Discovering common pathogenetic processes between SARS-CoV-2 infection and polycystic ovary syndrome based on an integrated bioinformatics approach and experiment validation

Discovering common pathogenetic processes between SARS-CoV-2 infection and polycystic ovary syndrome based on an integrated bioinformatics approach and experiment validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Discovering common pathogenetic processes between SARS-CoV-2 infection and polycystic ovary syndrome based on an integrated bioinformatics approach and experiment validation Hai Bai, Shanshan Zhang, Cui Li, Mingming Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4369010/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract The prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among polycystic ovary syndrome (PCOS) is significantly higher than in the general population. However, the mechanisms underlying this remain obscure. This study aimed to explore the mechanisms by identifying genetic signature of SARS-CoV-2 infection in PCOS. In the present study, a total of 27 common differentially expressed genes (DEGs) were selected for subsequent analyses. Functional analyses showed that immunity and hormone related pathways collectively participated in the development and progression of PCOS and SARS Cov-2 infection. Under these, 7 significant hub genes were identified, including S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, CD86 by using the algorithm in Cytoscape. Furthermore, hub genes expression was confirmed in validation set, PCOS clinical samples and mouse model. Immune microenvironment analysis with CIBERSORTx database demonstrated that the hub genes were significantly correlated with T cells, dendritic cells, mast cells, B cells, NK cells, eosinophils and positively correlated with immune scores. Among the hub genes, S100A9, MMP9, THBD, ITGB2, CD86 and ICAM1 exhibited preferable values as diagnostic makers for COVID-19 and PCOS. In addition, we established the interaction networks of ovary-specific genes, transcription factors, miRNAs, drugs, and chemical compounds with hub genes with NetworkAnalyst. This work uncovered the common pathogenesis and genetic signature of PCOS and SARS-CoV-2 infection, which might provide a theoretical basis and innovative ideas for further mechanistic research and drug discovery of the comorbidity of two diseases. Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Biological sciences/Molecular biology Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Signs and symptoms SARS-CoV-2 polycystic ovary syndrome integrated bioinformatics genetic signature immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Recently, several countries and regions have been experiencing outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since late 2019 1 . Infection with SARS-CoV-2 leads to Coronavirus disease 2019 (COVID-19), which can be asymptomatic or presents as acute respiratory distress syndrome 2 . However, in multiple large observations, the virus also readily infected extrapulmonary organs, including the heart, eyes, and kidneys. People with diabetes, obesity and cardiovascular disease are more likely to experience severe symptoms and die from COVID-19 3 . Therefore, preventive strategies are warranted to protect persons with comorbidities from developing severe COVID-19 4 . Men are more prone to contracting COVID-19 than women, possibly because of higher levels of androgen in men and differences in immune response between both sexes 5 . Polycystic ovary syndrome (PCOS), the most common endocrine disorder, affects about 5–15% of women of reproductive age and is characterized by hyperandrogenism, oligoovulation and polycystic ovaries 3 . It was reported that more than 80% of PCOS patients suffer from hyperandrogenemia 6 . In addition, women with PCOS were at risk of long-term complications, including obesity, type 2 diabetes mellitus, nonalcoholic fatty liver disease, and cardiovascular disorders 7 . Additionally, a recent epidemiological study revealed that women with PCOS were remarkably more likely than controls to contract COVID-19 8 . These studies suggest that PCOS patients are at higher risk of COVID-19. Considering PCOS patients with COVID-19 are at an elevated risk for complications, discerning synergistic biomolecular pathways is crucial for identifying therapeutic targets and repurposing medicines for PCOS patients with COVID-19. With the rapid development of sequencing technology, transcriptome analysis has been widely applied to study the pathogenesis of PCOS and COVID-19 9,10 . However, the link between PCOS and vulnerability to COVID-19 remains uncertain. In this study, we used bioinformatics methods to screen common differentially expressed genes (DEGs) of COVID-19 and PCOS, determine their signaling pathways and immune microenvironment, and build the interaction networks with miRNAs, transcript factors, drugs, and chemical compounds. The research may provide new perspectives and strategies for treating PCOS patients infected with SARS-CoV-2. 2. Materials and methods 2.1 Acquisition of datasets Gene Expression Omnibus (GEO) was used to obtain all eligible datasets. We used four datasets for this study, including two PCOS (GSE34526 and GSE10946) and two SARS-CoV-2 datasets (GSE147507 and GSE157103). GSE34526 and GSE147507 were derived from expression profiles using array, while GSE157103 and GSE10946 were derived from expression profiles using high-throughput sequencing. We selected normal and SARS-CoV-2 infected lung epithelial cell NHBE as part of the analysis of the GSE147507 dataset. In addition, 40 female COVID-19 and 12 female non-COVID-19 samples from the GSE157103 dataset were selected for this study. PCOS clinical samples and mouse model were used for the validation of hub genes. Details of the dataset were shown in Table 1 . Table 1 Gene expression profile information Data source Sample information Dataset feature GSE34526 3 normals and 7 PCOS patients Training set GSE10946 11 normals and 12 PCOS patients Validation set GSE147507 3 normals and 3 SARS-Cov-2 infected cells Training set GSE157103 12 normals and 40 COVID-19 patients Validation set Mouse model 6 controls and 6 PCOS mice Validation Clinical samples 20 normals and 20 PCOS patients Validation 2.2 Ethics approval We confirmed that all methods were carried out in accordance with relevant guidelines and regulations, and all methods are reported in accordance with ARRIVE guidelines ( https://arriveguidelines.org ). The clinical samples were from the previous collection and described in a previous publication 11 . The research followed the principles of the Declaration of Helsinki and was approved the local Human Investigation Ethics Committee (license numbers APK.002.9.2020 and APK.002.38.2021). Written informed consent was obtained from all patients before inclusion. All animal experiments were approved by the Ethics Committee for Animal Experimentation of Xuzhou Medical University (license numbers 202209S077). 2.3 Identification of DEGs and enrichment terms The datasets were downloaded from GEO, normalized, and DEGs were extracted with the R package “edgeR” 12 . Fold changes (FCs) were calculated for individual genes. DEGs were defined using a p 1.0. The R language package Venn Diagram was used to obtain shared DEGs 13 . Heatmap and volcano plot were created using an online platform ( https://www.bioinformatics.com.cn ). GO and KEGG enrichment analysis was performed using R, and adjusted p < 0.05 was used to select enriched terms. 2.4 Protein–protein interactions (PPI) network construction The PPI interaction network was constructed by STRING ( http://string-db.org ) 14 and visualized by Cytoscape (version 3.9.1) 15 . Molecular Complex Detection (MCODE) in Cytoscape was used to analyze core functional modules 16 . CytoHubba, a plugin for Cytoscape, was used to select hub genes. Seven algorithms were applied to confirm the final hub genes, which were then visualized with Venn diagrams 17 . Finally, we used GeneMANIA ( www.genemania.org ) to create a co-expression network of hub genes 18 . 2.5 Gene Set Enrichment Analysis (GSEA) analysis We obtained the GSEA software (version 3.0) from the GSEA website and divided the samples into high ( > = 50%) and low (< 50%) expression groups based on gene expression levels 19 . The “cluster Profiler” package was used to perform GSEA on potential mechanisms of c2 (c2. cp.v7.5.1. symbols.gmt) in the Molecular Signature Database (MSigDB) 20 . Pathways with false discovery rates (FDR) less than 0.05 were considered significant. 2.6 Immune microenvironment analysis Datasets GSE34526 and GSE157103 were downloaded from the GEO database for data pooling. In order to obtain individual gene immune scores and expression matrices for 22 immune cells, CIBERSORTx was used to analyze the immune microenvironment of human immune cells with the corresponding genes S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, CD86 21 . The scatter density of each gene was plotted using the “ggplot2” package in R. 2.7 Genes, drugs, and Chemical Compounds-hub genes regulatory networks establishment We established hub gene-miRNA, hub gene-transcription factor (TF), hub gene-drug and hub gene-chemical compound interaction network using NetworkAnalyst 22 . A hub gene-miRNA interaction network was discovered using TarBase and miRTarBase databases. In addition, a TF-DEG interaction network was developed using the JASPAR database 23 . 2.8 PCOS patient sample collection and mouse model built The details of granulosa cells collection and PCOS mouse model built were described in a previous publication 11 . For the collection of granulocytes, in brief, the follicular fluid was centrifuged at 250×g for 10 minutes and the granulosa cells were aspirated and placed in a new centrifuge tube. The cells were washed with PBS and resuspended, and collected by centrifugation again. For the PCOS mouse model, in brief, 3-week-old female C57BL/6J mice were injected subcutaneously with DHEA (6 mg/100 g body weight) or an equivalent dose of sesame oil for 28 consecutive days and their ovarian tissue was collected for assay analysis. 2.9 Hub gene testing and receiver operating characteristic curve (ROC) curve plotting Hub genes testing was described as previously 24 . Briefly, RNA was extracted from tissues or cells using the TRIzol method and reverse transcribed using a cDNA kit. Hub gene expression was detected by qPCR, and glyceraldehyde phosphate dehydrogenase (GAPDH) was used as an internal reference. Primer sequences are listed in Table S1 . Using the R package “pROC”, we constructed ROC curves and calculated the area under the ROC curves (AUC) for each hub gene to assess its diagnostic performance 25 . 2.10 Statistical analysis Values were analyzed by the two-tailed Student’s t test with the GraphPad Prism 8.0.1 software and presented as mean ± standard error of the mean (SEM). The significance of the statistics is expressed by the value of p. *p < 0.05, **p < 0.01, ***p < 0.001 3. Results 3.1 Differentially expressed genes (DEGs) identification We used the SARS-CoV-2 dataset GSE147507 and PCOS dataset GSE34526 from Gene Expression Omnibus (GEO) database to analyze DEGs of COVID-19 and PCOS. Differential expression genes were collected from primary human lung epithelium (NHBE) cells infected with SARS-CoV-2 and granulosa cells of PCOS patients. After screening, DEGs were identified with a p 1.0 and visualized using a volcano plot and heatmap (Figs. 1 A and 1 B). 3.2 Overlapping gene and enrichment pathway screening Based on a Venn diagram analysis, 27 common DEGs overlapped between GSE34526 and G147507 (Fig. 1 C). We used the GO and KEGG databases to conduct pathway enrichment analyses to better understand the biological functions of the DEGs identified. We found that DEGs contributed to biological processes, including inflammatory response, reaction oxygen species metabolic process, and NF-κB transcription factor activity (Fig. 1 D). In addition, DEGs were associated with molecular functions such as adenylytransferase activity, toll-like receptor binding, and RAGE receptor binding (Fig. 1 D). Similarly, KEGG were enriched in the IL17 signaling pathway, virus infection, cell adhesion molecules, phagosome, TNF signaling pathway and NOD-like receptor signaling pathway (Fig. 1 E). 3.3 Differential expression genes and Hub gene interaction network construction The interaction between DEGs was analyzed using the PPI network based on the STRING database and visualized using Cytoscape (Fig. 2 A). The MCODE plugin of Cytoscape was used to obtain two gene modules, including 12 shared DEGs (Figures. 2B and 2C). The top seven hub genes, including TLR2, MMP9, S100A9, ICAM1, THBD, ITGB2 and CD86, were acquired with Venn diagrams via the seven algorithms in CytoHubba (Fig. 2 D). We built a complex gene interaction network per the GeneMANIA database (Fig. 2 E). Based on the GO analysis of the hub gene, we found that the hub genes were primarily responsible for biological functions such as response to hormones, phagocytosis, viral life cycle, response to insulin, interaction with host and response to interferon-gamma (Fig. 2 F). Moreover, the major enrichment pathways in the KEGG analysis were related to virus infection, toll-like receptor signaling pathway, phagosome, and TNF signaling pathway (Fig. 2 G). 3.4 GSEA analysis of hub genes We used GSEA enrichment to perform systematic KEGG pathway mining for each of the seven hub genes. The TOP 6 signaling pathways for each gene were illustrated in the Fig. 3 . We discovered that the hub genes were significantly enriched to amino acids, glycans metabolism, and immune-related pathways, suggesting that immunity and metabolism may play a key role in PCOS and SARS-Cov-2 infection. 3.5 Immune microenvironment analysis of hub genes To explore the relationship between hub genes and immune microenvironment, we analyzed the level of immune microenvironment of hub genes S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, CD86 using the tool CIBERSORTx. The results revealed that 7 hub genes were significantly correlated with T cells, dendritic cells, mast cells, B cells, NK cells, eosinophils among the 22 types of immune cells (Fig. 4 A), and positively correlated with immune scores (Figures. 4B-4H). These results suggest that the hub genes were significantly correlated with the infiltration of immune cells. 3.6 Interaction analysis of hub genes with ovary specific genes, miRNAs and transcription factor (TF) We investigated the association of ovary-specific genes and pathways with hub genes. As shown in Fig. 5 A, the yellow and blue circles represent hub genes and ovary-specific genes, respectively. These genes had biological functions related to immunity, ovarian follicle formation, and the ovulation cycle (Fig. 5 B). Furthermore, KEGG pathways were enriched in leukocyte transendothelial migration, toll-like receptor signaling and GnRH signaling pathways (Fig. 5 C). We used the TarBase and miRTarBase databases to analyze hub gene-miRNA interactions. A network of interactions is shown in Fig. 6 . In addition, we identified the interaction network between the TF and hub genes with JASPER database (Fig. 7 A). 3.7 Hub gene-drug and hub gene-chemical compounds relationship establishment We displayed the effects of drugs and chemical compounds on hub genes. The information on hub gene and drug relationship was obtained from the Drug Bank database. Figure 7 B illustrates the hub gene-drug interaction. Figure 7 C showed the association between chemical compounds and hub genes. 3.8 Hub gene expression and ROC curve plotting To clarify the expression of hub genes in SARS-Cov-2 and PCOS samples, we assessed their expression in training sets GSE147507 for SARS-CoV-2 infection and GSE34526 for PCOS (Fig. 8 ). Moreover, we confirm their expression in validation sets GSE15703 and GSE10946 (Fig. 9 ). More importantly, we collected clinical samples and constructed a PCOS mouse model for further validation. The results exhibited that the hub genes were significantly higher in the granulosa cells from PCOS patients and ovarian tissues of PCOS mice compared with controls (Fig. 10 ). In general, these results were in line with the above analysis. Based on the validation sets, we evaluated the diagnostic performance of 7 hub genes in COVID-19 and PCOS utilizing ROC curves. The results showed that ITGB2, CD86, S100A9, THBP and MMP9 exhibited potential diagnostic performance, while TLR2 had poor diagnostic performance (Fig. 11 ). 4. Discussion Several retrospective studies reported PCOS as one of the most common comorbidities of COVID-19 26 . However, why PCOS patients are susceptible to COVID-19 remains uncertain. Therefore, understanding the molecular mechanisms, early diagnosis and intervention is of great clinical importance. In this work, we performed bioinformatics analyses on two independent gene chip databases containing SARS-CoV-2 infected NHBE and PCOS granulosa cells and identified 27 common DEGs. According to GO, KEGG and GSEA enrichment analysis, the DEGs were enriched primarily in immune response, NF-kappaB transcription factor activity regulation, Toll-like receptor (TLR) binding, virus infection, TNF signaling pathway, IL-17 signaling pathway and NOD-like receptor signaling pathway. These findings suggest that COVID-19 and PCOS have DEGs associated with inflammation and immune response. It has also been reported that severe COVID-19 triggers an exaggerated inflammatory response that can lead to acute respiratory distress syndrome, a life-threatening condition associated with multi-organ failure and high mortality 27 . By Pearson correlation analysis, we found that 7 hub genes were significantly positively correlated with immune scores, implying that they are associated with immune infiltration. Hub gene expression heatmaps showed that hub gene expression was low in innate immune cells such as monocytes, activated NK cells, and neutrophils. Most of the hub genes were lower expressed in M2 macrophages, whereas highly expressed in M1 macrophages. As is well known, M2 macrophages suppress inflammation while M1 macrophages promote it 28 . The differential expression of hub genes in macrophages may be a common pathogenic cause of PCOS and SRAS-Cov-2. Furthermore, in the training set, validation set, animal models and clinical samples, the expression of hub genes was significantly higher in the PCOS and SARS-Cov-2 groups than in the control group. This suggests that high expression of these genes is associated with the development of inflammation. Recent findings have indicated that the periovulatory follicles of women with PCOS contain elevated inflammatory mediators and that this low-grade inflammation might serve as a precursor to ovarian dysfunction in PCOS 29 . This underlying proinflammatory predisposition may put women with PCOS at increased risk of severe COVID-19 30 . Based on the MCODE and seven algorithms of CytoHubba, we screened seven hub genes, including S100A9, TLR2, CD86, ICAM, THBD, MMP9, and ITGB2. TLR2 was a key hub gene among the hub genes. During a viral infection, the host uses pattern recognition receptors, especially TLRs, to sense the virus and activate the innate immune system 31 . TLR2 was thought to be one of the most significant members of the TLR family and is responsible for sustaining airway inflammation 32 . In line with this, GO and KEGG Pathway identified significant enrichment in signaling pathways involving toll-like receptors. In addition, COVID-19 severity was associated with TLR2 expression. Coronavirus infection caused proinflammatory cytokines to be produced independently of viral entry via TLR2-dependent signaling. TLR2 sensed the SARS-CoV-2 envelope protein as its ligand 33 , 34 . Besides, there was evidence that TLRs are located in ovary granulosa cells, cumulus cells, and theca cells 35 . The abnormal expression of TLRs did not result in good oocyte quality and insufficient fertility 36 . In another study, TLR2 was found to be highly expressed in granulosa cells from PCOS patients and could mediate inflammation and oxidative stress caused by LPS 37 . These results showed that TLR2 was a key molecule mediating inflammation in COVID-19 and PCOS. We speculate that the state of chronic inflammation in PCOS primarily mediates the susceptibility of affected patients to COVID-19. Other hub genes were also associated with inflammatory pathways or metabolic pathways, which were involved in PCOS and became potential risk factors for susceptibility to COVID-19 38–40 . This study also revealed the interaction of hub genes with ovary-specific genes. GO and KEGG Pathway analyses showed significant enrichment in inflammatory pathway, ovarian follicle development and insulin signaling pathway. According to our findings in the PPI network, TLR2 interacted with MYD88, TLR1, IRF3, and other key players in the inflammatory response signaling pathway 41 , 42 . S100A9 could potentially interact with S100A8, and its complex was believed to facilitate cyst migration in PCOS development 43 . S100A9 could increase the production of inflammatory cytokines and disturb the steroidogenesis of PCOS 44 . Researchers have reported that S100A8 and S100A9 have diagnostic biomarker values and can be used to identify COVID-19 patients admitted to intensive care units 45 . Hence, S100A8 and S100A9 appeared to have important roles in causing COVID-19, as well as immune responses. In addition, we also found that EGFR proteins had direct or indirect interactions with S100A9, TLR2, ITGB and ICAM1. As a critical factor in cell growth, differentiation, implantation, and decidualization, EGFR was vital for reproduction 46 . It is believed that the EGFR was responsible for facilitating proliferation and inhibiting apoptosis of granulosa cells by activating the MAPK/ERK signaling pathway and inducing transcription factor AP1 expression 47 . According to other studies, EGFR was expressed by cells of the lungs after SARS-CoV-2 infection. An elevated level of EGFR expression can further exacerbate pulmonary disease and cause fibrosis. Nimotuzumab can block the EGFR, which could be a novel treatment strategy for COVID-19 48 . We have identified micro-RNA species has-miR-21-5p, has-miR-335-5p, has-miR-146a-5p and has-miR-143-5p shared by the hub genes. These micro-RNAs have been linked to the development of PCOS 49 – 51 . Among them, has-miR-335-5p and has-miR-146a-5p were demonstrated to be involved in the DEGs-miRNA network regulation in COVID-19 52 . In particular, our previous study displayed that miR-335-5p may function as a mediator in the etiopathogenesis of PCOS and has the potential as both a novel diagnostic biomarker and therapeutic target for PCOS 11 . Establishing hub gene-miRNA network might provide new insights into the pathogenesis of PCOS and COVID-19. We also identified TFs closely associated with the hub genes. For instance, we identified STAT3 that participated in both TLR2 and S100A9 transcription. STAT3 has been shown to regulate the expression of TLR2 and S100A9. Given the roles of TLR2 and SA100A9 discussed above, we speculated that STAT3 might have the same function in PCOS and COVID-19 53 . Additionally, our previous study found that STAT3 could act directly on the miR-27a-3p promoter to induce granulosa cell apoptosis during the development of PCOS 24 . The drugs and chemical compounds found in our analysis include resveratrol, lipopolysaccharides, methotrexate, nickel, tretinoin, Captopril and Glucosamine. For instance, tretinoin has been stipulated to regulate steroid biosynthesis in human ovarian theca cells 54 . In addition, resveratrol was used to regulate inflammation and oxidative stress of granulosa cells in PCOS via targeting TLR2, which was consistent with our above finding of TLR2 as a hub gene 55 . 5. Conclusion In the present study, we explored the mechanisms of common pathogenetic processes between SARS-CoV-2 infection and polycystic ovary syndrome based on an integrated bioinformatics approach. We mined seven common hub genes and validated their expression in clinical samples and mouse model. Six hub genes of S100A9, CD86, ICAM, THBD, MMP9, and ITGB2 exhibited a desirable performance in diagnosis for COVID - 19 and PCOS. Immune microenvironment analysis revealed that the hub genes were positively correlated with immune scores and significantly correlated with T cells, dendritic cells, B cells, NK cells and eosinophils. This suggests that the inflammatory state of PCOS is responsible for the susceptibility to SRAS-CoV-2. This study may provide a theoretical basis and innovative ideas for further mechanistic research and drug discovery of the comorbidity of two diseases. Abbreviations PCOS polycystic ovarian syndrome GO gene ontology KEGG Kyoto encyclopedia of genes and genomes PPI protein–protein interactions DEGs differentially expressed genes ROC receiver operating characteristic curve TF transcription factor GSEA Gene Set Enrichment Analysis Declarations Data availability All data are derived from publicly available sources, and all links to the data are provided in the materials and methods. Author contributions All authors contributed to the study conception and design. Data analysis, qPCR testing and manuscript writing were performed by Hai Bai and Shanshan Zhang. Material preparation and data collection were performed by Hai Bai. Manuscript revising was performed by Cui Li and Mingming Wang. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Declarations of interest The authors have no relevant financial or non-financial interests to disclose. Fundings This research was supported financially by the Natural Science Research of the Jiangsu Higher Education Institutions of China [Grant no. KY13022201], the Outstanding Talent Research Funding of Xuzhou Medical University [Grant no. RC20552029]. Acknowledgements We thank the depositors of the GSE34526, GSE10946, GSE147507 and GSE157103 datasets for their contribution. References Xu, M. et al. Effects of dietary grape seed proanthocyanidin extract supplementation on meat quality, muscle fiber characteristics and antioxidant capacity of finishing pigs. Food Chemistry 367, 130781, doi: 10.1016/j.foodchem.2021.130781 (2022). Horvath, E. et al. Photocatalytic Nanowires-Based Air Filter: Towards Reusable Protective Masks. 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Supplementary Files Supplenmentarytable1.docx Cite Share Download PDF Status: Published Journal Publication published 02 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Jul, 2024 Reviews received at journal 19 Jul, 2024 Reviews received at journal 19 Jul, 2024 Reviews received at journal 07 Jun, 2024 Reviewers agreed at journal 01 Jun, 2024 Reviewers agreed at journal 31 May, 2024 Reviewers agreed at journal 30 May, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers invited by journal 27 May, 2024 Editor assigned by journal 26 May, 2024 Editor invited by journal 10 May, 2024 Submission checks completed at journal 10 May, 2024 First submitted to journal 04 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4369010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304142976,"identity":"dea7e675-7441-4730-b5a6-bb35f8aa2a19","order_by":0,"name":"Hai Bai","email":"","orcid":"","institution":"Shanxi Datong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Bai","suffix":""},{"id":304142977,"identity":"7a936703-9853-472a-a90d-cc09415a46e3","order_by":1,"name":"Shanshan Zhang","email":"","orcid":"","institution":"Jining Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Zhang","suffix":""},{"id":304142978,"identity":"bc3f4e66-fc8b-4a5f-af24-03e5531f94b0","order_by":2,"name":"Cui Li","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Li","suffix":""},{"id":304142979,"identity":"ce7f9527-eb61-44af-b38d-ffc228fa4505","order_by":3,"name":"Mingming Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYHACxgcMDAfALAlitTAbkKyFTYI0LQbXTqdV8/y5E21wgPngbR4GuzzCWm7nbrs5g+dZ7oYDbMnWPAzJxQS1mAG13PggcRiohcdMmofhQGIDMVoKEgxAWvi/Ea+F4UMC2BY24rTY387dLDnjwOHcmYfZjC3nGCQT1iI5O3fjZ54/h3P7jjc/vPGmwo6wFgRgBhEGxKsfBaNgFIyCUYAHAABl0UFB9zWDGgAAAABJRU5ErkJggg==","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-04 15:25:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4369010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4369010/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-74347-y","type":"published","date":"2024-10-02T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56890551,"identity":"b7fa268c-b1a3-4cdf-9582-538713b8785c","added_by":"auto","created_at":"2024-05-21 19:34:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":342332,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression genes (DEGs) and enrichment pathway identification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Volcano plot showed the DEGs in GSE147507 (SARS-Cov-2 dataset).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003eVolcano plot showed the DEGs in GSE34526 (PCOS dataset).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e Venn diagram displayed 27 overlapping DECs in the two GSE sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD and E \u003c/strong\u003eGO and KEGG enrichment analysis of overlapping DEGs. GO analysis showing the biological, cell component and molecular function enrichment results (\u003cstrong\u003eD\u003c/strong\u003e). KEGG analysis showing the signaling pathway enrichment results (\u003cstrong\u003eE\u003c/strong\u003e). An adjusted p \u0026lt; 0.05 was identified as significantly changed.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/15e6a08f86a574264492ea95.png"},{"id":56890230,"identity":"50e21296-44e5-4ef4-9bca-73e3b5fd6602","added_by":"auto","created_at":"2024-05-21 19:26:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":481086,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs and hub gene interaction network construction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Analysis results of PPI network in STRING online tool and visualization with Cytoscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB and C\u003c/strong\u003e Two significant gene clustering modules were analyzed with Cytoscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD \u003c/strong\u003eVenn diagramdisplayed that 7 overlapping hub genes were screened out with 7 algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE \u003c/strong\u003eHub genes and their co-expression genes were analyzed via GeneMANIA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003eand G\u003cstrong\u003e \u003c/strong\u003eGO and KEGG enrichment analysis of the hub genes. The left side of the figure is a sankey diagram, which represents the genes contained in each pathway, and the right side is a conventional bubble diagram. The size of the bubble indicates the number of genes to which the pathway belongs, and the color of the bubble indicates the p value.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/e5cba5d9a8bf19dae9cd4bfe.png"},{"id":56890234,"identity":"ca63ac9d-07c1-4397-a002-ed2ade0dbffb","added_by":"auto","created_at":"2024-05-21 19:26:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182043,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis of hub genes.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GSEA, gene set enrichment analysis\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/dece1c1d8d055b1826b51ef1.png"},{"id":56890231,"identity":"d315aa27-6627-4af8-a1bf-9670eabfa385","added_by":"auto","created_at":"2024-05-21 19:26:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150903,"visible":true,"origin":"","legend":"\u003cp\u003eImmune microenvironment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Correlation between hub gene expression levels and 22 immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB-H\u003c/strong\u003e Correlation analysis of hub gene expression levels with immune scores.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/8ff4f966c4e9cb9363224057.png"},{"id":56890782,"identity":"dc845d22-9485-43d1-83b2-7e0e398bdce0","added_by":"auto","created_at":"2024-05-21 19:42:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":454824,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes and ovary specific genes interaction network analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003eHub genes-ovary specific genes interaction network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003eGO and KEGG enrichment analysis of the interacting genes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/797c96296964027357e10814.png"},{"id":56890554,"identity":"ebc0a097-4498-4e37-9dc9-6553d3828787","added_by":"auto","created_at":"2024-05-21 19:34:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":344999,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene-microRNA interaction network analysis.\u003c/p\u003e\n\u003cp\u003eHub genes were depicted as red circles, while miRNAs were represented as blue squares.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/331aff1d21c003f76570cd27.png"},{"id":56890239,"identity":"cd5db0c5-696e-4eed-8904-2c79f658124d","added_by":"auto","created_at":"2024-05-21 19:26:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":338970,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene-transcription factors (TFs), hub genes-drugs, hub genes–chemical compounds interaction network analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Hub gene-TFs interaction network analysis. The red circle and green square represented the hub genes and the TFs, respectively. A node's size was determined by its degree.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003eHub genes-drugs interaction network analysis. Drugs and hub genes represented by yellow squares and blue circles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC \u003c/strong\u003eHub genes–chemical compounds interaction network analysis. Green circular nodes represented hub genes, and red squares represented chemical components.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/09ddeb1eeeb433f9860c4fd3.png"},{"id":56890556,"identity":"a6b8e311-a3fc-42fc-847c-b52e9cf0995d","added_by":"auto","created_at":"2024-05-21 19:34:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":154454,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of hub genes in training sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-G \u003c/strong\u003eExpression levels of hub genes in training set GSE147507.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH-N\u003c/strong\u003e N\u003cstrong\u003e \u003c/strong\u003eExpression levels of hub genes in training set GSE34526.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/0e8a4fd27439cc040df967c6.png"},{"id":56890242,"identity":"a049898b-426d-4807-831d-d8aaf7b853d1","added_by":"auto","created_at":"2024-05-21 19:26:28","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":352158,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of hub genes in validation sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-G \u003c/strong\u003eExpression levels of hub genes in validation set GSE157103.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH-N\u003c/strong\u003e Expression levels of hub genes in validation set GSE10946.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/478bc356e424e2af35474f3c.png"},{"id":56890241,"identity":"4b9a7ab4-4e9a-46cd-898b-b40d2f9dabf7","added_by":"auto","created_at":"2024-05-21 19:26:28","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":215197,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of hub genes in PCOS clinical samples and mouse model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-G \u003c/strong\u003eExpression levels of hub genes in PCOS clinical samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH-N\u003c/strong\u003e Expression levels of hub genes in PCOS mouse model.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/bceeaf0085b2c81ba40aa97f.png"},{"id":56890553,"identity":"d2e5eba0-5c3d-4b20-ad40-6e0a65c9fb8d","added_by":"auto","created_at":"2024-05-21 19:34:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":274256,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operator characteristic curve (ROC) diagnostic curve of hub genes in validation set GSE157103 and GSE10946.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/4028453a7fa64d88fd17ef77.png"},{"id":66097456,"identity":"f8be824f-8de6-4739-a794-4220560e2302","added_by":"auto","created_at":"2024-10-07 16:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4075701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/0f328128-616b-4474-b625-e023c103cdb9.pdf"},{"id":56890783,"identity":"20145a8e-5f48-4570-b017-8ad657379cf1","added_by":"auto","created_at":"2024-05-21 19:42:28","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16909,"visible":true,"origin":"","legend":"","description":"","filename":"Supplenmentarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4369010/v1/93dcab66bbf5b00c6a47208a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovering common pathogenetic processes between SARS-CoV-2 infection and polycystic ovary syndrome based on an integrated bioinformatics approach and experiment validation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecently, several countries and regions have been experiencing outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since late 2019 \u003csup\u003e1\u003c/sup\u003e. Infection with SARS-CoV-2 leads to Coronavirus disease 2019 (COVID-19), which can be asymptomatic or presents as acute respiratory distress syndrome \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, in multiple large observations, the virus also readily infected extrapulmonary organs, including the heart, eyes, and kidneys. People with diabetes, obesity and cardiovascular disease are more likely to experience severe symptoms and die from COVID-19 \u003csup\u003e3\u003c/sup\u003e. Therefore, preventive strategies are warranted to protect persons with comorbidities from developing severe COVID-19 \u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMen are more prone to contracting COVID-19 than women, possibly because of higher levels of androgen in men and differences in immune response between both sexes \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Polycystic ovary syndrome (PCOS), the most common endocrine disorder, affects about 5\u0026ndash;15% of women of reproductive age and is characterized by hyperandrogenism, oligoovulation and polycystic ovaries \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. It was reported that more than 80% of PCOS patients suffer from hyperandrogenemia \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, women with PCOS were at risk of long-term complications, including obesity, type 2 diabetes mellitus, nonalcoholic fatty liver disease, and cardiovascular disorders \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Additionally, a recent epidemiological study revealed that women with PCOS were remarkably more likely than controls to contract COVID-19 \u003csup\u003e8\u003c/sup\u003e. These studies suggest that PCOS patients are at higher risk of COVID-19.\u003c/p\u003e \u003cp\u003eConsidering PCOS patients with COVID-19 are at an elevated risk for complications, discerning synergistic biomolecular pathways is crucial for identifying therapeutic targets and repurposing medicines for PCOS patients with COVID-19. With the rapid development of sequencing technology, transcriptome analysis has been widely applied to study the pathogenesis of PCOS and COVID-19 \u003csup\u003e9,10\u003c/sup\u003e. However, the link between PCOS and vulnerability to COVID-19 remains uncertain.\u003c/p\u003e \u003cp\u003eIn this study, we used bioinformatics methods to screen common differentially expressed genes (DEGs) of COVID-19 and PCOS, determine their signaling pathways and immune microenvironment, and build the interaction networks with miRNAs, transcript factors, drugs, and chemical compounds. The research may provide new perspectives and strategies for treating PCOS patients infected with SARS-CoV-2.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Acquisition of datasets\u003c/h2\u003e \u003cp\u003eGene Expression Omnibus (GEO) was used to obtain all eligible datasets. We used four datasets for this study, including two PCOS (GSE34526 and GSE10946) and two SARS-CoV-2 datasets (GSE147507 and GSE157103). GSE34526 and GSE147507 were derived from expression profiles using array, while GSE157103 and GSE10946 were derived from expression profiles using high-throughput sequencing. We selected normal and SARS-CoV-2 infected lung epithelial cell NHBE as part of the analysis of the GSE147507 dataset. In addition, 40 female COVID-19 and 12 female non-COVID-19 samples from the GSE157103 dataset were selected for this study. PCOS clinical samples and mouse model were used for the validation of hub genes. Details of the dataset were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene expression profile information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDataset feature\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE34526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 normals and 7 PCOS patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE10946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 normals and 12 PCOS patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE147507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 normals and 3 SARS-Cov-2 infected cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE157103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 normals and 40 COVID-19 patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMouse model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 controls and 6 PCOS mice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 normals and 20 PCOS patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ethics approval\u003c/h2\u003e \u003cp\u003eWe confirmed that all methods were carried out in accordance with relevant guidelines and regulations, and all methods are reported in accordance with ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The clinical samples were from the previous collection and described in a previous publication \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The research followed the principles of the Declaration of Helsinki and was approved the local Human Investigation Ethics Committee (license numbers APK.002.9.2020 and APK.002.38.2021). Written informed consent was obtained from all patients before inclusion. All animal experiments were approved by the Ethics Committee for Animal Experimentation of Xuzhou Medical University (license numbers 202209S077).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identification of DEGs and enrichment terms\u003c/h2\u003e \u003cp\u003eThe datasets were downloaded from GEO, normalized, and DEGs were extracted with the R package \u0026ldquo;edgeR\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Fold changes (FCs) were calculated for individual genes. DEGs were defined using a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC|\u0026gt;1.0. The R language package Venn Diagram was used to obtain shared DEGs \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Heatmap and volcano plot were created using an online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GO and KEGG enrichment analysis was performed using R, and adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to select enriched terms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Protein\u0026ndash;protein interactions (PPI) network construction\u003c/h2\u003e \u003cp\u003eThe PPI interaction network was constructed by STRING ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and visualized by Cytoscape (version 3.9.1) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Molecular Complex Detection (MCODE) in Cytoscape was used to analyze core functional modules \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. CytoHubba, a plugin for Cytoscape, was used to select hub genes. Seven algorithms were applied to confirm the final hub genes, which were then visualized with Venn diagrams \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Finally, we used GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://arriveguidelines.org\" target=\"_blank\"\u003ewww.genemania.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to create a co-expression network of hub genes \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene Set Enrichment Analysis (GSEA) analysis\u003c/h2\u003e \u003cp\u003eWe obtained the GSEA software (version 3.0) from the GSEA website and divided the samples into high (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50%) and low (\u0026lt;\u0026thinsp;50%) expression groups based on gene expression levels \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The \u0026ldquo;cluster Profiler\u0026rdquo; package was used to perform GSEA on potential mechanisms of c2 (c2. cp.v7.5.1. symbols.gmt) in the Molecular Signature Database (MSigDB) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Pathways with false discovery rates (FDR) less than 0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immune microenvironment analysis\u003c/h2\u003e \u003cp\u003eDatasets GSE34526 and GSE157103 were downloaded from the GEO database for data pooling. In order to obtain individual gene immune scores and expression matrices for 22 immune cells, CIBERSORTx was used to analyze the immune microenvironment of human immune cells with the corresponding genes S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, CD86 \u003csup\u003e21\u003c/sup\u003e. The scatter density of each gene was plotted using the \u0026ldquo;ggplot2\u0026rdquo; package in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Genes, drugs, and Chemical Compounds-hub genes regulatory networks establishment\u003c/h2\u003e \u003cp\u003eWe established hub gene-miRNA, hub gene-transcription factor (TF), hub gene-drug and hub gene-chemical compound interaction network using NetworkAnalyst \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. A hub gene-miRNA interaction network was discovered using TarBase and miRTarBase databases. In addition, a TF-DEG interaction network was developed using the JASPAR database \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 PCOS patient sample collection and mouse model built\u003c/h2\u003e \u003cp\u003eThe details of granulosa cells collection and PCOS mouse model built were described in a previous publication \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. For the collection of granulocytes, in brief, the follicular fluid was centrifuged at 250\u0026times;g for 10 minutes and the granulosa cells were aspirated and placed in a new centrifuge tube. The cells were washed with PBS and resuspended, and collected by centrifugation again.\u003c/p\u003e \u003cp\u003eFor the PCOS mouse model, in brief, 3-week-old female C57BL/6J mice were injected subcutaneously with DHEA (6 mg/100 g body weight) or an equivalent dose of sesame oil for 28 consecutive days and their ovarian tissue was collected for assay analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.9 Hub gene testing and receiver operating characteristic curve (ROC) curve plotting\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eHub genes testing was described as previously \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Briefly, RNA was extracted from tissues or cells using the TRIzol method and reverse transcribed using a cDNA kit. Hub gene expression was detected by qPCR, and glyceraldehyde phosphate dehydrogenase (GAPDH) was used as an internal reference. Primer sequences are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUsing the R package \u0026ldquo;pROC\u0026rdquo;, we constructed ROC curves and calculated the area under the ROC curves (AUC) for each hub gene to assess its diagnostic performance \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eValues were analyzed by the two-tailed Student\u0026rsquo;s t test with the GraphPad Prism 8.0.1 software and presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). The significance of the statistics is expressed by the value of p. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Differentially expressed genes (DEGs) identification\u003c/h2\u003e \u003cp\u003eWe used the SARS-CoV-2 dataset GSE147507 and PCOS dataset GSE34526 from Gene Expression Omnibus (GEO) database to analyze DEGs of COVID-19 and PCOS. Differential expression genes were collected from primary human lung epithelium (NHBE) cells infected with SARS-CoV-2 and granulosa cells of PCOS patients. After screening, DEGs were identified with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 FC | \u0026gt;1.0 and visualized using a volcano plot and heatmap (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Overlapping gene and enrichment pathway screening\u003c/h2\u003e \u003cp\u003eBased on a Venn diagram analysis, 27 common DEGs overlapped between GSE34526 and G147507 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We used the GO and KEGG databases to conduct pathway enrichment analyses to better understand the biological functions of the DEGs identified. We found that DEGs contributed to biological processes, including inflammatory response, reaction oxygen species metabolic process, and NF-κB transcription factor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In addition, DEGs were associated with molecular functions such as adenylytransferase activity, toll-like receptor binding, and RAGE receptor binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Similarly, KEGG were enriched in the IL17 signaling pathway, virus infection, cell adhesion molecules, phagosome, TNF signaling pathway and NOD-like receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differential expression genes and Hub gene interaction network construction\u003c/h2\u003e \u003cp\u003eThe interaction between DEGs was analyzed using the PPI network based on the STRING database and visualized using Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The MCODE plugin of Cytoscape was used to obtain two gene modules, including 12 shared DEGs (Figures. 2B and 2C). The top seven hub genes, including TLR2, MMP9, S100A9, ICAM1, THBD, ITGB2 and CD86, were acquired with Venn diagrams via the seven algorithms in CytoHubba (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). We built a complex gene interaction network per the GeneMANIA database (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Based on the GO analysis of the hub gene, we found that the hub genes were primarily responsible for biological functions such as response to hormones, phagocytosis, viral life cycle, response to insulin, interaction with host and response to interferon-gamma (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Moreover, the major enrichment pathways in the KEGG analysis were related to virus infection, toll-like receptor signaling pathway, phagosome, and TNF signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GSEA analysis of hub genes\u003c/h2\u003e \u003cp\u003eWe used GSEA enrichment to perform systematic KEGG pathway mining for each of the seven hub genes. The TOP 6 signaling pathways for each gene were illustrated in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We discovered that the hub genes were significantly enriched to amino acids, glycans metabolism, and immune-related pathways, suggesting that immunity and metabolism may play a key role in PCOS and SARS-Cov-2 infection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Immune microenvironment analysis of hub genes\u003c/h2\u003e \u003cp\u003eTo explore the relationship between hub genes and immune microenvironment, we analyzed the level of immune microenvironment of hub genes S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, CD86 using the tool CIBERSORTx. The results revealed that 7 hub genes were significantly correlated with T cells, dendritic cells, mast cells, B cells, NK cells, eosinophils among the 22 types of immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and positively correlated with immune scores (Figures. 4B-4H). These results suggest that the hub genes were significantly correlated with the infiltration of immune cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Interaction analysis of hub genes with ovary specific genes, miRNAs and transcription factor (TF)\u003c/h2\u003e \u003cp\u003eWe investigated the association of ovary-specific genes and pathways with hub genes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the yellow and blue circles represent hub genes and ovary-specific genes, respectively. These genes had biological functions related to immunity, ovarian follicle formation, and the ovulation cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Furthermore, KEGG pathways were enriched in leukocyte transendothelial migration, toll-like receptor signaling and GnRH signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe used the TarBase and miRTarBase databases to analyze hub gene-miRNA interactions. A network of interactions is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In addition, we identified the interaction network between the TF and hub genes with JASPER database (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Hub gene-drug and hub gene-chemical compounds relationship establishment\u003c/h2\u003e \u003cp\u003eWe displayed the effects of drugs and chemical compounds on hub genes. The information on hub gene and drug relationship was obtained from the Drug Bank database. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB illustrates the hub gene-drug interaction. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC showed the association between chemical compounds and hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Hub gene expression and ROC curve plotting\u003c/h2\u003e \u003cp\u003eTo clarify the expression of hub genes in SARS-Cov-2 and PCOS samples, we assessed their expression in training sets GSE147507 for SARS-CoV-2 infection and GSE34526 for PCOS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Moreover, we confirm their expression in validation sets GSE15703 and GSE10946 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). More importantly, we collected clinical samples and constructed a PCOS mouse model for further validation. The results exhibited that the hub genes were significantly higher in the granulosa cells from PCOS patients and ovarian tissues of PCOS mice compared with controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In general, these results were in line with the above analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the validation sets, we evaluated the diagnostic performance of 7 hub genes in COVID-19 and PCOS utilizing ROC curves. The results showed that ITGB2, CD86, S100A9, THBP and MMP9 exhibited potential diagnostic performance, while TLR2 had poor diagnostic performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSeveral retrospective studies reported PCOS as one of the most common comorbidities of COVID-19 \u003csup\u003e26\u003c/sup\u003e. However, why PCOS patients are susceptible to COVID-19 remains uncertain. Therefore, understanding the molecular mechanisms, early diagnosis and intervention is of great clinical importance. In this work, we performed bioinformatics analyses on two independent gene chip databases containing SARS-CoV-2 infected NHBE and PCOS granulosa cells and identified 27 common DEGs.\u003c/p\u003e \u003cp\u003eAccording to GO, KEGG and GSEA enrichment analysis, the DEGs were enriched primarily in immune response, NF-kappaB transcription factor activity regulation, Toll-like receptor (TLR) binding, virus infection, TNF signaling pathway, IL-17 signaling pathway and NOD-like receptor signaling pathway. These findings suggest that COVID-19 and PCOS have DEGs associated with inflammation and immune response. It has also been reported that severe COVID-19 triggers an exaggerated inflammatory response that can lead to acute respiratory distress syndrome, a life-threatening condition associated with multi-organ failure and high mortality \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. By Pearson correlation analysis, we found that 7 hub genes were significantly positively correlated with immune scores, implying that they are associated with immune infiltration. Hub gene expression heatmaps showed that hub gene expression was low in innate immune cells such as monocytes, activated NK cells, and neutrophils. Most of the hub genes were lower expressed in M2 macrophages, whereas highly expressed in M1 macrophages. As is well known, M2 macrophages suppress inflammation while M1 macrophages promote it \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The differential expression of hub genes in macrophages may be a common pathogenic cause of PCOS and SRAS-Cov-2. Furthermore, in the training set, validation set, animal models and clinical samples, the expression of hub genes was significantly higher in the PCOS and SARS-Cov-2 groups than in the control group. This suggests that high expression of these genes is associated with the development of inflammation. Recent findings have indicated that the periovulatory follicles of women with PCOS contain elevated inflammatory mediators and that this low-grade inflammation might serve as a precursor to ovarian dysfunction in PCOS \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This underlying proinflammatory predisposition may put women with PCOS at increased risk of severe COVID-19 \u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on the MCODE and seven algorithms of CytoHubba, we screened seven hub genes, including S100A9, TLR2, CD86, ICAM, THBD, MMP9, and ITGB2. TLR2 was a key hub gene among the hub genes. During a viral infection, the host uses pattern recognition receptors, especially TLRs, to sense the virus and activate the innate immune system \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. TLR2 was thought to be one of the most significant members of the TLR family and is responsible for sustaining airway inflammation \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In line with this, GO and KEGG Pathway identified significant enrichment in signaling pathways involving toll-like receptors. In addition, COVID-19 severity was associated with TLR2 expression. Coronavirus infection caused proinflammatory cytokines to be produced independently of viral entry via TLR2-dependent signaling. TLR2 sensed the SARS-CoV-2 envelope protein as its ligand \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBesides, there was evidence that TLRs are located in ovary granulosa cells, cumulus cells, and theca cells \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The abnormal expression of TLRs did not result in good oocyte quality and insufficient fertility \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In another study, TLR2 was found to be highly expressed in granulosa cells from PCOS patients and could mediate inflammation and oxidative stress caused by LPS \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These results showed that TLR2 was a key molecule mediating inflammation in COVID-19 and PCOS. We speculate that the state of chronic inflammation in PCOS primarily mediates the susceptibility of affected patients to COVID-19. Other hub genes were also associated with inflammatory pathways or metabolic pathways, which were involved in PCOS and became potential risk factors for susceptibility to COVID-19 \u003csup\u003e38\u0026ndash;40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study also revealed the interaction of hub genes with ovary-specific genes. GO and KEGG Pathway analyses showed significant enrichment in inflammatory pathway, ovarian follicle development and insulin signaling pathway. According to our findings in the PPI network, TLR2 interacted with MYD88, TLR1, IRF3, and other key players in the inflammatory response signaling pathway \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. S100A9 could potentially interact with S100A8, and its complex was believed to facilitate cyst migration in PCOS development \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. S100A9 could increase the production of inflammatory cytokines and disturb the steroidogenesis of PCOS \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Researchers have reported that S100A8 and S100A9 have diagnostic biomarker values and can be used to identify COVID-19 patients admitted to intensive care units \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Hence, S100A8 and S100A9 appeared to have important roles in causing COVID-19, as well as immune responses.\u003c/p\u003e \u003cp\u003eIn addition, we also found that EGFR proteins had direct or indirect interactions with S100A9, TLR2, ITGB and ICAM1. As a critical factor in cell growth, differentiation, implantation, and decidualization, EGFR was vital for reproduction \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. It is believed that the EGFR was responsible for facilitating proliferation and inhibiting apoptosis of granulosa cells by activating the MAPK/ERK signaling pathway and inducing transcription factor AP1 expression \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. According to other studies, EGFR was expressed by cells of the lungs after SARS-CoV-2 infection. An elevated level of EGFR expression can further exacerbate pulmonary disease and cause fibrosis. Nimotuzumab can block the EGFR, which could be a novel treatment strategy for COVID-19 \u003csup\u003e48\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe have identified micro-RNA species has-miR-21-5p, has-miR-335-5p, has-miR-146a-5p and has-miR-143-5p shared by the hub genes. These micro-RNAs have been linked to the development of PCOS \u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Among them, has-miR-335-5p and has-miR-146a-5p were demonstrated to be involved in the DEGs-miRNA network regulation in COVID-19 \u003csup\u003e52\u003c/sup\u003e. In particular, our previous study displayed that miR-335-5p may function as a mediator in the etiopathogenesis of PCOS and has the potential as both a novel diagnostic biomarker and therapeutic target for PCOS \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Establishing hub gene-miRNA network might provide new insights into the pathogenesis of PCOS and COVID-19.\u003c/p\u003e \u003cp\u003eWe also identified TFs closely associated with the hub genes. For instance, we identified STAT3 that participated in both TLR2 and S100A9 transcription. STAT3 has been shown to regulate the expression of TLR2 and S100A9. Given the roles of TLR2 and SA100A9 discussed above, we speculated that STAT3 might have the same function in PCOS and COVID-19 \u003csup\u003e53\u003c/sup\u003e. Additionally, our previous study found that STAT3 could act directly on the miR-27a-3p promoter to induce granulosa cell apoptosis during the development of PCOS \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe drugs and chemical compounds found in our analysis include resveratrol, lipopolysaccharides, methotrexate, nickel, tretinoin, Captopril and Glucosamine. For instance, tretinoin has been stipulated to regulate steroid biosynthesis in human ovarian theca cells \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In addition, resveratrol was used to regulate inflammation and oxidative stress of granulosa cells in PCOS via targeting TLR2, which was consistent with our above finding of TLR2 as a hub gene \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn the present study, we explored the mechanisms of common pathogenetic processes between SARS-CoV-2 infection and polycystic ovary syndrome based on an integrated bioinformatics approach. We mined seven common hub genes and validated their expression in clinical samples and mouse model. Six hub genes of S100A9, CD86, ICAM, THBD, MMP9, and ITGB2 exhibited a desirable performance in diagnosis for COVID\u003cem\u003e-\u003c/em\u003e19 and PCOS. Immune microenvironment analysis revealed that the hub genes were positively correlated with immune scores and significantly correlated with T cells, dendritic cells, B cells, NK cells and eosinophils. This suggests that the inflammatory state of PCOS is responsible for the susceptibility to SRAS-CoV-2. This study may provide a theoretical basis and innovative ideas for further mechanistic research and drug discovery of the comorbidity of two diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolycystic ovarian syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto encyclopedia of genes and genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein\u0026ndash;protein interactions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etranscription factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are derived from publicly available sources, and all links to the data are provided in the materials and methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll authors contributed to the study conception and design.\u0026nbsp;\u003c/em\u003eData analysis, qPCR testing and manuscript writing\u0026nbsp;\u003cem\u003ewere performed by Hai Bai and Shanshan Zhang. Material preparation and data collection were performed by Hai Bai.\u003c/em\u003e Manuscript revising\u0026nbsp;was performed by\u0026nbsp;Cui Li and Mingming Wang.\u003cem\u003e\u0026nbsp;All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported financially by\u0026nbsp;the\u0026nbsp;Natural Science Research of the Jiangsu\u0026nbsp;Higher Education Institutions of China [Grant no. KY13022201], the\u0026nbsp;Outstanding Talent Research Funding of Xuzhou Medical University [Grant no.\u0026nbsp;RC20552029].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the depositors of the GSE34526, GSE10946, GSE147507 and GSE157103 datasets for their contribution.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu, M. \u003cem\u003eet al.\u003c/em\u003e Effects of dietary grape seed proanthocyanidin extract supplementation on meat quality, muscle fiber characteristics and antioxidant capacity of finishing pigs. 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N. 2.5 A structure of aspartate carbamoyltransferase complexed with the bisubstrate analog N-(phosphonacetyl)-L-aspartate. J Mol Biol 193, 527\u0026ndash;553, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0022-2836(87)90265-8\u003c/span\u003e\u003cspan address=\"10.1016/0022-2836(87)90265-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1987).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, polycystic ovary syndrome, integrated bioinformatics, genetic signature, immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-4369010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4369010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among polycystic ovary syndrome (PCOS) is significantly higher than in the general population. However, the mechanisms underlying this remain obscure. This study aimed to explore the mechanisms by identifying genetic signature of SARS-CoV-2 infection in PCOS. In the present study, a total of 27 common differentially expressed genes (DEGs) were selected for subsequent analyses. Functional analyses showed that immunity and hormone related pathways collectively participated in the development and progression of PCOS and SARS Cov-2 infection. Under these, 7 significant hub genes were identified, including S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, CD86 by using the algorithm in Cytoscape. Furthermore, hub genes expression was confirmed in validation set, PCOS clinical samples and mouse model. Immune microenvironment analysis with CIBERSORTx database demonstrated that the hub genes were significantly correlated with T cells, dendritic cells, mast cells, B cells, NK cells, eosinophils and positively correlated with immune scores. Among the hub genes, S100A9, MMP9, THBD, ITGB2, CD86 and ICAM1 exhibited preferable values as diagnostic makers for COVID-19 and PCOS. In addition, we established the interaction networks of ovary-specific genes, transcription factors, miRNAs, drugs, and chemical compounds with hub genes with NetworkAnalyst. This work uncovered the common pathogenesis and genetic signature of PCOS and SARS-CoV-2 infection, which might provide a theoretical basis and innovative ideas for further mechanistic research and drug discovery of the comorbidity of two diseases.\u003c/p\u003e","manuscriptTitle":"Discovering common pathogenetic processes between SARS-CoV-2 infection and polycystic ovary syndrome based on an integrated bioinformatics approach and experiment validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-21 19:26:23","doi":"10.21203/rs.3.rs-4369010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-23T05:15:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-19T18:11:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-19T14:44:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-08T01:22:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9247973956516522240904203929305199733","date":"2024-06-01T08:25:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172149838037742371858643754940956558704","date":"2024-05-31T14:25:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142628631193633352378247989042136396727","date":"2024-05-30T12:07:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281258536841104050402632865649160938056","date":"2024-05-27T16:54:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-27T07:22:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-26T22:06:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-10T10:35:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-10T10:34:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-04T15:15:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d4614c7-9cb8-4a1f-a109-b29082c9eb1c","owner":[],"postedDate":"May 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32108258,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":32108259,"name":"Biological sciences/Immunology"},{"id":32108260,"name":"Biological sciences/Molecular biology"},{"id":32108261,"name":"Health sciences/Biomarkers"},{"id":32108262,"name":"Health sciences/Diseases"},{"id":32108263,"name":"Health sciences/Endocrinology"},{"id":32108264,"name":"Health sciences/Signs and symptoms"}],"tags":[],"updatedAt":"2024-10-07T16:08:32+00:00","versionOfRecord":{"articleIdentity":"rs-4369010","link":"https://doi.org/10.1038/s41598-024-74347-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-02 15:57:58","publishedOnDateReadable":"October 2nd, 2024"},"versionCreatedAt":"2024-05-21 19:26:23","video":"","vorDoi":"10.1038/s41598-024-74347-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-74347-y","workflowStages":[]},"version":"v1","identity":"rs-4369010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4369010","identity":"rs-4369010","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}