Exploring the Mechanism of Regulating Tubal Inflammatory Infertility through TGF- B1/Smads/CTGF Signal Transduction Pathway by Promoting Blood Circulation, Removing Stasis, and Tonifying collaterals Method Based on Bioinformatics and Molecular Docking

Exploring the Mechanism of Regulating Tubal Inflammatory Infertility through TGF- B1/Smads/CTGF Signal Transduction Pathway by Promoting Blood Circulation, Removing Stasis, and Tonifying collaterals Method Based on Bioinformatics and Molecular Docking | 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 Exploring the Mechanism of Regulating Tubal Inflammatory Infertility through TGF- B1/Smads/CTGF Signal Transduction Pathway by Promoting Blood Circulation, Removing Stasis, and Tonifying collaterals Method Based on Bioinformatics and Molecular Docking Shengpan Jiang, Hui Dong, Tao Zhou, Yiqing Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7454231/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 Purpose: Based on bioinformatics and molecular docking, this study explores the mechanism of promoting blood circulation, removing blood stasis, and unblocking collaterals through the TGF-B1/Smads/CTGF signaling pathway to regulate tubal inflammatory infertility. Methods: Collect data from OMIM database, RefSeq Gene database, TTD database, CTD database, and Genecards database. Collect 12 medicinal ingredients and targets of modified prescriptions for promoting blood circulation, removing blood stasis, and unblocking collaterals using the TCMSP database. Collect common targets using Venn diagram and perform GO and KEGG analysis. Select relevant representative targets and corresponding components for molecular docking. Results： There are 37,176 target points for tubal inflammatory infertility disease. Through pharmacological analysis, there are 343 corresponding targets for 14 related traditional Chinese medicine herbs using the method of promoting blood circulation, removing blood stasis, and unblocking collaterals. There are 326 common targets with tubal inflammatory infertility disease. The top 20 key targets including AKT1, TNF, IL6, IL1B, TP53, EGFR, ESR1, PTGS2, STAT3, SRC, CASP3, BCL2, NFKB1, HIF1A, MMP9, CTNNB1, IFNG, ICAM1, STAT1, and NFKBIA were obtained through Cytoscape software analysis. It involves signaling pathways such as WP5434, hsa05417, hsa05208, WP5420, WP5470, WP2882, WP4298, hsa05207, WP4396, hsa05418, etc. Conclusion: The method of promoting blood circulation, removing blood stasis, and unblocking collaterals can regulate tubal inflammatory infertility through multiple targets and pathways, including the TGF-B1/Smads/CTGF signaling pathway. Bioinformatics Molecular docking method The method of promoting blood circulation and removing blood stasis and unblocking collaterals TGF-B1/Smads/CTGF signal Transduction pathway Regulation Tubal inflammatory infertility Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction Tubal factor infertility (TFI) is one of the common causes of infertility in women of reproductive age. Its pathological basis mainly includes tubal inflammation, fibrosis, and obstruction [ 1 – 3 ]. Inflammatory factors play a significant role in the process of tubal inflammation and fibrosis, with transforming growth factor-beta 1 (TGF-B1) and connective tissue growth factor (CTGF) being key players [ 4 , 5 ]. These growth factors regulate a series of signaling pathways, including the TGF-B1/Smads/CTGF pathway, which are involved in the occurrence, development, and fibrosis of tubal inflammation, ultimately affecting the normal function of the fallopian tubes and leading to infertility. The method of promoting blood circulation, removing blood stasis, and unblocking collaterals is one of the commonly used TCM approaches for treating tubal inflammatory infertility. This method improves local blood circulation and the microenvironment of the fallopian tubes by regulating qi and blood, promoting blood circulation, and unblocking meridians, thereby reducing inflammation, promoting tissue repair, and restoring tubal function [ 6 – 8 ]. However, the specific mechanism by which this method treats tubal inflammatory infertility is not fully understood. In recent years, bioinformatics and molecular docking techniques have been widely applied in TCM research [ 9 , 10 ]. Bioinformatics can integrate and analyze large amounts of biological data, revealing interactions between genes, proteins, and signaling pathways, providing new perspectives and methods for studying the mechanisms of TCM [ 11 , 12 , 13 ]. Molecular docking can simulate molecular interactions, predict the binding ability and affinity between drugs and targets, and provide strong support for drug screening and mechanism research. Based on this background, this study uses bioinformatics and molecular docking to explore the mechanism by which the method of promoting blood circulation, removing blood stasis, and unblocking collaterals regulates tubal inflammatory infertility through the TGF-B1/Smads/CTGF signaling pathway. Specifically, this study will first use bioinformatics to analyze the gene and protein expression profiles related to tubal inflammatory infertility, as well as the key nodes and regulatory mechanisms of the TGF-B1/Smads/CTGF signaling pathway. Then, molecular docking will be used to predict the binding ability and affinity between key components of the method and key targets in the TGF-B1/Smads/CTGF signaling pathway, revealing the regulatory effect of the method on this pathway. This will further elucidate the biological mechanism of the method in treating tubal inflammatory infertility, providing scientific evidence and theoretical support for the application of TCM in this field. It will also offer new ideas and methods for the treatment of tubal inflammatory infertility, potentially improving treatment outcomes and quality of life for patients. 2. Materials and methods 2.1 Disease Target Screening This study systematically identified gene targets related to TFI by integrating multiple bioinformatics databases. Using keywords such as "Tubal factor infertility," "Tubal inflammation," and "infertility," a comprehensive search was conducted in the OMIM, RefSeq Gene, TTD, CTD, and Genecards databases to obtain gene expression information related to tubal infertility. After integrating information from these five databases, duplicate gene targets were removed to ensure each target was retained only once, resulting in a disease target set for tubal infertility. 2.2 Screening of Effective Components and Target Prediction for the Modified Prescription of Promoting Blood Circulation, Removing Blood Stasis, and Unblocking Collaterals To screen the effective components and predict their targets for the modified prescription of promoting blood circulation, removing blood stasis, and unblocking collaterals, the TCMSP database was used. The prescription includes: Angelica sinensis (15g), Rehmannia glutinosa (15g), Peach kernel (15g), Carthamus tinctorius (10g), Paeonia lactiflora (10g), Bupleurum chinense (10g), Ligusticum chuanxiong (10g), Glycyrrhiza uralensis (6g), Manis pentadactyla (9g), Luffa cylindrica (15g), Retinervus Luffae Fructus (10g), Gleditsia sinensis (10g), Melia toosendan (10g), and Millettia dielsiana (15g). The database extracted compound information for each herb, and effective active components were screened based on oral bioavailability (≥ 30%) and drug-likeness (≥ 0.18). Target proteins and gene information for these active components were also collected. To standardize the data format, the Uniprot database was used to convert drug target IDs into gene symbols using "Homo sapiens" as the keyword. Based on this, a TCM-component-target database was established to support subsequent network diagram construction and topological analysis. 2.3 Drawing the TCM-Component-Gene Network Diagram To visually demonstrate the potential mechanism of the modified prescription in treating TFI, the R software package "VennDiagram" was used to perform intersection analysis between drug genes and disease genes. The resulting intersection genes were considered potential therapeutic targets for TFI. Perl language was used to organize the intersection gene network and type files, and Cytoscape software was used for visualization, drawing the TCM-component-gene network diagram. This diagram clearly shows how the effective components in the prescription act on specific gene targets to treat TFI. 2.4 PPI Network Construction, Core Network Construction, and Topological Analysis To further understand the interaction relationships between the potential therapeutic genes of the modified prescription for TFI, these genes were input into the STRING database. After selecting Homo sapiens as the organism, a protein-protein interaction (PPI) network was obtained. The PPI network information was then imported into Cytoscape software, and the CytoNCA plugin was used for topological analysis. This analysis identified key nodes and pathways in the core network, which may play important roles in the treatment of TFI by the modified prescription. 3. Results 3.1 Differential Gene Analysis in the GEO Database By querying the GEO database for gene expression profiles related to tubal infertility, the GSE262037 chip expression dataset and GPL21827 platform file were obtained, including 3 normal samples and 3 tubal inflammation samples. Differential genes were selected based on an adjusted P-value < 0.05 and a fold change ≥ 2 (|log2FC| ≥ 1.0), resulting in 241 differential genes, including 143 upregulated genes and 94 downregulated genes. The volcano plot of differential genes is shown in Figure 1, where green represents downregulated genes, red represents upregulated genes, and black represents genes with no significant expression changes. 3.2 Statistical Analysis of Disease Targets for Tubal Inflammatory Infertility Targets related to tubal inflammatory infertility were collected from the five databases mentioned above（Figure 2）. The CTD database contained the most targets (37,144), followed by Genecards (455), RefSeq Gene (212), TTD (22), and OMIM (15). After removing duplicates, a total of 37,176 disease targets for tubal inflammatory infertility were obtained. 3.3 Effective Components and Predicted Targets of the Method of Promoting Blood Circulation, Removing Blood Stasis, and Unblocking Collaterals The 14 TCM herbs in the prescription were screened for components based on oral bioavailability (≥30%) and drug-likeness (≥0.18). Manis pentadactyla and Retinervus Luffae Fructus were not available in the TCMSP database and were collected from the ETCM database, PubChem, and TCM websites. After removing duplicates, the prescription had 343 unique targets. The top 100 nodes were analyzed using the cytoHubba plugin, identifying 69 key components, 6 key herbs, and 25 key targets in Figure 3.Please see the below Table 1 2, 3, and 4. 3.4 Common Targets Between the promoting blood circulation, removing blood stasis and dredging collateral method and Tubal Inflammatory Infertility Collect the targets of tubal inflammatory infertility disease and the corresponding targets of the promoting blood circulation, removing blood stasis and dredging collateral method into the Venny2.1 online tool for interaction analysis, resulting in 326 common targets (see Figure 4). Input the obtained common targets into the STRING database, selecting the human species and setting the interaction score threshold, to filter out the 323 most relevant and reliable protein interaction pairs. After completing the settings, click the search button to generate a protein-protein interaction (PPI) network related to the promoting blood circulation, removing blood stasis and dredging collateral method and tubal inflammatory infertility targets using the STRING database. The results show 323 nodes (with 3 not recognized in the database), 6,279 edges, and a clustering coefficient of 0.585. After completing these steps, download the TSV file generated by the STRING database, which contains detailed information about all nodes (proteins) and edges (interactions) in the network. This information serves as the foundation for subsequent visualization in Cytoscape software. To simplify the network and focus on core interactions, isolated nodes can be hidden. Isolated nodes refer to those that exist in isolation in the network and do not form interactions with other nodes. By removing isolated nodes, the protein interactions become more specific. As shown in Figure 5, this is the initial graph from the STRING database, which contains isolated proteins. Download the TSV file and import it into Cytoscape 3.10.0 to hide the isolated nodes. Through degree value analysis, four target network circles are created based on degree values of 0–20, 21–50, 51–100, and ≥101, from the outer to the inner layer (as shown in Figure 6). To obtain the top 20 important targets, the two outer circles with degree values of 0–20 and 21–50 are removed, resulting in Figure 7. Then, using the cytoHubba plugin, the top 20 important common targets are identified (as shown in Figure 8), including AKT1 (degree: 167), TNF (degree: 158), IL6 (degree: 157), IL1B (degree: 148), TP53 (degree: 148), EGFR (degree: 136), ESR1 (degree: 133), PTGS2 (degree: 133), STAT3 (degree: 131), SRC (degree: 129), CASP3 (degree: 129), BCL2 (degree: 124), NFKB1 (degree: 122), HIF1A (degree: 122), MMP9 (degree: 120), CTNNB1 (degree: 119), IFNG (degree: 107), ICAM1 (degree: 93), STAT1 (degree: 90), and NFKBIA (degree: 80). 3.5 GO Functional Enrichment Analysis and KEGG Pathway Analysis of Potential Targets The 326 common targets were analyzed using the Metascape database for GO functional enrichment and KEGG pathway analysis. The results showed enrichment in responses to xenobiotic stimuli, cellular responses to lipids, cellular responses to nitrogen compounds, neurotransmitter receptor activity, responses to molecules of bacterial origin, regulation of hormone levels, responses to alcohol, responses to nutrient levels, positive regulation of programmed cell death, responses to decreased oxygen levels, cellular responses to cytokine stimuli, regulation of system processes, gland development, steroid metabolic processes, responses to radiation, regulation of inflammatory responses, responses to oxidative stress, intracellular receptor signaling pathways, regulation of apoptotic signaling pathways, and regulation of reactive oxygen species metabolic processes (see Table 5, Figure 9). Through signaling pathway analysis, the following pathway clusters were identified: cancer pathways, lipid and atherosclerosis, chemical carcinogenesis—reactive oxygen species, ADHD and autism spectrum disorder (ASD) pathways, acute viral myocarditis, chemical carcinogenesis—receptor activation, non-alcoholic fatty liver disease, fluid shear stress and atherosclerosis, NF-κB survival signaling pathway induced by photodynamic therapy, receptor complexes, androgen receptor networks in prostate cancer, neuroinflammation, and glutamatergic signaling. Among these, the TGF-β1/Smads/CTGF signaling pathway is only involved in cancer pathways and spinal cord injury. See Table 6 for details. 3.6 Analysis of the Regulation of Tubal Inflammatory Infertility by the Method of Promoting Blood Circulation, Removing Blood Stasis, and Unblocking Collaterals through the TGF-B1/Smads/CTGF Signaling Pathway 3.6.1 Pathway Analysis The Cancer pathways and Spinal cord injury signaling pathway clusters were dissected to identify genes involved in the TGF-B1/Smads/CTGF signaling pathway. Both pathways clearly involved TGF-B1/Smads/CTGF signaling, and some pathways, although not explicitly involving TGF-B1/Smads/CTGF, were related to TGF-B1, including WP2431, hsa05140, WP2328, WP5095, hsa05321, WP560, WP4816, WP5039, M54, M36, WP5473, hsa04060, and M5883. See Figure 10. 3. 6.2 Molecular Docking Analysis Typical targets of tubal inflammatory infertility (e.g., TGFB1, ESR2) were selected for molecular docking with common chemical molecules in the prescription (e.g., MOL000358, MOL000098 for TGFB1; MOL000392, MOL000354, MOL004598, MOL000490, MOL004609, MOL002135, MOL002565 for ESR2). The PDB files of the targets were obtained from the RCSB database, and the SDF files of the chemical molecules were obtained from PubChem. AutoDock was used for blind docking to identify binding sites, binding energy, and residual structures. The results of molecular docking between some drug components and common targets are shown in Tables 7-13 and Figure 11-16. 4. Discussion In TCM, tubal inflammatory infertility is not uniformly named but is classified under categories such as "abdominal mass," "infertility," "cessation of menstruation," and "abdominal pain in women." Its formation mechanism is related to blood stasis, meridian blockage, and pathogenic factors entering the uterus, leading to infertility [ 14 – 16 ]. This study identified 37,176 disease targets for tubal inflammatory infertility through bioinformatics analysis. Pharmacological analysis identified 343 corresponding targets for 14 TCM herbs in the prescription, with 326 common targets shared with tubal inflammatory infertility. The top 20 key targets identified through Cytoscape software analysis were AKT1, TNF, IL6, IL1B, TP53, EGFR, ESR1, PTGS2, STAT3, SRC, CASP3, BCL2, NFKB1, HIF1A, MMP9, CTNNB1, IFNG, ICAM1, STAT1, and NFKBIA. The top 10 signaling pathways involved in the treatment of tubal inflammatory infertility were WP5434, hsa05417, hsa05208, WP5420, WP5470, WP2882, WP4298, hsa05207, WP4396, and hsa05418. The TGF-B1/Smads/CTGF signaling pathway was only involved in Cancer pathways and Spinal cord injury. The only two pathways involved in TGF-B1/Smads/CTGF signal transduction are Cancer pathways and Spinal cord injury. TGF-B1 is a secreted homologous protein with a wide range of biological activities, which can regulate physiological processes such as cell proliferation, differentiation, migration and apoptosis. The homeostasis of the fallopian tube requires dynamic regulation by heterogeneous cell populations. However, this balance is disrupted in fallopian tube-related infertility and ovarian cancer. Among 59,738 fallopian tube cells, epithelial, stromal, and immune cells predominated. Notably, 798 epithelial and stromal cells were found to potentially activate the TGF-β1 signaling pathway, subsequently inducing fibrotic pathological changes, During tubal inflammatory processes, TGF-β1 may participate in inflammatory regulation and compromise normal tubal structure and function. Research by Lecker LSM et al. [ 18 ] demonstrated that CD163-positive macrophages in the stroma among fallopian tubes, fimbriae, and ovaries produce TGF-β1 during pathological processes, thereby modulating immune mechanisms and promoting tumor cell proliferation. However, conflicting evidence exists. Van der Ploeg P et al. [ 19 ] compared ciliated epithelial cells from 17 premenopausal patients with benign gynecological diseases and 8 postmenopausal patients, finding no significant activation of TGF-β/Smad proteins—key signaling molecules in the TGF-β1 pathway—in fimbrial epithelium. Xijiao HU et al. [ 20 ] pointed out through rat experiments that the TGF-B1/p38 mitogen-activated protein kinase signaling pathway can aggravate the lesions of chronic salpingitis, and that electroacupuncture can downregulate this signaling pathway, which indirectly indicates that TGF-B1 is still involved in fallopian tube lesions. Smads protein is a key signaling molecule in the TGF-B signal transduction pathway, which can transmit signals from the cell membrane to the cell nucleus [ 21 , 22 ]. Under the stimulation of TGF-B1, Smads protein will be phosphorylated and activated, and then form a complex and transfer to the cell nucleus to regulate the transcription of target genes [ 23 , 24 ]. In patients with tubal inflammation infertility, the TGF-B1/Smads signal transduction pathway may be abnormal, leading to pathological changes such as tubal fibrosis, tubal stenosis or occlusion. CTGF is a pro-fibrotic factor that can promote the synthesis and deposition of extracellular matrix and participate in the fibrosis process of tissues. TGF-B1 can induce the expression of CTGF, and CTGF can further enhance the pro-fibrotic effect of TGF-B1 [ 25 , 26 ]. In conclusion, pharmacological evidence from this study indicates that the therapeutic strategy of promoting blood circulation, removing blood stasis, and unblocking collaterals can inhibit TGF-β1 activity by acting on relevant targets, thereby attenuating its promotive effect on tubal fibrosis. This is achieved through regulation of Smad protein phosphorylation, which modulates their function within the TGF-β1 signaling pathway and ultimately alleviates fibrotic damage in the fallopian tubes. Furthermore, suppression of CTGF expression or activity reduces its contribution to fibrotic progression, facilitating restoration of normal tubal structure and function. Thus, the method of promoting blood circulation, removing blood stasis, and unblocking collaterals regulates tubal inflammatory infertility through a multi-target and multi-pathway approach, including intervention in the TGF-β1/Smads/CTGF signaling axis. Declarations Acknowledgements We would like to thank our research team, all the researchers for their dedication, support and hard work. Availability of data and materials The datasets generated and/or analysed during the current study are available in the following repository.Using keywords such as "Tubal factor infertility," "Tubal inflammation," and "infertility," a comprehensive search was conducted in the OMIM（https://omim.org/）, RefSeq Gene（https://www.ncbi.nlm.nih.gov/refseq/）, TTD（https://db.idrblab.net/）, CTD（https://ctdbase.org/）, and Genecards databases（https://www.genecards.org/） to obtain gene expression information related to tubal infertility. Funding This study was supported by Hubei Knowledge Innovation Project [number 2019CFC917] and Wuhan Municipal Health Commission under Grant [number WZ24B79]. Authorship contribution statement Shengpan Jiang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing-original draft，Funding acquisition. Hui Dong: Conceptualization, Data curation, Formal analysis, Methodology, Writing-review & editing. Tao Zhou: Conceptualization, Data curation, Methodology, Writing- review & editing. Yiqing Tan: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Funding acquisition, Project administration, Supervision, Writing-review & editing. Ethical considerations There are no animal experiments in this article. All procedures involving human subjects were carried out in compliance with the 2013 revision of the Helsinki Declaration as well as the institutional and/or national research committee's ethical guidelines. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests References Gonullu DC, Huang XM, Robinson LG et al (2022) Tubal factor infertility and its impact on reproductive freedom of African American women. 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Cell Mol Biol (Noisy-le-grand). 69(4):101–104 Published 2023 Apr 30. 10.14715/cmb/2023.69.4.15 Tables Table 1 to 13 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files table1.docx table2.docx table3.docx table4.docx table5.docx table6.docx table7.docx table8.docx table9.docx table10.docx table11.docx table12.docx table13.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7454231","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508315960,"identity":"df91770b-c304-478e-a44a-444475f772bd","order_by":0,"name":"Shengpan Jiang","email":"","orcid":"","institution":"Wuhan Third Hospital (Tongren Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Shengpan","middleName":"","lastName":"Jiang","suffix":""},{"id":508315961,"identity":"03dfa03e-5226-461f-9f06-49f36b097fa6","order_by":1,"name":"Hui Dong","email":"","orcid":"","institution":"Wuhan Third Hospital (Tongren 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Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYLCCBAYJBgZm/o8PPhjY2BFUzQPXwt5gbDijIC2ZOC0Q1gEzaZ4PhxgbCGmxZz97TOJBmUWefERCgrSNwQFmBvbDRzfgtYUnL00i4ZxEseGNhAPGOQZ3+Bh40tJu4HdYjplEYptE4sYZiQ3JOQbPmBkkeMzwa+F/A9OSzHDYwuAwYwNBLRJQW+bzHGNsZiBKy403xhZAvyRuYO9hZuwxSEtmI+QX9v4cw5s/yuoS5zfzsP/48cfGjp/98DG8WoCARYKBjYHB4ACUy0ZAOQgwfwApk28gQukoGAWjYBSMTAAA49ZIXqTP7n0AAAAASUVORK5CYII=","orcid":"","institution":"Wuhan Third Hospital (Tongren Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Yiqing","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2025-08-25 13:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7454231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7454231/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90661025,"identity":"422c235d-a941-4cdd-bf96-30eb158f45d9","added_by":"auto","created_at":"2025-09-05 11:28:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130005,"visible":true,"origin":"","legend":"\u003cp\u003eOperational set plot of differentiated gene analysis for tubal infertility in the GEO database.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/5e8ea79b77572cacb904376a.png"},{"id":90661415,"identity":"4a49b356-9693-4dad-8cfb-7327979cd78f","added_by":"auto","created_at":"2025-09-05 11:36:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57655,"visible":true,"origin":"","legend":"\u003cp\u003eData plots of tubal inflammatory infertility disease targets.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/7d24c79bf14f376a7cf6e04c.png"},{"id":90661417,"identity":"155de336-9a7b-4358-b3f5-6d07d2ae1e5c","added_by":"auto","created_at":"2025-09-05 11:36:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3329244,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram of top 100 key TCM-ingredient-target method by activating blood circulation and removing blood stasis under cytoHubba plug-in.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/f62a5b9e9b60288243920278.png"},{"id":90661029,"identity":"657eadb3-970b-4b6f-a15b-8a099cf0c2d4","added_by":"auto","created_at":"2025-09-05 11:28:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52254,"visible":true,"origin":"","legend":"\u003cp\u003eCommon targets.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/7d87215ff19ce0f8cc333243.png"},{"id":90661016,"identity":"ba8c4a2e-3c25-4fdc-a02e-98fc56c01ee2","added_by":"auto","created_at":"2025-09-05 11:28:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3142442,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction diagram of blood circulation and fallopian tube inflammatory infertility under STRING database.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/f5b83401531fa26430b2bc96.png"},{"id":90661434,"identity":"f7ea71be-5b72-4ed6-8d9a-c14eebedd4ae","added_by":"auto","created_at":"2025-09-05 11:36:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2232639,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction diagram between blood circulation method and tubal inflammatory infertility under Cytoscape3.10.0 software.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/aaa5f989cf2b37fb1f4d9b4b.png"},{"id":90661008,"identity":"c8ec55d9-aa7c-432a-b4d7-83a46167bb0c","added_by":"auto","created_at":"2025-09-05 11:28:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1490955,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction diagram between 51 to 100 and 101, degree under Cytoscape3.10.0.\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/e8281c18f290fed055370b62.png"},{"id":90661010,"identity":"8b40cf9e-ac82-4085-a54f-4b74d8c76c98","added_by":"auto","created_at":"2025-09-05 11:28:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":810861,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction diagram of Finfertility and front 20 under Cytoscape3.10.0 software.\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/5a553bb88b5eb46f914730fb.png"},{"id":90661421,"identity":"61815dd1-83f1-4ad2-84a1-ddd4a1580b85","added_by":"auto","created_at":"2025-09-05 11:36:56","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":87274,"visible":true,"origin":"","legend":"\u003cp\u003eThe GO functional enrichment analysis of the potential targets.\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/365054cdb393b233e253e66f.png"},{"id":90661012,"identity":"4cd12628-d83f-43d8-826c-318c915f0740","added_by":"auto","created_at":"2025-09-05 11:28:56","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":611332,"visible":true,"origin":"","legend":"\u003cp\u003eInvolves circle disassembly of TGF-B1 / Smads / CTGF signaling pathway clusters.\u003c/p\u003e","description":"","filename":"110.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/bb50198ed1b0eea6045edbb8.png"},{"id":90661416,"identity":"7272181c-3cf9-44a5-911d-d8ff2c74e0fc","added_by":"auto","created_at":"2025-09-05 11:36:55","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":6381954,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking diagram of beta-sitosterol and TGFB1.\u003c/p\u003e","description":"","filename":"111.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/6d7be9b531ee66ad95394eb7.png"},{"id":90661436,"identity":"05ab4262-089f-493e-8591-75fd0cbf72d8","added_by":"auto","created_at":"2025-09-05 11:36:57","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":5461564,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking diagram of Quercetin and TGFB1.\u003c/p\u003e","description":"","filename":"112.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/b75534e048ea09e842f702eb.png"},{"id":90661024,"identity":"48bc11d2-f77b-4e87-9ffb-73a462d1faa2","added_by":"auto","created_at":"2025-09-05 11:28:56","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":10910024,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking diagram of Formononetin and ESR2.\u003c/p\u003e","description":"","filename":"113.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/790b343360d8f46b2a5527e4.png"},{"id":90661430,"identity":"7bb4cfd1-835c-40b8-ade6-73d1a3acbe7b","added_by":"auto","created_at":"2025-09-05 11:36:57","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":6422782,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking diagram of isorhamnetin and ESR2.\u003c/p\u003e","description":"","filename":"114.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/670366170dc64e95631d0f86.png"},{"id":90661439,"identity":"62d9130a-9821-490f-9ec4-8135d0918ae4","added_by":"auto","created_at":"2025-09-05 11:36:58","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":4441163,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking diagram of Quercetin and TNF.\u003c/p\u003e","description":"","filename":"115.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/f546a16ddb6ead3aaaedb675.png"},{"id":90661443,"identity":"e5958a6d-58bc-4d0c-8a4f-c46c8a1f23d8","added_by":"auto","created_at":"2025-09-05 11:36:58","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":3159783,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking diagram of Baicalein and CASP3.\u003c/p\u003e","description":"","filename":"116.png","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/92bb0f7926adb4174176d821.png"},{"id":96917842,"identity":"7e0ab784-fc6b-4bc5-a01c-b5034e2f7ef2","added_by":"auto","created_at":"2025-11-27 14:10:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":59754983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/6057b960-132c-4f0b-8229-eeae68367753.pdf"},{"id":90661002,"identity":"1bf82941-572f-4f3c-82d0-89384a02ea17","added_by":"auto","created_at":"2025-09-05 11:28:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17847,"visible":true,"origin":"","legend":"","description":"","filename":"table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/3234963e50a39fe55c7a2aba.docx"},{"id":90661001,"identity":"a626a42d-fbc7-41ae-8b4c-2b2d2a458262","added_by":"auto","created_at":"2025-09-05 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11:28:56","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":17233,"visible":true,"origin":"","legend":"","description":"","filename":"table8.docx","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/31d6250ca05931dfef68f9bb.docx"},{"id":90661042,"identity":"ddbbb37b-0b68-49fb-b8ec-7c23fba98019","added_by":"auto","created_at":"2025-09-05 11:28:58","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":16946,"visible":true,"origin":"","legend":"","description":"","filename":"table9.docx","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/c8ccf5fa011883231aa83a4a.docx"},{"id":90661423,"identity":"c4d1bc83-1034-41e3-990c-ce04b313803d","added_by":"auto","created_at":"2025-09-05 11:36:56","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":17212,"visible":true,"origin":"","legend":"","description":"","filename":"table10.docx","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/3774dd9fcea798c49091c70f.docx"},{"id":90661006,"identity":"cc71e36c-c9e8-404f-a817-14bcd537a0ec","added_by":"auto","created_at":"2025-09-05 11:28:55","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":17085,"visible":true,"origin":"","legend":"","description":"","filename":"table11.docx","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/e114f6e53085110fa75ccd11.docx"},{"id":90661019,"identity":"cb1bcea8-a7e2-4337-b4bd-ffb92223ef65","added_by":"auto","created_at":"2025-09-05 11:28:56","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":17190,"visible":true,"origin":"","legend":"","description":"","filename":"table12.docx","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/300ce5d97a4c18b7464a9c73.docx"},{"id":90661445,"identity":"69d7ac80-bb3c-4b22-9d37-314b85f061a6","added_by":"auto","created_at":"2025-09-05 11:36:58","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":17080,"visible":true,"origin":"","legend":"","description":"","filename":"table13.docx","url":"https://assets-eu.researchsquare.com/files/rs-7454231/v1/81401a977979006934fe0f0c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Mechanism of Regulating Tubal Inflammatory Infertility through TGF- B1/Smads/CTGF Signal Transduction Pathway by Promoting Blood Circulation, Removing Stasis, and Tonifying collaterals Method Based on Bioinformatics and Molecular Docking","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTubal factor infertility (TFI) is one of the common causes of infertility in women of reproductive age. Its pathological basis mainly includes tubal inflammation, fibrosis, and obstruction [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Inflammatory factors play a significant role in the process of tubal inflammation and fibrosis, with transforming growth factor-beta 1 (TGF-B1) and connective tissue growth factor (CTGF) being key players [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These growth factors regulate a series of signaling pathways, including the TGF-B1/Smads/CTGF pathway, which are involved in the occurrence, development, and fibrosis of tubal inflammation, ultimately affecting the normal function of the fallopian tubes and leading to infertility. The method of promoting blood circulation, removing blood stasis, and unblocking collaterals is one of the commonly used TCM approaches for treating tubal inflammatory infertility. This method improves local blood circulation and the microenvironment of the fallopian tubes by regulating qi and blood, promoting blood circulation, and unblocking meridians, thereby reducing inflammation, promoting tissue repair, and restoring tubal function [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the specific mechanism by which this method treats tubal inflammatory infertility is not fully understood. In recent years, bioinformatics and molecular docking techniques have been widely applied in TCM research [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Bioinformatics can integrate and analyze large amounts of biological data, revealing interactions between genes, proteins, and signaling pathways, providing new perspectives and methods for studying the mechanisms of TCM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Molecular docking can simulate molecular interactions, predict the binding ability and affinity between drugs and targets, and provide strong support for drug screening and mechanism research. Based on this background, this study uses bioinformatics and molecular docking to explore the mechanism by which the method of promoting blood circulation, removing blood stasis, and unblocking collaterals regulates tubal inflammatory infertility through the TGF-B1/Smads/CTGF signaling pathway. Specifically, this study will first use bioinformatics to analyze the gene and protein expression profiles related to tubal inflammatory infertility, as well as the key nodes and regulatory mechanisms of the TGF-B1/Smads/CTGF signaling pathway. Then, molecular docking will be used to predict the binding ability and affinity between key components of the method and key targets in the TGF-B1/Smads/CTGF signaling pathway, revealing the regulatory effect of the method on this pathway. This will further elucidate the biological mechanism of the method in treating tubal inflammatory infertility, providing scientific evidence and theoretical support for the application of TCM in this field. It will also offer new ideas and methods for the treatment of tubal inflammatory infertility, potentially improving treatment outcomes and quality of life for patients.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Disease Target Screening\u003c/h2\u003e\u003cp\u003eThis study systematically identified gene targets related to TFI by integrating multiple bioinformatics databases. Using keywords such as \"Tubal factor infertility,\" \"Tubal inflammation,\" and \"infertility,\" a comprehensive search was conducted in the OMIM, RefSeq Gene, TTD, CTD, and Genecards databases to obtain gene expression information related to tubal infertility. After integrating information from these five databases, duplicate gene targets were removed to ensure each target was retained only once, resulting in a disease target set for tubal infertility.\u003c/p\u003e\u003cp\u003e2.2 Screening of Effective Components and Target Prediction for the Modified Prescription of Promoting Blood Circulation, Removing Blood Stasis, and Unblocking Collaterals\u003c/p\u003e\u003cp\u003eTo screen the effective components and predict their targets for the modified prescription of promoting blood circulation, removing blood stasis, and unblocking collaterals, the TCMSP database was used. The prescription includes: Angelica sinensis (15g), Rehmannia glutinosa (15g), Peach kernel (15g), Carthamus tinctorius (10g), Paeonia lactiflora (10g), Bupleurum chinense (10g), Ligusticum chuanxiong (10g), Glycyrrhiza uralensis (6g), Manis pentadactyla (9g), Luffa cylindrica (15g), Retinervus Luffae Fructus (10g), Gleditsia sinensis (10g), Melia toosendan (10g), and Millettia dielsiana (15g). The database extracted compound information for each herb, and effective active components were screened based on oral bioavailability (\u0026ge;\u0026thinsp;30%) and drug-likeness (\u0026ge;\u0026thinsp;0.18). Target proteins and gene information for these active components were also collected. To standardize the data format, the Uniprot database was used to convert drug target IDs into gene symbols using \"Homo sapiens\" as the keyword. Based on this, a TCM-component-target database was established to support subsequent network diagram construction and topological analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Drawing the TCM-Component-Gene Network Diagram\u003c/h2\u003e\u003cp\u003eTo visually demonstrate the potential mechanism of the modified prescription in treating TFI, the R software package \"VennDiagram\" was used to perform intersection analysis between drug genes and disease genes. The resulting intersection genes were considered potential therapeutic targets for TFI. Perl language was used to organize the intersection gene network and type files, and Cytoscape software was used for visualization, drawing the TCM-component-gene network diagram. This diagram clearly shows how the effective components in the prescription act on specific gene targets to treat TFI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 PPI Network Construction, Core Network Construction, and Topological Analysis\u003c/h2\u003e\u003cp\u003eTo further understand the interaction relationships between the potential therapeutic genes of the modified prescription for TFI, these genes were input into the STRING database. After selecting Homo sapiens as the organism, a protein-protein interaction (PPI) network was obtained. The PPI network information was then imported into Cytoscape software, and the CytoNCA plugin was used for topological analysis. This analysis identified key nodes and pathways in the core network, which may play important roles in the treatment of TFI by the modified prescription.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Differential Gene Analysis in the GEO Database\u003c/p\u003e\n\u003cp\u003eBy querying the GEO database for gene expression profiles related to tubal infertility, the GSE262037 chip expression dataset and GPL21827 platform file were obtained, including 3 normal samples and 3 tubal inflammation samples. Differential genes were selected based on an adjusted P-value \u0026lt; 0.05 and a fold change \u0026ge; 2 (|log2FC| \u0026ge; 1.0), resulting in 241 differential genes, including 143 upregulated genes and 94 downregulated genes. The volcano plot of differential genes is shown in Figure 1, where green represents downregulated genes, red represents upregulated genes, and black represents genes with no significant expression changes.\u003c/p\u003e\n\u003cp\u003e3.2 Statistical Analysis of Disease Targets for Tubal Inflammatory Infertility\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTargets related to tubal inflammatory infertility were collected from the five databases mentioned above（Figure 2）. The CTD database contained the most targets (37,144), followed by Genecards (455), RefSeq Gene (212), TTD (22), and OMIM (15). After removing duplicates, a total of 37,176 disease targets for tubal inflammatory infertility were obtained.\u003c/p\u003e\n\u003cp\u003e3.3 Effective Components and Predicted Targets of the Method of Promoting Blood Circulation, Removing Blood Stasis, and Unblocking Collaterals\u003c/p\u003e\n\u003cp\u003eThe 14 TCM herbs in the prescription were screened for components based on oral bioavailability (\u0026ge;30%) and drug-likeness (\u0026ge;0.18). Manis pentadactyla and Retinervus Luffae Fructus were not available in the TCMSP database and were collected from the ETCM database, PubChem, and TCM websites. After removing duplicates, the prescription had 343 unique targets. The top 100 nodes were analyzed using the cytoHubba plugin, identifying 69 key components, 6 key herbs, and 25 key targets in Figure 3.Please see the below Table 1 2, 3, and 4.\u003c/p\u003e\n\u003cp\u003e3.4 Common Targets Between the promoting blood circulation, removing blood stasis and dredging collateral method and Tubal Inflammatory Infertility\u003c/p\u003e\n\u003cp\u003eCollect the targets of tubal inflammatory infertility disease and the corresponding targets of the promoting blood circulation, removing blood stasis and dredging collateral method into the Venny2.1 online tool for interaction analysis, resulting in 326 common targets (see Figure 4). Input the obtained common targets into the STRING database, selecting the human species and setting the interaction score threshold, to filter out the 323 most relevant and reliable protein interaction pairs. After completing the settings, click the search button to generate a protein-protein interaction (PPI) network related to the promoting blood circulation, removing blood stasis and dredging collateral method and tubal inflammatory infertility targets using the STRING database. The results show 323 nodes (with 3 not recognized in the database), 6,279 edges, and a clustering coefficient of 0.585. After completing these steps, download the TSV file generated by the STRING database, which contains detailed information about all nodes (proteins) and edges (interactions) in the network. This information serves as the foundation for subsequent visualization in Cytoscape software. To simplify the network and focus on core interactions, isolated nodes can be hidden. Isolated nodes refer to those that exist in isolation in the network and do not form interactions with other nodes. By removing isolated nodes, the protein interactions become more specific. As shown in Figure 5, this is the initial graph from the STRING database, which contains isolated proteins. Download the TSV file and import it into Cytoscape 3.10.0 to hide the isolated nodes. Through degree value analysis, four target network circles are created based on degree values of 0\u0026ndash;20, 21\u0026ndash;50, 51\u0026ndash;100, and \u0026ge;101, from the outer to the inner layer (as shown in Figure 6). To obtain the top 20 important targets, the two outer circles with degree values of 0\u0026ndash;20 and 21\u0026ndash;50 are removed, resulting in Figure 7. Then, using the cytoHubba plugin, the top 20 important common targets are identified (as shown in Figure 8), including AKT1 (degree: 167), TNF (degree: 158), IL6 (degree: 157), IL1B (degree: 148), TP53 (degree: 148), EGFR (degree: 136), ESR1 (degree: 133), PTGS2 (degree: 133), STAT3 (degree: 131), SRC (degree: 129), CASP3 (degree: 129), BCL2 (degree: 124), NFKB1 (degree: 122), HIF1A (degree: 122), MMP9 (degree: 120), CTNNB1 (degree: 119), IFNG (degree: 107), ICAM1 (degree: 93), STAT1 (degree: 90), and NFKBIA (degree: 80).\u003c/p\u003e\n\u003cp\u003e3.5 GO Functional Enrichment Analysis and KEGG Pathway Analysis of Potential Targets\u003c/p\u003e\n\u003cp\u003eThe 326 common targets were analyzed using the Metascape database for GO functional enrichment and KEGG pathway analysis. The results showed enrichment in responses to xenobiotic stimuli, cellular responses to lipids, cellular responses to nitrogen compounds, neurotransmitter receptor activity, responses to molecules of bacterial origin, regulation of hormone levels, responses to alcohol, responses to nutrient levels, positive regulation of programmed cell death, responses to decreased oxygen levels, cellular responses to cytokine stimuli, regulation of system processes, gland development, steroid metabolic processes, responses to radiation, regulation of inflammatory responses, responses to oxidative stress, intracellular receptor signaling pathways, regulation of apoptotic signaling pathways, and regulation of reactive oxygen species metabolic processes (see Table 5, Figure 9).\u003c/p\u003e\n\u003cp\u003eThrough signaling pathway analysis, the following pathway clusters were identified: cancer pathways, lipid and atherosclerosis, chemical carcinogenesis\u0026mdash;reactive oxygen species, ADHD and autism spectrum disorder (ASD) pathways, acute viral myocarditis, chemical carcinogenesis\u0026mdash;receptor activation, non-alcoholic fatty liver disease, fluid shear stress and atherosclerosis, NF-\u0026kappa;B survival signaling pathway induced by photodynamic therapy, receptor complexes, androgen receptor networks in prostate cancer, neuroinflammation, and glutamatergic signaling. Among these, the TGF-\u0026beta;1/Smads/CTGF signaling pathway is only involved in cancer pathways and spinal cord injury. See Table 6 for details.\u003c/p\u003e\n\u003cp\u003e3.6 Analysis of the Regulation of Tubal Inflammatory Infertility by the Method of Promoting Blood Circulation, Removing Blood Stasis, and Unblocking Collaterals through the TGF-B1/Smads/CTGF Signaling Pathway\u003c/p\u003e\n\u003cp\u003e3.6.1 Pathway Analysis\u003c/p\u003e\n\u003cp\u003eThe Cancer pathways and Spinal cord injury signaling pathway clusters were dissected to identify genes involved in the TGF-B1/Smads/CTGF signaling pathway. Both pathways clearly involved TGF-B1/Smads/CTGF signaling, and some pathways, although not explicitly involving TGF-B1/Smads/CTGF, were related to TGF-B1, including WP2431, hsa05140, WP2328, WP5095, hsa05321, WP560, WP4816, WP5039, M54, M36, WP5473, hsa04060, and M5883. See\u0026nbsp;Figure 10.\u003c/p\u003e\n\u003cp\u003e3. 6.2 Molecular Docking Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTypical targets of tubal inflammatory infertility (e.g., TGFB1, ESR2) were selected for molecular docking with common chemical molecules in the prescription (e.g., MOL000358, MOL000098 for TGFB1; MOL000392, MOL000354, MOL004598, MOL000490, MOL004609, MOL002135, MOL002565 for ESR2). The PDB files of the targets were obtained from the RCSB database, and the SDF files of the chemical molecules were obtained from PubChem. AutoDock was used for blind docking to identify binding sites, binding energy, and residual structures. The results of molecular docking between some drug components and common targets are shown in Tables 7-13 and Figure 11-16.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn TCM, tubal inflammatory infertility is not uniformly named but is classified under categories such as \"abdominal mass,\" \"infertility,\" \"cessation of menstruation,\" and \"abdominal pain in women.\" Its formation mechanism is related to blood stasis, meridian blockage, and pathogenic factors entering the uterus, leading to infertility [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This study identified 37,176 disease targets for tubal inflammatory infertility through bioinformatics analysis. Pharmacological analysis identified 343 corresponding targets for 14 TCM herbs in the prescription, with 326 common targets shared with tubal inflammatory infertility. The top 20 key targets identified through Cytoscape software analysis were AKT1, TNF, IL6, IL1B, TP53, EGFR, ESR1, PTGS2, STAT3, SRC, CASP3, BCL2, NFKB1, HIF1A, MMP9, CTNNB1, IFNG, ICAM1, STAT1, and NFKBIA. The top 10 signaling pathways involved in the treatment of tubal inflammatory infertility were WP5434, hsa05417, hsa05208, WP5420, WP5470, WP2882, WP4298, hsa05207, WP4396, and hsa05418. The TGF-B1/Smads/CTGF signaling pathway was only involved in Cancer pathways and Spinal cord injury.\u003c/p\u003e\u003cp\u003eThe only two pathways involved in TGF-B1/Smads/CTGF signal transduction are Cancer pathways and Spinal cord injury. TGF-B1 is a secreted homologous protein with a wide range of biological activities, which can regulate physiological processes such as cell proliferation, differentiation, migration and apoptosis. The homeostasis of the fallopian tube requires dynamic regulation by heterogeneous cell populations. However, this balance is disrupted in fallopian tube-related infertility and ovarian cancer. Among 59,738 fallopian tube cells, epithelial, stromal, and immune cells predominated. Notably, 798 epithelial and stromal cells were found to potentially activate the TGF-β1 signaling pathway, subsequently inducing fibrotic pathological changes, During tubal inflammatory processes, TGF-β1 may participate in inflammatory regulation and compromise normal tubal structure and function. Research by Lecker LSM et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] demonstrated that CD163-positive macrophages in the stroma among fallopian tubes, fimbriae, and ovaries produce TGF-β1 during pathological processes, thereby modulating immune mechanisms and promoting tumor cell proliferation. However, conflicting evidence exists. Van der Ploeg P et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] compared ciliated epithelial cells from 17 premenopausal patients with benign gynecological diseases and 8 postmenopausal patients, finding no significant activation of TGF-β/Smad proteins\u0026mdash;key signaling molecules in the TGF-β1 pathway\u0026mdash;in fimbrial epithelium. Xijiao HU et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] pointed out through rat experiments that the TGF-B1/p38 mitogen-activated protein kinase signaling pathway can aggravate the lesions of chronic salpingitis, and that electroacupuncture can downregulate this signaling pathway, which indirectly indicates that TGF-B1 is still involved in fallopian tube lesions. Smads protein is a key signaling molecule in the TGF-B signal transduction pathway, which can transmit signals from the cell membrane to the cell nucleus [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Under the stimulation of TGF-B1, Smads protein will be phosphorylated and activated, and then form a complex and transfer to the cell nucleus to regulate the transcription of target genes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In patients with tubal inflammation infertility, the TGF-B1/Smads signal transduction pathway may be abnormal, leading to pathological changes such as tubal fibrosis, tubal stenosis or occlusion. CTGF is a pro-fibrotic factor that can promote the synthesis and deposition of extracellular matrix and participate in the fibrosis process of tissues. TGF-B1 can induce the expression of CTGF, and CTGF can further enhance the pro-fibrotic effect of TGF-B1 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn conclusion, pharmacological evidence from this study indicates that the therapeutic strategy of promoting blood circulation, removing blood stasis, and unblocking collaterals can inhibit TGF-β1 activity by acting on relevant targets, thereby attenuating its promotive effect on tubal fibrosis. This is achieved through regulation of Smad protein phosphorylation, which modulates their function within the TGF-β1 signaling pathway and ultimately alleviates fibrotic damage in the fallopian tubes. Furthermore, suppression of CTGF expression or activity reduces its contribution to fibrotic progression, facilitating restoration of normal tubal structure and function. Thus, the method of promoting blood circulation, removing blood stasis, and unblocking collaterals regulates tubal inflammatory infertility through a multi-target and multi-pathway approach, including intervention in the TGF-β1/Smads/CTGF signaling axis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank our research team, all the researchers for their dedication, support and hard work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the following repository.Using keywords such as \u0026quot;Tubal factor infertility,\u0026quot; \u0026quot;Tubal inflammation,\u0026quot; and \u0026quot;infertility,\u0026quot; a comprehensive search was conducted in the OMIM（https://omim.org/）, RefSeq Gene（https://www.ncbi.nlm.nih.gov/refseq/）, TTD（https://db.idrblab.net/）, CTD（https://ctdbase.org/）, and Genecards databases（https://www.genecards.org/）\u0026nbsp;to obtain gene expression information related to tubal infertility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Hubei Knowledge Innovation Project [number 2019CFC917] and \u0026nbsp;Wuhan Municipal Health Commission under Grant [number WZ24B79].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShengpan Jiang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing-original draft，Funding acquisition. Hui Dong: Conceptualization, Data curation, Formal analysis, Methodology, Writing-review \u0026amp; editing. Tao Zhou: Conceptualization, Data curation, Methodology, Writing- review \u0026amp; editing. Yiqing Tan: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Funding acquisition, Project administration, Supervision, Writing-review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no animal experiments in this article. All procedures involving human subjects were carried out in compliance with the 2013 revision of the Helsinki Declaration as well as the institutional and/or national research committee\u0026apos;s ethical guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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 no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGonullu DC, Huang XM, Robinson LG et al (2022) Tubal factor infertility and its impact on reproductive freedom of African American women. 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Cell Mol Biol (Noisy-le-grand). 69(4):101\u0026ndash;104 Published 2023 Apr 30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14715/cmb/2023.69.4.15\u003c/span\u003e\u003cspan address=\"10.14715/cmb/2023.69.4.15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 13 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioinformatics, Molecular docking method, The method of promoting blood circulation and removing blood stasis and unblocking collaterals, TGF-B1/Smads/CTGF signal, Transduction pathway, Regulation, Tubal inflammatory infertility","lastPublishedDoi":"10.21203/rs.3.rs-7454231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7454231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eBased on bioinformatics and molecular docking, this study explores the mechanism of promoting blood circulation, removing blood stasis, and unblocking collaterals through the TGF-B1/Smads/CTGF signaling pathway to regulate tubal inflammatory infertility.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eCollect data from OMIM database, RefSeq Gene database, TTD database, CTD database, and Genecards database. Collect 12 medicinal ingredients and targets of modified prescriptions for promoting blood circulation, removing blood stasis, and unblocking collaterals using the TCMSP database. Collect common targets using Venn diagram and perform GO and KEGG analysis. Select relevant representative targets and corresponding components for molecular docking.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults：\u003c/strong\u003e\u003c/em\u003eThere are 37,176 target points for tubal inflammatory infertility disease. Through pharmacological analysis, there are 343 corresponding targets for 14 related traditional Chinese medicine herbs using the method of promoting blood circulation, removing blood stasis, and unblocking collaterals. There are 326 common targets with tubal inflammatory infertility disease. The top 20 key targets including AKT1, TNF, IL6, IL1B, TP53, EGFR, ESR1, PTGS2, STAT3, SRC, CASP3, BCL2, NFKB1, HIF1A, MMP9, CTNNB1, IFNG, ICAM1, STAT1, and NFKBIA were obtained through Cytoscape software analysis. It involves signaling pathways such as WP5434, hsa05417, hsa05208, WP5420, WP5470, WP2882, WP4298, hsa05207, WP4396, hsa05418, etc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eThe method of promoting blood circulation, removing blood stasis, and unblocking collaterals can regulate tubal inflammatory infertility through multiple targets and pathways, including the TGF-B1/Smads/CTGF signaling pathway.\u003c/p\u003e","manuscriptTitle":"Exploring the Mechanism of Regulating Tubal Inflammatory Infertility through TGF- B1/Smads/CTGF Signal Transduction Pathway by Promoting Blood Circulation, Removing Stasis, and Tonifying collaterals Method Based on Bioinformatics and Molecular Docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-05 11:28:50","doi":"10.21203/rs.3.rs-7454231/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":"c127e20d-0172-4011-83a1-baa3180146bd","owner":[],"postedDate":"September 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T10:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-05 11:28:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7454231","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7454231","identity":"rs-7454231","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}