, Wang S, Xia Y, Chen H, Yin L, Hu K. Screening for Regulatory Network of miRNA-Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study. Int J Gen Med. 2022;15:1715-1731. doi:10.2147/IJGM.S354359 Yu X, Zhao P, Luo Q, Wu X, Wang Y, Nan Y, Liu S, Gao W, Li B, Liu Z, et al. RUNX1-IT1 acts as a scaffold of STAT1 and NuRD complex to promote ROS-mediated NF-κB activation and ovarian cancer progression. Oncogene. 2024;43(6):420-433. doi:10.1038/s41388-023-02910-4 Zhang F, Jiang J, Xu B, Xu Y, Wu C. Over-expression of CXCL2 is associated with poor prognosis in patients with ovarian cancer. Medicine (Baltimore). 2021;100(4):e24125. doi:10.1097/MD.0000000000024125 Zhang J, Yang X, Zong Y, Yu T, Yang X. miR-196b-5p regulates inflammatory process and migration via targeting Nras in trabecular meshwork cells. Int Immunopharmacol. 2024;129:111646. doi:10.1016/j.intimp.2024.111646 Zhang J, Zhang S, Xu S, Zhu Z, Li J, Wang Z, Wada Y, Gatt A, Liu J. Oxidative Stress Induces E-Selectin Expression through Repression of Endothelial Transcription Factor ERG. J Immunol. 2023;211(12):1835-1843. doi:10.4049/jimmunol.2300043 Zhang L, Zhang L, Li S, Zhang Q, Luo Y, Zhang C, Huan Q, Zhang C. Overexpression of mm9_circ_013935 alleviates renal inflammation and fibrosis in diabetic nephropathy via the miR-153-3p/NFIC axis. Can J Physiol Pharmacol. 2021;99(11):1199-1206. doi:10.1139/cjpp-2021-0187 Zhao H, Yu H, Zheng J, Ning N, Tang F, Yang Y, Wang Y. Lowly-expressed lncRNA GAS5 facilitates progression of ovarian cancer through targeting miR-196-5p and thereby regulating HOXA5. Gynecol Oncol. 2018;151(2):345-355. doi:10.1016/j.ygyno.2018.08.032 Zhao L, Liang X, Wang L, Zhang X. The Role of miRNA in Ovarian Cancer: an Overview. Reprod Sci. 2022;29(10):2760-2767. doi:10.1007/s43032-021-00717-w Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234-W241. doi:10.1093/nar/gkz240 Zhou L, Cui M, Yu J, Liu Y, Zeng F, Liu Y. 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 Zhu 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 Zou 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 Tables Table 1 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 2 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 3 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 4 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 5 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 6 Drug- hub gene topology table Regulation Gene Degree Drug Up F2R 4 Vorapaxar Up COL1A1 2 Halofuginone Up COL1A2 1 Collagenase clostridium histolyticum Up COL3A1 1 Collagenase clostridium histolyticum Additional Declarations The authors declare no competing interests. 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College of Pharmacy, Gadag 582101, Karnataka, India.","correspondingAuthor":false,"prefix":"","firstName":"Basavaraj","middleName":"Mallikarjunayya","lastName":"Vastrad","suffix":""},{"id":483337816,"identity":"4a905af3-a45a-433e-9c58-5b04fd04af20","order_by":1,"name":"Shivaling Pattanashetti","email":"","orcid":"https://orcid.org/0009-0003-9246-1604","institution":"Department of Pharmaceutical Chemistry, K.L.E. College of Pharmacy, Gadag 582101, Karnataka, India.","correspondingAuthor":false,"prefix":"","firstName":"Shivaling","middleName":"","lastName":"Pattanashetti","suffix":""},{"id":483337818,"identity":"338d21d6-7f96-47a1-a47d-5d6823ab10a5","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":"https://orcid.org/0000-0003-3615-4450","institution":"Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India.","correspondingAuthor":true,"prefix":"","firstName":"Chanabasayya","middleName":"","lastName":"Vastrad","suffix":""}],"badges":[],"createdAt":"2025-07-10 07:21:51","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7090018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7090018/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86470938,"identity":"a19f05cb-1310-440a-9c97-a987655aa290","added_by":"auto","created_at":"2025-07-11 05:32:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159701,"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":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/8fff219c2f8e06dd57ab8e3e.png"},{"id":86471616,"identity":"2159f6e2-bbc1-477a-b45e-3cd575ace763","added_by":"auto","created_at":"2025-07-11 05:40:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185086,"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":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/0e784047b24508652df6b104.png"},{"id":86470941,"identity":"40b30d05-f52d-484a-8d78-dfb5ad7d04b1","added_by":"auto","created_at":"2025-07-11 05:32:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":406602,"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":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/8fe6d09a39bcf90c6ad73fc2.png"},{"id":86471875,"identity":"84cfdb5a-d2f5-4fbe-af7b-d0648ee67e87","added_by":"auto","created_at":"2025-07-11 05:48:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":645850,"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":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/0b550168016950fe4b25187d.png"},{"id":86470949,"identity":"20ac28bd-02bb-4b86-aab8-76b7babf05cb","added_by":"auto","created_at":"2025-07-11 05:32:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":417657,"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":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/94c4c390f3a1932826ae105f.png"},{"id":86470953,"identity":"0645adab-9655-4383-a0b2-39fcadba0753","added_by":"auto","created_at":"2025-07-11 05:32:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135498,"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":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/1f7adf80ba2147c6dd66922a.png"},{"id":86470942,"identity":"0aead30d-ad8f-4b6e-938b-d4649ea637ae","added_by":"auto","created_at":"2025-07-11 05:32:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":145399,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analyses of hub genes. A) COL1A1 B) COL1A2 C) NR3C2 D) SELE\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/f6f5740e648203cca9b71c22.png"},{"id":86472593,"identity":"d31a8470-5454-45b4-96dc-433c78cce617","added_by":"auto","created_at":"2025-07-11 05:56:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3225727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7090018/v1/2e92e920-22e4-403e-a5d2-1a9a9412c768.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrated bioinformatics analysis reveals key candidate genes, signaling pathways and therapeutic molecules in ovarian cancer\u003c/strong\u003e\u003c/p\u003e","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 \u0026nbsp;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\u0026nbsp;ovarian cancer\u0026nbsp;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\u0026nbsp;et al.\u0026nbsp;2018] and oxidative stress [Ding\u0026nbsp;et al. 2021]. Therefore, the early prognosis and diagnosis of\u0026nbsp;ovarian cancer remains a essential and concern for doctors and scientists, and examination of novel biomarkers and therapeutic targets\u0026nbsp;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;\u0026nbsp;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.\u0026nbsp;\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. The final results will help us obtain novel treatment targets 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 ≤ 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 .\u0026nbsp;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.\u0026nbsp;2018] \u0026nbsp;is a resource of pathway database for the clarification of high-level features and effects of biological systems. P \u0026lt; 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; (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.\u0026nbsp;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].\u0026nbsp;\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 (\u003ca href=\"https://www.mirnet.ca/\"\u003ehttps://www.mirnet.ca/\u003c/a\u003e) [Fan et al 2018]. \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.\u0026nbsp;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.\u0026nbsp;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.\u0026nbsp;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’s diagnostic performance, with an AUC value greater than 0.8 considered indicative of diagnostic value.\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 ‘limma (version 3.5.1)’. In total, 38 DEGs were identified, including 19 up regulated genes and 19 down regulated genes (Table 1). All DEGs were described by the volcano plot (Fig. 1). Volcano plot with cut-off criteria set to adj.P.Val ≤ 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.2).\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 2). \u0026nbsp;For CC, the obtained results indicated that proteins encoded by DEGs were mostly located in the\u0026nbsp;\u0026nbsp;cell periphery and endomembrane system (Table 2). For GO MF, the obtained results indicated that DEGs were significantly associated with glycine N-benzoyltransferase activity and HMG box domain binding (Table 2). The results of REACTOME pathway enrichment analysis showed that the pathways associated with extracellular matrix organization and common pathway of fibrin clot formation (Table 3).\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.3). 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 4).\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.4). 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 5).\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.5). 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 5).\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.6. 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 6).\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 = 0.931), COL1A2 (AUC = 0.910), NR3C2 (AUC = 0.934) \u0026nbsp; and SELE (AUC = 0.917) exhibiting robust predictive performance (Fig.6.).\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 \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e], cell adhesion genes [Rafii et al \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e], and DNA methylation [Papakonstantinou et al \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] 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 \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], COL1A1 [Shi et al \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], COL1A2 [Xu et al \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], VCAN (versican) [Wight et al \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and MYOM1 [Chen et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] are linked with inflammation. COL1A1 [Xiao et al \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] is linked with the proliferation and invasion of ovarian cancer cells. The functional role of VCAN (versican) [Zhou et al \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] 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 \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. COL1A1 [Druso et al \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] and VCAN (versican) [Wu et al \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2005\u003c/span\u003e] 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\u003cp\u003eGene Ontology (GO) and pathway enrichment analysis can help us better understand the specific molecular pathogenesis of ovarian cancer. Extracellular matrix organization [Puttock et al \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and cell adhesion [Elmasri et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e] 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 \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. COL6A3 [Ho et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] 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 \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. Some studies show that SPON1 plays an important role in chemoresistance in ovarian cancer [Nagasawa et al \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. VMP1 [Liu et al \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e] has been linked to ovarian cancer cell invasion and metastasis. SELE (selectin E) [Yang et al \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] 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 \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e]. BGN (biglycan) [Guo et al \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], COL6A3 [Gesta et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], SELE (selectin E) [Yang et al \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] 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 \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], COL6A3 [Li et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and SELE (selectin E) [Zhang et al \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] 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\u003cp\u003eWe performed PPI network construction and analysis to investigate the hub genes related to ovarian cancer. COL1A1 [Xiao et al \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], VCAN (versican) [Zhou et al \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], SELE (selectin E) [Yang et al \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], CXCL2 [Zhang et al \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and TM4SF1 [Huang et al \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] promotes the development and progression of ovarian cancer. COL1A1 [Shi et al \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], COL1A2 [Xu et al \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], VCAN (versican) [Wight et al \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], SERPINE2 [Botteri et al \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NR3C2 [Huang et al \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], CXCL2 [Liu et al \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] and MYOM1 [Chen et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e] pays a major role in the occurrence and development of inflammation. COL1A1 [Druso et al \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], VCAN (versican) [Wu et al \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2005\u003c/span\u003e], SELE (selectin E) [Zhang et al \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] and CXCL2 [Pan et al \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] 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\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 were hsa-miR-146a-5p [Takamizawa et al \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e], STAT1 [Yu et al \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], PPARG [Luo et al \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e], NFKB1 [Bai et al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], SREBF1 [Wang et al 2021], HOXA5 [Zhao et al \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], JUN [Eckhoff et al \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e] and GATA2 [Erceylan et al \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] are associated with ovarian cancer. Hsa-miR-6515-5p [Son et al \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], hsa-miR-518a-3p [Yin et al \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], hsa-miR-135a-5p [Li et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], hsa-miR-196b-5p [Zhang et al \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], STAT1 [Ploeger et al \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], PPARG [Geng et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], NFKB1 [Cartwright et al \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], HOXA5 [Zhu and Ma, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], JUN [Schonthaler et al \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e], GATA2 [Baba et al \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] and NFIC [Zhang et al \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e] expression might be a shared target for inflammation. hsa-miR-518a-3p [Yin et al \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], STAT1 [Totten et al \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], PPARG [Wang et al \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], NFKB1 [Guo et al \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], SREBF1 [Okuno et al \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], HOXA5 [Saijo et al \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2016\u003c/span\u003e], JUN [Liu et al \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e] and GATA2 [Huang et al \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e] 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.\u003c/p\u003e\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\u003cp\u003eIn 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.\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) (\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e) 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. \u0026nbsp; \u0026nbsp;- Writing original draft, and review and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS.P. \u0026nbsp; \u0026nbsp; - Formal analysis and validation\u003c/p\u003e\n\u003cp\u003eC. V. \u0026nbsp; - 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; ORCID ID: 0000-0003-2202-7637\u003c/p\u003e\n\u003cp\u003eShivaling Pattanashetti \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;ORCID ID: 0000-0003-3615-4450\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkbarzadeh 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\u003c/li\u003e\n\u003cli\u003eAlshabi AM, Vastrad B, Shaikh IA, Vastrad C. Identification of important invasion and proliferation related genes in adrenocortical carcinoma. Med Oncol. 2019;36(9):73. doi:10.1007/s12032-019-1296-7\u003c/li\u003e\n\u003cli\u003eAlur VC, Raju V, Vastrad B, Vastrad C. Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics. Diagnostics (Basel). 2019;9(2):39. doi:10.3390/diagnostics9020039\u003c/li\u003e\n\u003cli\u003eAu-Yeung CL, Yeung TL, Achreja A, Zhao H, Yip KP, Kwan SY, Onstad M, Sheng J, Zhu Y, Baluya DL, et al. ITLN1 modulates invasive potential and metabolic reprogramming of ovarian cancer cells in omental microenvironment. Nat Commun. 2020;11(1):3546. doi:10.1038/s41467-020-17383-2\u003c/li\u003e\n\u003cli\u003eBaba H, Kimura N, Kanegane H, Miya F, Kosaki K, Morio T, Koike R. GATA2 deficiency of a novel missense variant with multiorgan inflammation. Rheumatology (Oxford). 2024;63(8):e226-e228. doi:10.1093/rheumatology/keae062\u003c/li\u003e\n\u003cli\u003eBai Y, Ren C, Wang B, Xue J, Li F, Liu J, Yang L. LncRNA MAFG-AS1 promotes the malignant phenotype of ovarian cancer by upregulating NFKB1-dependent IGF1. Cancer Gene Ther. 2022;29(3-4):277-291. doi:10.1038/s41417-021-00306-8\u003c/li\u003e\n\u003cli\u003eBoone JD, Arend RC, Johnston BE, Cooper SJ, Gilchrist SA, Oelschlager DK, Grizzle WE, McGwin G Jr, Gangrade A, Straughn JM Jr, et al. Targeting the Wnt/\u0026beta;-catenin pathway in primary ovarian cancer with the porcupine inhibitor WNT974. Lab Invest. 2016;96(2):249-259. doi:10.1038/labinvest.2015.150\u003c/li\u003e\n\u003cli\u003eBotteri E, Borroni E, Sloan EK, Bagnardi V, Bosetti C, Peveri G, Santucci C, Specchia C, van den Brandt P, Gallus S. Serine protease inhibitor E2 protects against cartilage tissue destruction and inflammation in osteoarthritis by targeting NF-\u0026kappa;B signalling. Rheumatology (Oxford). 2024;63(11):3172-3183. doi:10.1093/rheumatology/keae452\u003c/li\u003e\n\u003cli\u003eBrilhante AV, Augusto KL, Portela MC, Sucupira LC, Oliveira LA, Pouchaim AJ, N\u0026oacute;brega LR, Magalh\u0026atilde;es TF, Sobreira LR. Endometriosis and Ovarian Cancer: an Integrative Review (Endometriosis and Ovarian Cancer). Asian Pac J Cancer Prev. 2017;18(1):11-16. doi:10.22034/APJCP.2017.18.1.11\u003c/li\u003e\n\u003cli\u003eCartwright T, Perkins ND, L Wilson C. NFKB1: a suppressor of inflammation, ageing and cancer. FEBS J. 2016;283(10):1812-1822. doi:10.1111/febs.13627\u003c/li\u003e\n\u003cli\u003eChen LM, Wang W, Lee JC, Chiu FH, Wu CT, Tai CJ, Wang CK, Tai CJ, Huang MT, Chang YJ. Thrombomodulin mediates the progression of epithelial ovarian cancer cells. Tumour Biol. 2013;34(6):3743-3751. doi:10.1007/s13277-013-0958-x\u003c/li\u003e\n\u003cli\u003eChen S, Zhang H, Li H. MiR-135a is highly expressed and aggravates inflammatory response in sepsis by targeting MYOM1. Acta Biochim Pol. 2022;69(3):587-592. doi:10.18388/abp.2020_5882\u003c/li\u003e\n\u003cli\u003eChoi SY, Choi JH. Ovarian Cancer and the Microbiome: Connecting the Dots for Early Diagnosis and Therapeutic Innovations-A Review. Medicina (Kaunas). 2024;60(3):516. doi:10.3390/medicina60030516\u003c/li\u003e\n\u003cli\u003eClough E, Barrett T. The Gene Expression Omnibus Database. Methods Mol Biol. 2016;1418:93-110. doi:10.1007/978-1-4939-3578-9_5\u003c/li\u003e\n\u003cli\u003eCoward JI, Middleton K, Murphy F. New perspectives on targeted therapy in ovarian cancer. Int J Womens Health. 2015;7:189-203. doi:10.2147/IJWH.S52379\u003c/li\u003e\n\u003cli\u003eCunningham JM, Vierkant RA, Sellers TA, Phelan C, Rider DN, Liebow M, Schildkraut J, Berchuck A, Couch FJ, Wang X, et al. Cell cycle genes and ovarian cancer susceptibility: a tagSNP analysis. Br J Cancer. 2009;101(8):1461-1468. doi:10.1038/sj.bjc.6605284\u003c/li\u003e\n\u003cli\u003eDing DN, Xie LZ, Shen Y, Li J, Guo Y, Fu Y, Liu FY, Han FJ. Insights into the Role of Oxidative Stress in Ovarian Cancer. Oxid Med Cell Longev. 2021;2021:8388258. doi:10.1155/2021/8388258\u003c/li\u003e\n\u003cli\u003eDruso JE, MacPherson MB, Chia SB, Elko E, Aboushousha R, Seward DJ, Abdelhamid H, Erickson C, Corteselli E, Tarte M, et al. Endoplasmic Reticulum Oxidative Stress Promotes Glutathione-Dependent Oxidation of Collagen-1A1 and Promotes Lung Fibroblast Activation. Am J Respir Cell Mol Biol. 2024;71(5):589-602. doi:10.1165/rcmb.2023-0379OC\u003c/li\u003e\n\u003cli\u003eEckhoff K, Flursch\u0026uuml;tz R, Trillsch F, Mahner S, J\u0026auml;nicke F, Milde-Langosch K. The prognostic significance of Jun transcription factors in ovarian cancer. J Cancer Res Clin Oncol. 2013;139(10):1673-1680. doi:10.1007/s00432-013-1489-y \u003c/li\u003e\n\u003cli\u003eElmasri WM, Casagrande G, Hoskins E, Kimm D, Kohn EC. Cell adhesion in ovarian cancer. Cancer Treat Res. 2009;149:297-318. doi:10.1007/978-0-387-98094-2_14\u003c/li\u003e\n\u003cli\u003eErceylan \u0026Ouml;F, Savaş A, G\u0026ouml;v E. Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer. Turk J Biol. 2021;45(2):127-137. doi:10.3906/biy-2010-39\u003c/li\u003e\n\u003cli\u003eFabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B et al The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649\u0026ndash;D655. doi:10.1093/nar/gkx1132\u003c/li\u003e\n\u003cli\u003eFan Y, Xia J. miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol Biol. 2018; 1819:215-233. doi:10.1007/978-1-4939-8618-7_10\u003c/li\u003e\n\u003cli\u003eFang SY, Zhang XM, Chen XP. Biglycan promotes proliferation and metastasis of ovarian cancer. Int J Clin Exp Pathol. 2025;18(4):166-172. doi:10.62347/DOZK6884\u003c/li\u003e\n\u003cli\u003eGasparri ML, Bardhi E, Ruscito I, Papadia A, Farooqi AA, Marchetti C, Bogani G, Ceccacci I, Mueller MD, Benedetti Panici P. PI3K/AKT/mTOR Pathway in Ovarian Cancer Treatment: Are We on the Right Track?. Geburtshilfe Frauenheilkd. 2017;77(10):1095-1103. doi:10.1055/s-0043-118907 \u003c/li\u003e\n\u003cli\u003eGeng Q, Xu J, Cao X, Wang Z, Jiao Y, Diao W, Wang X, Wang Z, Zhang M, Zhao L, et al. PPARG-mediated autophagy activation alleviates inflammation in rheumatoid arthritis. J Autoimmun. 2024;146:103214. doi:10.1016/j.jaut.2024.103214\u003c/li\u003e\n\u003cli\u003eGesta S, Guntur K, Majumdar ID, Akella S, Vishnudas VK, Sarangarajan R, Narain NR. Reduced expression of collagen VI alpha 3 (COL6A3) confers resistance to inflammation-induced MCP1 expression in adipocytes. Obesity (Silver Spring). 2016;24(8):1695-1703. doi:10.1002/oby.21565\u003c/li\u003e\n\u003cli\u003eGilbert M, Li Z, Wu XN, Rohr L, Gombos S, Harter K, Schulze WX. Comparison of path-based centrality measures in protein-protein interaction networks revealed proteins with phenotypic relevance during adaptation to changing nitrogen environments. J Proteomics. 2021;235:104114. doi:10.1016/j.jprot.2021.104114\u003c/li\u003e\n\u003cli\u003eGreen GH, Diggle PJ. On the operational characteristics of the Benjamini and Hochberg False Discovery Rate procedure. Stat Appl Genet Mol Biol. 2007;6:Article27. doi:10.2202/1544-6115.1302\u003c/li\u003e\n\u003cli\u003eGuo D, Zhang W, Yang H, Bi J, Xie Y, Cheng B, Wang Y, Chen S. Celastrol Induces Necroptosis and Ameliorates Inflammation via Targeting Biglycan in Human Gastric Carcinoma. Int J Mol Sci. 2019;20(22):5716. doi:10.3390/ijms20225716\u003c/li\u003e\n\u003cli\u003eGuo Y, Liu Z, Wang M. NFKB1-mediated downregulation of microRNA-106a promotes oxidative stress injury and insulin resistance in mice with gestational hypertension. Cytotechnology. 2021;73(1):115-126. doi:10.1007/s10616-020-00448-x\u003c/li\u003e\n\u003cli\u003eHendrikse CSE, Theelen PMM, van der Ploeg P, Westgeest HM, Boere IA, Thijs AMJ, Ottevanger PB, van de Stolpe A, Lambrechts S, Bekkers RLM, et al. The potential of RAS/RAF/MEK/ERK (MAPK) signaling pathway inhibitors in ovarian cancer: A systematic review and meta-analysis. Gynecol Oncol. 2023;171:83-94. doi:10.1016/j.ygyno.2023.01.038\u003c/li\u003e\n\u003cli\u003eHo CM, Yen TL, Chang TH, Huang SH. COL6A3 Exosomes Promote Tumor Dissemination and Metastasis in Epithelial Ovarian Cancer. Int J Mol Sci. 2024;25(15):8121. doi:10.3390/ijms25158121\u003c/li\u003e\n\u003cli\u003eHollis RL, Gourley C. Genetic and molecular changes in ovarian cancer. Cancer Biol Med. 2016;13(2):236-247. doi:10.20892/j.issn.2095-3941.2016.0024\u003c/li\u003e\n\u003cli\u003eHuang D, Chen D, Hu T, Liang H. GATA2 promotes oxidative stress to aggravate renal ischemia-reperfusion injury by up-regulating Redd1. Mol Immunol. 2023;153:75-84. doi:10.1016/j.molimm.2022.09.012\u003c/li\u003e\n\u003cli\u003eHuang Y, Wang Y, Ouyang Y. Elevated microRNA-135b-5p relieves neuronal injury and inflammation in post-stroke cognitive impairment by targeting NR3C2. Int J Neurosci. 2022;132(1):58-66. doi:10.1080/00207454.2020.1802265\u003c/li\u003e\n\u003cli\u003eHuang Z, Yao H, Yang Z. Prognostic significance of TM4SF1 and DDR1 expression in epithelial ovarian cancer. Oncol Lett. 2023;26(4):448. doi:10.3892/ol.2023.14035\u003c/li\u003e\n\u003cli\u003eJoshi H, Vastrad B, Vastrad C. Identification of Important Invasion-Related Genes in Non-functional Pituitary Adenomas. J Mol Neurosci. 2019;68(4):565-589. doi:10.1007/s12031-019-01318-8\u003c/li\u003e\n\u003cli\u003eKim MJ, Lee SJ, Ryu JH, Kim SH, Kwon IC, Roberts TM. Combination of KRAS gene silencing and PI3K inhibition for ovarian cancer treatment. J Control Release. 2020;318:98-108. doi:10.1016/j.jconrel.2019.12.019\u003c/li\u003e\n\u003cli\u003eKolasa IK, Rembiszewska A, Felisiak A, Ziolkowska-Seta I, Murawska M, Moes J, Timorek A, Dansonka-Mieszkowska A, Kupryjanczyk J. PIK3CA amplification associates with resistance to chemotherapy in ovarian cancer patients. Cancer Biol Ther. 2009;8(1):21-26. doi:10.4161/cbt.8.1.7209\u003c/li\u003e\n\u003cli\u003eKoussounadis A, Langdon SP, Harrison DJ, Smith VA. Chemotherapy-induced dynamic gene expression changes in vivo are prognostic in ovarian cancer. Br J Cancer. 2014;110(12):2975-2984. doi:10.1038/bjc.2014.258\u003c/li\u003e\n\u003cli\u003eKuroda Y, Chiyoda T, Kawaida M, Nakamura K, Aimono E, Yoshimura T, Takahashi M, Saotome K, Yoshihama T, Iwasa N, et al. ARID1A mutation/ARID1A loss is associated with a high immunogenic profile in clear cell ovarian cancer. Gynecol Oncol. 2021;162(3):679-685. doi:10.1016/j.ygyno.2021.07.005\u003c/li\u003e\n\u003cli\u003eLeizer AL, Alvero AB, Fu HH, Holmberg JC, Cheng YC, Silasi DA, Rutherford T, Mor G. Regulation of inflammation by the NF-\u0026kappa;B pathway in ovarian cancer stem cells. Am J Reprod Immunol. 2011;65(4):438-447. doi:10.1111/j.1600-0897.2010.00914.x\u003c/li\u003e\n\u003cli\u003eLi C, Zhang S, Chen X, Ji J, Yang W, Gui T, Gai Z, Li Y. Farnesoid X receptor activation inhibits TGFBR1/TAK1-mediated vascular inflammation and calcification via miR-135a-5p. Commun Biol. 2020;3(1):327. doi:10.1038/s42003-020-1058-2\u003c/li\u003e\n\u003cli\u003eLi G, Li M, Wang J, Li Y, Pan Y. United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins. IEEE/ACM Trans Comput Biol Bioinform. 2020;17(4):1451-1458. doi:10.1109/TCBB.2018.2889978\u003c/li\u003e\n\u003cli\u003eLi H, Wu N, Liu ZY, Chen YC, Cheng Q, Wang J. Development of a novel transcription factors-related prognostic signature for serous ovarian cancer. Sci Rep. 2021;11(1):7207. doi:10.1038/s41598-021-86294-z\u003c/li\u003e\n\u003cli\u003eLi T, Zhang H, Lian M, He Q, Lv M, Zhai L, Zhou J, Wu K, Yi M. Global status and attributable risk factors of breast, cervical, ovarian, and uterine cancers from 1990 to 2021. J Hematol Oncol. 2025;18(1):5. doi:10.1186/s13045-025-01660-y\u003c/li\u003e\n\u003cli\u003eLi Y, Li W, Tan Y, Liu F, Cao Y, Lee KY. Hierarchical Decomposition for Betweenness Centrality Measure of Complex Networks. Sci Rep. 2017;7:46491.. doi:10.1038/srep46491\u003c/li\u003e\n\u003cli\u003eLi Y, Zhang J, Dai Y, Fan Y, Xu J. Novel Mutations in COL6A3 That Associated With Peters\u0026apos; Anomaly Caused Abnormal Intracellular Protein Retention and Decreased Cellular Resistance to Oxidative Stress. Front Cell Dev Biol. 2020;8:531986. doi:10.3389/fcell.2020.531986\u003c/li\u003e\n\u003cli\u003eLiu S, Liu J, Yang X, Jiang M, Wang Q, Zhang L, Ma Y, Shen Z, Tian Z, Cao X. Cis-acting lnc-Cxcl2 restrains neutrophil-mediated lung inflammation by inhibiting epithelial cell CXCL2 expression in virus infection. Proc Natl Acad Sci U S A. 2021;118(41):e2108276118. doi:10.1073/pnas.2108276118\u003c/li\u003e\n\u003cli\u003eLiu T, Zhao L, Chen W, Li Z, Hou H, Ding L, Li X. Inactivation of von Hippel-Lindau increases ovarian cancer cell aggressiveness through the HIF1\u0026alpha;/miR-210/VMP1 signaling pathway. Int J Mol Med. 2014;33(5):1236-1242. doi:10.3892/ijmm.2014.1661\u003c/li\u003e\n\u003cli\u003eLiu Y, Wang Z, Xie W, Gu Z, Xu Q, Su L. Oxidative stress regulates mitogen‑activated protein kinases and c‑Jun activation involved in heat stress and lipopolysaccharide‑induced intestinal epithelial cell apoptosis. Mol Med Rep. 2017;16(3):2579-2587. doi:10.3892/mmr.2017.6859\u003c/li\u003e\n\u003cli\u003eLiu Z, Zhang TT, Zhao JJ, Qi SF, Du P, Liu DW, Tian QB. The association between overweight, obesity and ovarian cancer: a meta-analysis. Jpn J Clin Oncol. 2015;45(12):1107-1115. doi:10.1093/jjco/hyv150\u003c/li\u003e\n\u003cli\u003eLloyd KL, Cree IA, Savage RS. Prediction of resistance to chemotherapy in ovarian cancer: a systematic review. BMC Cancer. 2015;15:117. doi:10.1186/s12885-015-1101-8\u003c/li\u003e\n\u003cli\u003eLuo S, Wang J, Ma Y, Yao Z, Pan H. PPAR\u0026gamma; inhibits ovarian cancer cells proliferation through upregulation of miR-125b. Biochem Biophys Res Commun. 2015;462(2):85-90. doi:10.1016/j.bbrc.2015.04.023\u003c/li\u003e\n\u003cli\u003eLuo X, Guo L, Dai XJ, Wang Q, Zhu W, Miao X, Gong H. Abnormal intrinsic functional hubs in alcohol dependence: evidence from a voxelwise degree centrality analysis. Neuropsychiatr Dis Treat. 2017;13:2011-2020. doi:10.2147/NDT.S142742\u003c/li\u003e\n\u003cli\u003eMaas HA, Kruitwagen RF, Lemmens VE, Goey SH, Janssen-Heijnen ML. The influence of age and co-morbidity on treatment and prognosis of ovarian cancer: a population-based study. Gynecol Oncol. 2005;97(1):104-109. doi:10.1016/j.ygyno.2004.12.026\u003c/li\u003e\n\u003cli\u003eMahoney DE, Pierce JD. Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and Management. Clin J Oncol Nurs. 2022;26(5):533-542. doi:10.1188/22.CJON.533-542\u003c/li\u003e\n\u003cli\u003eMartins FC, Couturier DL, Paterson A, Karnezis AN, Chow C, Nazeran TM, Odunsi A, Gentry-Maharaj A, Vrvilo A, Hein A, Talhouk A, et al. Clinical and pathological associations of PTEN expression in ovarian cancer: a multicentre study from the Ovarian Tumour Tissue Analysis Consortium. Br J Cancer. 2020;123(5):793-802. doi:10.1038/s41416-020-0900-0\u003c/li\u003e\n\u003cli\u003eNagasawa S, Ikeda K, Shintani D, Yang C, Takeda S, Hasegawa K, Horie K, Inoue S. Identification of a Novel Oncogenic Fusion Gene SPON1-TRIM29 in Clinical Ovarian Cancer That Promotes Cell and Tumor Growth and Enhances Chemoresistance in A2780 Cells. Int J Mol Sci. 2022;23(2):689. doi:10.3390/ijms23020689\u003c/li\u003e\n\u003cli\u003eNick AM, Coleman RL, Ramirez PT, Sood AK. A framework for a personalized surgical approach to ovarian cancer. Nat Rev Clin Oncol. 2015;12(4):239-245. doi:10.1038/nrclinonc.2015.26\u003c/li\u003e\n\u003cli\u003eOkuno Y, Fukuhara A, Hashimoto E, Kobayashi H, Kobayashi S, Otsuki M, Shimomura I. Oxidative Stress Inhibits Healthy Adipose Expansion Through Suppression of SREBF1-Mediated Lipogenic Pathway. Diabetes. 2018;67(6):1113-1127. doi:10.2337/db17-1032\u003c/li\u003e\n\u003cli\u003ePan X, Yang L, Wang S, Liu Y, Yue L, Chen S. Semaglutide ameliorates obesity-induced cardiac inflammation and oxidative stress mediated via reduction of neutrophil Cxcl2, S100a8, and S100a9 expression. Mol Cell Biochem. 2024;479(5):1133-1147. doi:10.1007/s11010-023-04784-2\u003c/li\u003e\n\u003cli\u003ePapakonstantinou E, Androutsopoulos G, Logotheti S, Adonakis G, Maroulis I, Tzelepi V. DNA Methylation in Epithelial Ovarian Cancer: Current Data and Future Perspectives. Curr Mol Pharmacol. 2021;14(6):1013-1027. doi:10.2174/1874467213666200810141858\u003c/li\u003e\n\u003cli\u003ePloeger C, Schreck J, Huth T, Fraas A, Albrecht T, Charbel A, Ji J, Singer S, Breuhahn K, Pusch S, et al. STAT1 and STAT3 Exhibit a Crosstalk and Are Associated with Increased Inflammation in Hepatocellular Carcinoma. Cancers (Basel). 2022;14(5):1154. doi:10.3390/cancers14051154\u003c/li\u003e\n\u003cli\u003ePorras P, Barrera E, Bridge A, Del-Toro N, Cesareni G, Duesbury M, Hermjakob H, Iannuccelli M, Jurisica I, Kotlyar M, et al. Towards a unified open access dataset of molecular interactions. Nat Commun. 2020;11(1):6144. doi:10.1038/s41467-020-19942-z\u003c/li\u003e\n\u003cli\u003ePuttock EH, Tyler EJ, Manni M, Maniati E, Butterworth C, Burger Ramos M, Peerani E, Hirani P, Gauthier V, Liu Y, et al. Extracellular matrix educates an immunoregulatory tumor macrophage phenotype found in ovarian cancer metastasis. Nat Commun. 2023;14(1):2514. doi:10.1038/s41467-023-38093-5\u003c/li\u003e\n\u003cli\u003eRafii A, Halabi NM, Malek JA. High-prevalence and broad spectrum of Cell Adhesion and Extracellular Matrix gene pathway mutations in epithelial ovarian cancer. J Clin Bioinforma. 2012;2(1):15. doi:10.1186/2043-9113-2-15\u003c/li\u003e\n\u003cli\u003eReimand J, Kull M, Peterson H, Hansen J, Vilo J. g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007;35(Web Server issue):W193-W200. doi:10.1093/nar/gkm226\u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:10.1093/nar/gkv007\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, M\u0026uuml;ller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77. doi:10.1186/1471-2105-12-77\u003c/li\u003e\n\u003cli\u003eSaed GM. Is there a link between talcum powder, oxidative stress, and ovarian cancer risk?. Expert Rev Anticancer Ther. 2024;24(7):485-491. doi:10.1080/14737140.2024.2352506\u003c/li\u003e\n\u003cli\u003eSaijo H, Hirohashi Y, Torigoe T, Horibe R, Takaya A, Murai A, Kubo T, Kajiwara T, Tanaka T, Shionoya Y, et al. Plasticity of lung cancer stem-like cells is regulated by the transcription factor HOXA5 that is induced by oxidative stress. Oncotarget. 2016;7(31):50043-50056. doi:10.18632/oncotarget.10571\u003c/li\u003e\n\u003cli\u003eSavant SS, Sriramkumar S, O\u0026apos;Hagan HM. The Role of Inflammation and Inflammatory Mediators in the Development, Progression, Metastasis, and Chemoresistance of Epithelial Ovarian Cancer. Cancers (Basel). 2018;10(8):251. doi:10.3390/cancers10080251 \u003c/li\u003e\n\u003cli\u003eSchonthaler HB, Guinea-Viniegra J, Wagner EF. Targeting inflammation by modulating the Jun/AP-1 pathway. Ann Rheum Dis. 2011;70 Suppl 1:i109-i112. doi:10.1136/ard.2010.140533\u003c/li\u003e\n\u003cli\u003eShi W, Sun H, Yao Q, Liu H, Zhang L, Han W. Phellodendrine ameliorates intestinal inflammation and protects mucosal barrier via modulating COL1A1, VCAM1 and IL-17 a. Int Immunopharmacol. 2025;152:114403. doi:10.1016/j.intimp.2025.114403\u003c/li\u003e\n\u003cli\u003eSiminiak N, Czepczyński R, Zaborowski MP, Iżycki D. Immunotherapy in Ovarian Cancer. Arch Immunol Ther Exp (Warsz). 2022;70(1):19. doi:10.1007/s00005-022-00655-8\u003c/li\u003e\n\u003cli\u003eSimpkins F, Garcia-Soto A, Slingerland J. New insights on the role of hormonal therapy in ovarian cancer. Steroids. 2013;78(6):530-537. doi:10.1016/j.steroids.2013.01.008\u003c/li\u003e\n\u003cli\u003eSon ES, Ko UW, Jeong HY, Park SY, Lee YE, Park JW, Jeong SH, Kim SH, Kyung SY. miRNA-6515-5p regulates particulate matter-induced inflammatory responses by targeting CSF3 in human bronchial epithelial cells. Toxicol In Vitro. 2022;84:105428. doi:10.1016/j.tiv.2022.105428\u003c/li\u003e\n\u003cli\u003eSu J, Ruan S, Dai S, Mi J, Chen W, Jiang S. NF1 regulates apoptosis in ovarian cancer cells by targeting MCL1 via miR-142-5p. Pharmacogenomics. 2019;20(3):155-165. doi:10.2217/pgs-2018-0161\u003c/li\u003e\n\u003cli\u003eSzabados T, Moln\u0026aacute;r A, Kenyeres \u0026Eacute;, G\u0026ouml;m\u0026ouml;ri K, Pipis J, P\u0026oacute;sa B, Makkos A, \u0026Aacute;gg B, Giricz Z, Ferdinandy P, et al. Identification of New, Translatable ProtectomiRs against Myocardial Ischemia/Reperfusion Injury and Oxidative Stress: The Role of MMP/Biglycan Signaling Pathways. Antioxidants (Basel). 2024;13(6):674. doi:10.3390/antiox13060674\u003c/li\u003e\n\u003cli\u003eTakamizawa S, Kojima J, Umezu T, Kuroda M, Hayashi S, Maruta T, Okamoto A, Nishi H. miR‑146a‑5p and miR‑191‑5p as novel diagnostic marker candidates for ovarian clear cell carcinoma. Mol Clin Oncol. 2023;20(2):14. doi:10.3892/mco.2023.2712\u003c/li\u003e\n\u003cli\u003eThomas PD. The Gene Ontology and the Meaning of Biological Function. Methods Mol Biol. 2017;1446:15‐24. doi:10.1007/978-1-4939-3743-1_2\u003c/li\u003e\n\u003cli\u003eTotten SP, Im YK, Cepeda Ca\u0026ntilde;edo E, Najyb O, Nguyen A, H\u0026eacute;bert S, Ahn R, Lewis K, Lebeau B, La Selva R, et al. STAT1 potentiates oxidative stress revealing a targetable vulnerability that increases phenformin efficacy in breast cancer. Nat Commun. 2021;12(1):3299. doi:10.1038/s41467-021-23396-2\u003c/li\u003e\n\u003cli\u003eVastrad B, Vastrad C, Tengli A, Iliger S. Identification of differentially expressed genes regulated by molecular signature in breast cancer-associated fibroblasts by bioinformatics analysis. Arch Gynecol Obstet. 2018;297(1):161-183. doi:10.1007/s00404-017-4562-y\u003c/li\u003e\n\u003cli\u003eWalsh CS. Latest clinical evidence of maintenance therapy in ovarian cancer. Curr Opin Obstet Gynecol. 2020;32(1):15-21. doi:10.1097/GCO.0000000000000592\u003c/li\u003e\n\u003cli\u003eWang F, Niu Y, Chen K, Yuan X, Qin Y, Zheng F, Cui Z, Lu W, Wu Y, Xia D. Extracellular Vesicle-Packaged circATP2B4 Mediates M2 Macrophage Polarization via miR-532-3p/SREBF1 Axis to Promote Epithelial Ovarian Cancer Metastasis. Cancer Immunol Res. 2023;11(2):199-216. doi:10.1158/2326-6066.CIR-22-0410\u003c/li\u003e\n\u003cli\u003eWang T, Townsend MK, Simmons V, Terry KL, Matulonis UA, Tworoger SS. Prediagnosis and postdiagnosis smoking and survival following diagnosis with ovarian cancer. Int J Cancer. 2020;147(3):736-746. doi:10.1002/ijc.32773\u003c/li\u003e\n\u003cli\u003eWang X, Zhu M, Loor JJ, Jiang Q, Zhu Y, Li W, Du X, Song Y, Gao W, Lei L, et al. Propionate alleviates fatty acid-induced mitochondrial dysfunction, oxidative stress, and apoptosis by upregulating PPARG coactivator 1 alpha in hepatocytes. J Dairy Sci. 2022;105(5):4581-4592. doi:10.3168/jds.2021-21198\u003c/li\u003e\n\u003cli\u003eWebb PM, Jordan SJ. Epidemiology of epithelial ovarian cancer. Best Pract Res Clin Obstet Gynaecol. 2017;41:3-14. doi:10.1016/j.bpobgyn.2016.08.006\u003c/li\u003e\n\u003cli\u003eWight TN, Kang I, Evanko SP, Harten IA, Chang MY, Pearce OMT, Allen CE, Frevert CW. Versican-A Critical Extracellular Matrix Regulator of Immunity and Inflammation. Front Immunol. 2020;11:512. doi:10.3389/fimmu.2020.00512\u003c/li\u003e\n\u003cli\u003eWu Y, Wu J, Lee DY, Yee A, Cao L, Zhang Y, Kiani C, Yang BB. Versican protects cells from oxidative stress-induced apoptosis. Matrix Biol. 2005;24(1):3-13. doi:10.1016/j.matbio.2004.11.007\u003c/li\u003e\n\u003cli\u003eXiao X, Long F, Yu S, Wu W, Nie D, Ren X, Li W, Wang X, Yu L, Wang P, et al. Col1A1 as a new decoder of clinical features and immune microenvironment in ovarian cancer. Front Immunol. 2025;15:1496090. doi:10.3389/fimmu.2024.1496090\u003c/li\u003e\n\u003cli\u003eXu J, Zhou K, Gu H, Zhang Y, Wu L, Bian C, Huang Z, Chen G, Cheng X, Yin X. Exosome miR-4738-3p-mediated regulation of COL1A2 through the NF-\u0026kappa;B and inflammation signaling pathway alleviates osteoarthritis low-grade inflammation symptoms. Biomol Biomed. 2024;24(3):520-536. doi:10.17305/bb.2023.9921\u003c/li\u003e\n\u003cli\u003eYang B, Yin S, Zhou Z, Huang L, Xi M. Inflammation Control and Tumor Growth Inhibition of Ovarian Cancer by Targeting Adhesion Molecules of E-Selectin. Cancers (Basel). 2023;15(7):2136. doi:10.3390/cancers15072136\u003c/li\u003e\n\u003cli\u003eYang J, He R, Zhang X, Wang X, Liu M, Liu X, Li Y. KLC3 activates PI3K/AKT signaling and promotes ovarian cancer cell proliferation and migration through COL3A1. Oncol Rep. 2025;53(6):67. doi:10.3892/or.2025.8900\u003c/li\u003e\n\u003cli\u003eYang SM, Ka SM, Wu HL, Yeh YC, Kuo CH, Hua KF, Shi GY, Hung YJ, Hsiao FC, Yang SS, et al. Thrombomodulin domain 1 ameliorates diabetic nephropathy in mice via anti-NF-\u0026kappa;B/NLRP3 inflammasome-mediated inflammation, enhancement of NRF2 antioxidant activity and inhibition of apoptosis. Diabetologia. 2014;57(2):424-434. doi:10.1007/s00125-013-3115-6\u003c/li\u003e\n\u003cli\u003eYin X, Wang X, Wang S, Xia Y, Chen H, Yin L, Hu K. Screening for Regulatory Network of miRNA-Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study. Int J Gen Med. 2022;15:1715-1731. doi:10.2147/IJGM.S354359\u003c/li\u003e\n\u003cli\u003eYu X, Zhao P, Luo Q, Wu X, Wang Y, Nan Y, Liu S, Gao W, Li B, Liu Z, et al. RUNX1-IT1 acts as a scaffold of STAT1 and NuRD complex to promote ROS-mediated NF-\u0026kappa;B activation and ovarian cancer progression. Oncogene. 2024;43(6):420-433. doi:10.1038/s41388-023-02910-4\u003c/li\u003e\n\u003cli\u003eZhang F, Jiang J, Xu B, Xu Y, Wu C. Over-expression of CXCL2 is associated with poor prognosis in patients with ovarian cancer. Medicine (Baltimore). 2021;100(4):e24125. doi:10.1097/MD.0000000000024125\u003c/li\u003e\n\u003cli\u003eZhang J, Yang X, Zong Y, Yu T, Yang X. miR-196b-5p regulates inflammatory process and migration via targeting Nras in trabecular meshwork cells. Int Immunopharmacol. 2024;129:111646. doi:10.1016/j.intimp.2024.111646\u003c/li\u003e\n\u003cli\u003eZhang J, Zhang S, Xu S, Zhu Z, Li J, Wang Z, Wada Y, Gatt A, Liu J. Oxidative Stress Induces E-Selectin Expression through Repression of Endothelial Transcription Factor ERG. J Immunol. 2023;211(12):1835-1843. doi:10.4049/jimmunol.2300043\u003c/li\u003e\n\u003cli\u003eZhang L, Zhang L, Li S, Zhang Q, Luo Y, Zhang C, Huan Q, Zhang C. Overexpression of mm9_circ_013935 alleviates renal inflammation and fibrosis in diabetic nephropathy via the miR-153-3p/NFIC axis. Can J Physiol Pharmacol. 2021;99(11):1199-1206. doi:10.1139/cjpp-2021-0187\u003c/li\u003e\n\u003cli\u003eZhao H, Yu H, Zheng J, Ning N, Tang F, Yang Y, Wang Y. Lowly-expressed lncRNA GAS5 facilitates progression of ovarian cancer through targeting miR-196-5p and thereby regulating HOXA5. Gynecol Oncol. 2018;151(2):345-355. doi:10.1016/j.ygyno.2018.08.032\u003c/li\u003e\n\u003cli\u003eZhao L, Liang X, Wang L, Zhang X. The Role of miRNA in Ovarian Cancer: an Overview. Reprod Sci. 2022;29(10):2760-2767. doi:10.1007/s43032-021-00717-w\u003c/li\u003e\n\u003cli\u003eZhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234-W241. doi:10.1093/nar/gkz240 \u003c/li\u003e\n\u003cli\u003eZhou L, Cui M, Yu J, Liu Y, Zeng F, Liu Y. 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\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: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elogFC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003etvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.705479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6.66E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e8.005357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.984305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.25E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e7.612776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eSFRP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.481883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.95E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.929767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.592584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8.62E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.485731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.043548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.01E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.39482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\u003e\n \u003cp\u003eversican\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSPON1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.943975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.06E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.368547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eBGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3.053935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.11E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.344523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\u003e\n \u003cp\u003ebiglycan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCOL6A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.414595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.15E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.326491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eRUNX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.75519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.16E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.321531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.992717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.22E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.293766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eTUG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.985688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.94E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.039396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eNT5DC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.970153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.97E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e6.03119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003ePRRX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.02811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.47E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.728486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eCOL16A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.056656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5.63E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.473747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eC4B_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.488349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6.95E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.36537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eSEPT8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.104221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e7.31E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.339117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.069188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e7.35E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.336208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eSTAB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.215354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e7.97E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.294708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eVMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.212453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8.46E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e5.263772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eGPM6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.22974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.16E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-9.8873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.50643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8.12E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-7.8812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eGLYAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.23944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8.81E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-7.83062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eC2orf40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-2.23128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9.26E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-7.79952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.23194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.65E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-7.4467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eSTXBP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.57584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.85E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-7.37721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eDEFB132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.29844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.57E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-7.18026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eLOC101926960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.54525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.10E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-7.07109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eC8orf34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.17111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.80E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-6.81691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eKANK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.52627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6.54E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-6.64089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eTRDN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.66658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8.50E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-6.49338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\u003e\n \u003cp\u003etriadin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-2.9814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.00E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-6.02196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.3474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e3.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-5.66618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eCSF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.35389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.09E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-5.64129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-3.58351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4.85E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-5.55206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6.85E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-5.37288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\u003e\n \u003cp\u003ethrombomodulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eLINC00968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.98628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6.99E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-5.36194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eLRRN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.40976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e7.79E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-5.30646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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: 113px;\"\u003e\n \u003cp\u003eMAMDC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-2.3466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8.19E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-5.28032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 403px;\"\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\u003cstrong\u003eTable 2\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\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCATEGORY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eadjusted_p_value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\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: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\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: 73px;\"\u003e\n \u003cp\u003eGO:0007155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ecell adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000129946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.886238721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSERPINE2,COL1A1,SFRP2,VCAN,SPON1,COL6A3,\u003cbr\u003eLOC100506403,COL3A1,COL16A1,STAB1,VMP1,STXBP6,SELE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eGO:0009617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eresponse to bacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000129946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.886238721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eCOL1A1,SFRP2,COL1A2,VCAN,BGN,LOC100506403,\u003cbr\u003eCOL3A1,PRRX1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eGO:0071944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ecell periphery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.000687742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.162574496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSERPINE2,COL1A1,SFRP2,COL1A2,VCAN,SPON1,\u003cbr\u003eBGN,COL6A3,COL3A1,COL16A1,C4A,F2R,STAB1,\u003cbr\u003eVMP1,GPM6A,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: 73px;\"\u003e\n \u003cp\u003eGO:0012505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eendomembrane system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.003307269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.480530528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSERPINE2,COL1A1,COL1A2,VCAN,SPON1,\u003cbr\u003eBGN,COL6A3,COL3A1,TUG1,COL16A1,C4A,F2R,\u003cbr\u003eVMP1,TRDN,NR3C2,SELE,MAMDC2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eGO:0047962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eglycine N-benzoyltransferase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.017435608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.758562895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eGLYAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eGO:0071837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHMG box domain binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.11769815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.929230363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\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\u003cstrong\u003eTable 3\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: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\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: 132px;\"\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: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\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: 121px;\"\u003e\n \u003cp\u003eREAC:R-HSA-1474244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eExtracellular matrix organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4.05332E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.392188608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eCOL1A1,COL1A2,VCAN,BGN,COL6A3,COL3A1,COL16A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eREAC:R-HSA-140875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCommon Pathway of Fibrin Clot Formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000174191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.758973492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\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\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e 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: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetweenness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.393488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e32896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.347574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e33052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.198743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e27946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.172345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e16394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.097264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.03531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eBGN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNT5DC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.068607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSTAB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.058955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSFRP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCOL16A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.024545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCOL6A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.416667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.204209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e16614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.193212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.097264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.833333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.051972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.032551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCSF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.039502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGLYAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.039502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTRDN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eC8orf34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eKANK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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: 121px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSTXBP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\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 5\u003c/strong\u003e MiRNA - hub gene and TF \u0026ndash; hub gene topology table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHub Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHub Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\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: 79px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-6515-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFOXL1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-518a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSERPINE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-146a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eVCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eNFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-24-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSREBF1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-3913-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eEGR1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-6838-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eHOXA5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-135a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eJUN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-196b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eTHBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eUSF2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCXCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eGATA2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eMYOM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-151a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eNFIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ehsa-miR-4652-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNR3C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\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 6\u003c/strong\u003e Drug- hub gene topology table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"632\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\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: 85px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eF2R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003e\n \u003cp\u003eVorapaxar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eCOL1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003e\n \u003cp\u003eHalofuginone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eCOL1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\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: 85px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003eCOL3A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 423px;\"\u003e\n \u003cp\u003eCollagenase clostridium histolyticum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"KLE College of Pharamacy, Gadag 582101, Karanataka, India","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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)","lastPublishedDoi":"10.21203/rs.3.rs-7090018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7090018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOvarian 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. Finally, receiver operating characteristic (ROC) curve analysis was performed for hub genes. 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, \u0026nbsp;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. This investigation could serve as a basis for further understanding the \u0026nbsp;molecular pathogenesis and potential therapeutic targets of ovarian cancer.\u003c/p\u003e","manuscriptTitle":"Integrated bioinformatics analysis reveals key candidate genes, signaling pathways and therapeutic molecules in ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 05:32:45","doi":"10.21203/rs.3.rs-7090018/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 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51321964,"name":"Bioinformatics"},{"id":51321965,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2025-07-11T05:32:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 05:32:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7090018","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7090018","identity":"rs-7090018","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}