The Mechanism of Rose in Treating Sjögren's Syndrome Based on Network Pharmacology and Molecular Docking

The Mechanism of Rose in Treating Sjögren's Syndrome Based on Network Pharmacology 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 The Mechanism of Rose in Treating Sjögren's Syndrome Based on Network Pharmacology and Molecular Docking Xi Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4793586/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 Background: Modern medicine has yet to cure the xerostomia and other symptoms caused by the early onset of Sjögren's Syndrome (SS). Rose, a common flower used in traditional Chinese medicine, is investigated in this study using network pharmacology and molecular docking techniques to explore its potential mechanisms of action against SS. Methods: The active components and targets of rose were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The genes encoding these targets were identified using the UniProt database. Additionally, SS-related targets were identified from the GeneCards and OMIM databases. By intersecting the compound targets with SS targets, the predicted targets for rose in the treatment of SS were obtained. A "candidate compound-target" network was constructed using Cytoscape 3.10.2, and a protein-protein interaction network was built. Further analysis of active compounds and their targets was performed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using R software. Finally, molecular docking techniques were employed to validate the affinity between the candidate compounds and key targets. Results: Quercetin, beta-carotene, beta-sitosterol, and demethoxycapillarisin in rose interacted with IL6, TNF, AKT1, ALB, IL1B, TP53, JUN, TGFB1, BCL2, and ESR1. These findings indicate that rose exerts therapeutic effects on peripheral glandular damage in SS and its associated cardiovascular diseases and tumorigenesis through anti-inflammatory and antioxidant pathways. Conclusion: From a network pharmacology perspective, this study systematically identified the main active ingredients, targets, and specific mechanisms of rose in treating SS, providing a theoretical basis and research direction for further exploration of rose's therapeutic mechanisms in SS. Sjögren's Syndrome Rose molecular docking network pharmacology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Sjögren's Syndrome (SS) is a complex systemic autoimmune disease characterized by dysfunction of the exocrine glands, particularly the salivary and lacrimal glands, and by chronic lymphocytic infiltration of the glandular parenchyma 1 . This results in dryness of the major mucosal surfaces, such as the mouth, eyes, nose, pharynx, larynx, and vagina 2 . SS is marked by lymphocytic infiltration of exocrine glands and a variety of systemic manifestations. It can occur as a primary disease (primary) or in association with other autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, dermatomyositis, or systemic sclerosis 3 . In primary SS, the female-to-male ratio is 9:1, with a peak incidence around the age of fifty 4 , the prevalence in the general population ranges from approximately 0.02–2.7% 5 . The systemic manifestations of SS are common and may involve various domains such as skin, kidneys, joints, muscles, peripheral nervous system, central nervous system, hematology, and glands 6 . The EULAR SS Disease Activity Index categorizes systemic disease activity from low to high across 12 domains (skin, kidneys, joints, muscles, peripheral nervous system, central nervous system, hematology, glands, constitutional, lymphadenopathy, lungs, biology) 7 . The most common and active the EULAR SS Disease Activity Index domains are joints (56%), glands (34%), lungs (15%), and skin (13%) 8 . Common immunomodulators, including hydroxychloroquine, prednisone, methotrexate, mycophenolate mofetil, and azathioprine, often fail to improve functional parameters of the salivary and lacrimal glands in clinical trials for primary SS 9,10 . Additionally, the use of immunosuppressants in the early stages of SS-related xerostomia is not recommended due to the risks of immune disorders, tumors, and liver and kidney damage 11 . Recent studies have shown that traditional Chinese medicine can effectively reduce inflammatory markers such as IL-6, IL-10, erythrocyte sedimentation rate, and C-reactive protein in patients with SS, thereby alleviating local symptoms and preventing further disease progression 12 . Rosa contain high concentrations of phenolic compounds 13 , which can be subdivided into several subclasses such as flavonoids, phenolic acids, stilbenes, and lignans 14 . These compounds primarily exhibit antioxidant and antibacterial activities 15–17 . As early as 1951, research demonstrated that Rosa indica can aid in the diagnosis of keratoconjunctivitis sicca 18 . Therefore, this study employs network pharmacology's multi-component, multi-target, and multi-pathway research approach to investigate the effective components, therapeutic targets, and mechanisms of action of rose in treating SS (Table 1 ). Table 1 Work-flow Diagram Materials and Methods Acquisition of Active Components and Targets of Rosa from TCMSP Database: Active components of Rosa were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) ( https://old.tcmsp-e.com/tcmsp.php ) using "Rosa" as the keyword. The selection criteria were oral bioavailability ≥ 30% and drug-likeness ≥ 0.18. The active components of Rosa were screened as candidate compounds 19 . Targets of the candidate compounds were identified from the TCMSP database, and human genes encoding the target molecules were identified from the UniProt database ( www.genecards.org ) using gene names to refer to target molecules. Identification of SS-Related Target Genes in GeneCards and OMIM Databases: SS-related targets were screened using "SS" as the keyword in the GeneCards database ( https://www.genecards.org/ ) and the OMIM database ( https://omim.org/ ), and the results were compared. Construction and Analysis of the "Drug-Candidate Compound-Target-Disease" Network Using Cytoscape 3.10.2: The molecular targets of Rosa candidate compounds and SS -related targets were summarized to predict the targets for Rosa in treating SS. The "Compound-Target" network was constructed and analyzed using Cytoscape 3.10.2 ( https://cytoscape.org/ ). Nodes in the network represent candidate compounds and potential targets, while edges represent associations between candidate compounds and potential targets. The main components for treating SS were identified through network analysis. Construction and Analysis of the Protein-Protein Interaction (PPI) Network Using STRING Database and Cytoscape 3.10.2: Predicted targets for Rosa in treating SS were analyzed using the STRING database ( https://string-db.org/ ), with the species specified as Homo sapiens, a minimum interaction score of 0.4, and default values for other parameters. The PPI network was constructed and analyzed using Cytoscape 3.10.2, and "highly connected targets" were selected as key targets. Gene Ontology ( GO) Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment Analysis : Gene GO enrichment analysis was performed using DAVID 2021 (Dec. 2021), DAVID Knowledgebase (v2023q4, updated quarterly). KEGG pathway enrichment analysis of 73 predicted target genes was conducted using R (R 4.4.0 GUI 1.80 Big Sur Intel build (8376)) and R Studio (version 2024.04.1 + 748 (2024.04.1 + 748)). Cytoscape 3.10.2 was used to construct networks of key genes from KEGG enrichment analysis, identifying key targets in signaling pathways. Molecular Docking: The top 20 genes ranked by KEGG were included in the PPI network, and overlapping genes were selected as target genes for molecular docking. The three-dimensional structures of pre-target genes were downloaded from the RCSB Protein Data Bank ( www.rcsb.org ), with corresponding ligands as positive references. Molecular docking of protein targets and compound ligands was performed using AutoDock Vina 1.1.2 version (May 11, 2011) and AutoDockTools 1.5.7 version. The highest-scoring conformations were visualized using PyMOL (TM) Molecular Graphics System, Version 3.0.0, showcasing the molecular docking of receptors with the highest binding affinity to their ligands. Results Detection of Rosa Components in the TCMSP Database: A total of 121 components were detected in Rosa from the TCMSP database. By applying the screening criteria of "oral bioavailability ≥ 30% and drug-likeness ≥ 0.18", 10 components met the requirements. These qualifying components were arranged in ascending order of their oral bioavailability values, and the results are presented in Table 2 . From the TCMSP database, 189 targets corresponding to these 10 qualifying components were identified. The human gene codes for these targets were determined using the UniProt database, and the results are also listed in Table 2 . Table 2 Top receptor-ligand binding affinities of four compounds and their associated parameters Target PDBID Ligand Formula IUPAC Name Degree CCL2 3ifd Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene -4.2 EGFR 8a27 Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene -7.8 IFNG 1hig Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene -1.6 IL1B 5r7w Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene 0.0 IL6 1alu Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene -7.2 MMP9 8k5v Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene -6.7 MYC 5i4z Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene -5.6 TP53 6va5 Beta-Carotene C40H56 1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene -7.5 CCL2 3ifd Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol -4.6 EGFR 8a27 Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol -6.5 IFNG 1hig Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol -2.5 IL1B 5r7w Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol 0.0 IL6 1alu Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol -6.8 MMP9 8k5v Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol -6.1 MYC 5i4z Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol -5.4 TP53 6va5 Beta-Sitosterol C29H50O (3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol -7.3 CCL2 3ifd Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one -4.7 EGFR 8a27 Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one -6.4 IFNG 1hig Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one -3.8 IL1B 5r7w Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one 0.0 IL6 1alu Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one -5.8 MMP9 8k5v Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one -8.5 MYC 5i4z Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one -5.8 TP53 6va5 Demethoxycapillarisin C15H10O6 5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one -6.5 CCL2 3ifd Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one -4.6 EGFR 8a27 Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one -7.3 IFNG 1hig Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one -3.7 IL1B 5r7w Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one 0.0 IL6 1alu Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one -5.3 MMP9 8k5v Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one -6.9 MYC 5i4z Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one -5.4 TP53 6va5 Quercetin C15H10O7 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one -6.7 Identification of SS-Related Targets in the GeneCards, OMIM, and DisGeNET Databases: Using "SS" as the keyword in the GeneCards database, SS-related targets were identified by locating the list of genes associated with SS. The median relevance score was used as the screening criterion, with target genes having a relevance score ≥ the median being selected, resulting in 629 relevant target genes [ 1 ] [ 2 ] [ 3 ] . In the OMIM database, using "SS" as the keyword, a list of genes associated with SS was identified, resulting in 644 relevant target genes. Similarly, using "SS" as the keyword in the DisGeNET database and applying the median relevance score as the screening criterion, 481 relevant target genes were identified 20–22 . The target genes from the three databases were then merged, and duplicates were removed, resulting in a total of 1526 target genes associated with SS. Integration of Target Genes from TCMSP and SS-Related Genes: The 233 target genes related to Rosa from the TCMSP database were merged with the 1526 SS-related genes, resulting in 73 overlapping target genes associated with the five candidate compounds. A "Compound-Target" network was constructed using Cytoscape 3.10.2 software. This network consisted of 78 nodes (5 compound nodes and 73 target gene nodes) and 92 edges (Fig. 1 ). Nodes with more connections have higher degree values. The key compounds in the network were quercetin (degree = 68), beta-carotene (degree = 11), beta-sitosterol (degree = 9), and demethoxycapillarisin (degree = 3). Analysis of PPI Network Results: The 73 predicted targets were imported into the STRING database to construct the PPI network. In this network, 72 target proteins exhibited protein-protein interactions, represented by 1351 edges, with one isolated target (CHRM3) that did not interact with any other targets. The PPI network results were ranked by degree using Cytoscape 3.10.2 software (Fig. 2 ). The top 10 proteins in the PPI network were IL6 (degree = 65), TNF (degree = 64), AKT1 (degree = 63), ALB (degree = 62), IL1B (degree = 61), TP53 (degree = 60), JUN (degree = 58), TGFB1 (degree = 58), BCL2 (degree = 58), and ESR1 (degree = 57). Analysis of Enrichment Results: GO gene enrichment analysis was conducted using DAVID 2021 (Dec. 2021) and the DAVID Knowledgebase (v2023q4, updated quarterly). The GO enrichment analysis indicated that 73 human predicted target genes are involved in 1190 biological processes, 273 cellular components, and 403 molecular functions (p < 0.05, q < 0.05). The visualization of the GO enrichment analysis results is shown in Fig. 3 . Using R (R 4.4.0 GUI 1.80 Big Sur Intel build (8376)) and RStudio (version 2024.04.1 + 748 (2024.04.1 + 748)), the distribution of these genes in different cellular components or pathways was analyzed (Fig. 4 ). The most enriched cellular components or pathways were "protein binding", "nucleus", "cytosol", "identical protein binding", and "cytoplasm". The main corresponding molecular functions were "protein binding", "identical protein binding", "enzyme binding", "protein homodimerization activity", and "DNA binding" (Fig. 5 ). The main biological processes involved were "positive regulation of transcription from RNA polymerase II promoter", "positive regulation of transcription, DNA-templated", "positive regulation of gene expression", "signal transduction", and "positive regulation of cell proliferation" (Fig. 6 ). The main cellular components involved were "nucleus", "cytosol", "cytoplasm", "extracellular space", and "nucleoplasm" (Fig. 7 ). KEGG gene enrichment analysis of the 73 predicted target genes was performed using R (R 4.4.0 GUI 1.80 Big Sur Intel build (8376)) and RStudio (version 2024.04.1 + 748 (2024.04.1 + 748)) to reveal significantly enriched pathways under specific conditions. These results help us understand which pathways play important roles in specific biological contexts. The KEGG gene enrichment analysis showed that 148 pathways were significantly enriched (p.adjust < 0.05, q < 0.05). The visualization of these results is shown in bar plots (Fig. 8 ) and bubble plots (Fig. 9 ). In the bubble plots, each bubble represents a KEGG pathway, with the size proportional to the number of genes (Count) and the color indicating the p.adjust value. The color ranges from white (non-significant) to deep red (significant), with deeper colors indicating higher significance. In the bubble plots, "Lipid and atherosclerosis" (Count = 28) showed the largest bubble, indicating that this pathway contains a large number of significant genes, with both p.adjust and q values less than 0.001, demonstrating high significance in the enrichment analysis. Other pathways with both p.adjust and q values less than 0.001 and containing the most genes include "Fluid shear stress and atherosclerosis" (Count = 22), "AGE-RAGE signaling pathway in diabetic complications" (Count = 21), "TNF signaling pathway" (Count = 19), "Hepatitis C" (Count = 19), and "Hepatitis B" (Count = 19). Analysis of Molecular Docking Results: The "Compound-Target" network results indicated that the key compounds in the network were "quercetin", "beta-carotene", "beta-sitosterol", and "demethoxycapillarisin". Among the top-ranked genes in the PPI network and those in the KEGG enrichment analysis, 8 overlapping genes were identified: "EGFR", "MMP9", "MYC", "TP53", "IFNG", "IL1B", "IL6", and "CCL2". Molecular docking was performed for these eight protein targets and four compound ligands ("quercetin", "beta-carotene", "beta-sitosterol", "demethoxycapillarisin") using AutoDock Vina 1.1.2 version (May 11, 2011) and AutoDockTools 1.5.7 version. Each ligand-receptor pair was docked three times, and the average of the three results was taken, with the values rounded to one decimal place for comparison. Table 2 lists the basic information and average binding free energies (affinity in kcal/mol) for each compound-ligand and receptor. The molecular docking results were visualized using PyMOL (TM) Molecular Graphics System, Version 3.0.0. The visualization of the molecular docking of receptors with the highest binding affinity to their ligands is shown in Fig. 10 (Figs. 10 - 1 A, 10 - 1 B, 11 - 2 A, 10 - 2 B, 10 - 3 A, 10 - 3 B, and 10 - 4 A, 10 - 4 B). Figure 10 - 1 A shows the overall docking of Beta-Carotene with EGFR (8a27), and Fig. 10 - 1 B shows the local docking view of Beta-Carotene with EGFR (8a27). Figure 10 - 2 A shows the overall docking of Quercetin with EGFR (8a27), and Fig. 10 - 2 B shows the local docking view of Quercetin with EGFR (8a27). Figure 10 - 3 A shows the overall docking of Demethoxycapillarisin with MMP9 (8k5v), and Fig. 10 - 3 B shows the local docking view of Demethoxycapillarisin with MMP9 (8k5v). Figure 10 - 4 A shows the overall docking of Beta-Sitosterol with TP53 (6va5), and Fig. 10 - 4 B shows the local docking view of Beta-Sitosterol with TP53 (6va5). The molecular docking results for Beta-Carotene with EGFR (8a27) indicated 10 active rotational bonds during the docking process. An affinity heat map of the four ligand compounds ("quercetin", "beta-carotene", "beta-sitosterol", "demethoxycapillarisin") and the eight protein targets ("EGFR", "MMP9", "MYC", "TP53", "IFNG", "IL1B", "IL6", "CCL2") was created using R (Fig. 11 ). Discussion This study utilized network pharmacology and molecular docking methods to investigate the active ingredients, targets, and mechanisms of action of Rosa for the treatment of Sjogren's syndrome. By constructing a "compound-target" interaction network, the key active compounds identified were quercetin, beta-carotene, beta-sitosterol, and demethoxycapillarisin. Sjogren's syndrome is an autoimmune disease characterized by exocrine gland damage and lymphocyte infiltration. Pathological manifestations in SS exocrine gland tissues include CD4 + T and B lymphocyte infiltration, changes in follicular structures, fibrosis, underdevelopment, or loss of gland function 23 . Additionally, approximately 25 ± 5% of SS salivary glands form ectopic germinal centers (GCs), special sites for B cell activation and antibody maturation in non-lymphoid organs 24 . One of the more severe aspects of SS is the high risk of developing malignant non-Hodgkin's lymphoma, with an incidence rate of about 2%-5%, which is a major cause of decreased survival rates in SS patients 25 . Quercetin, also known as 3,3′,4′,5,7-pentahydroxyflavone, is a dietary flavonoid with unique biological properties. Its anti-inflammatory, antioxidant, and anticancer activities are major mechanisms of action 26 . The anti-inflammatory activity of quercetin has been reported in numerous studies 27,28 . Quercetin can downregulate the expression of inflammatory factors by inhibiting the production of inflammation mediators induced by lipopolysaccharides in macrophages and by reducing the synthesis of inflammatory factors through inhibiting the phosphorylation of related inflammatory enzymes and enhancing the activity of antioxidant enzymes 29–31 . Beta-carotene is a low-molecular-weight lipophilic pigment produced by photosynthetic organisms and fungi. In addition to its provitamin A and antioxidant activities, its anti-inflammatory activity has been extensively studied. A meta-analysis of randomized clinical trials indicated that carotenoids significantly reduce the levels of the inflammatory marker C-reactive protein 32 . Beta-carotene is an effective reactive oxygen species scavenger and may interact with transcription factors such as nuclear factor κB or nuclear factor erythroid 2-related factor 2 which are related to inflammation inhibition and oxidative stress, respectively 33 . Beta-sitosterol has been widely used as an anti-inflammatory agent in traditional North African medicine. It can inhibit the expression of the pro-inflammatory neutrophil chemoattractant interleukin IL-8 in bronchial epithelial cells exposed to Pseudomonas aeruginosa 34 . The results of PPI, GO enrichment, and KEGG enrichment analyses indicate that Rosa may exert therapeutic effects on SS through multiple targets and mechanisms. According to the PPI network analysis and KEGG results, the key targets for Rosa in the treatment of SS are IL6, TNF, AKT1, ALB, IL1B, TP53, JUN, TGFB1, BCL2, and ESR1. Studies have confirmed that IL-6 is associated with the pathogenesis of SS and can mediate the activation of the Janus kinase–signal transducer and activator of transcription signaling pathway 35,36 . Levels of IL-1β and IL-6 have been shown to be related to the symptoms of decreased gland secretion in SS 36 . IL-6 can act as an intermediate product promoting glandular cell transformation mediated by TGF-β1 37 . Furthermore, elevated levels of TNF-α in the peripheral blood of SS patients are significantly correlated with systemic inflammation markers 38 . Research on the roles of other genes in SS is currently limited. Besides the key target genes identified in this study, there are other cytokines and pathways closely related to the pathogenesis of SS. IL-27 promotes disease progression in local lacrimal gland inflammation in SS, affecting both CD4 and CD8 T cells 39 . This study demonstrates the various therapeutic effects of Rosa on SS through interactions between its active ingredients and multiple targets. However, this research did not investigate the impact of drug formulations and dosages on efficacy in experimental and clinical settings. Additionally, the possibility that other components of Rosa, not screened in this study, may have therapeutic effects on SS cannot be ruled out, necessitating further experimental validation. Conclusion This study systematically elucidated the potential key active ingredients, targets, and related mechanisms of Rosa for the treatment of SS using network pharmacology. The combined approach of network pharmacology and molecular docking provides an effective model for studying the anti-disease mechanisms of traditional Chinese medicine. The findings suggest that Rosa may exert therapeutic effects on SS through anti-inflammatory and antioxidant mechanisms. The results provide a theoretical basis and further research directions for the potential use of Rosa in the treatment of SS. However, these findings require further experimental and clinical validation. Abbreviations GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genome PPI Protein-Protein Interaction SS Sjögren's Syndrome TCMSP Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform Declarations 1. Ethics approval and consent to participate - Not applicable 2. Consent for publication - Not applicable 3. Availability of data and material - All data used in this study are publicly available and can be accessed from the following databases: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (http://tcmspw.com/tcmsp.php), GeneCards (https://www.genecards.org/), and OMIM (https://omim.org/). The datasets supporting the conclusions of this article are available in these repositories. 4. Competing interests - I declare that I have no competing interests. 5. Funding - Not applicable 6. Authors' contributions - XZ has undertaken the tasks of data collection and processing, manuscript preparation, image collection, table creation, and other activities related to the composition of this article. XZ also certifies that the aforementioned information is accurate. 7. Acknowledgements - Given my limited experience with submissions, I extend my sincere gratitude to the editors of BMC Medical Genomics for their patient guidance. References Vitali C, Bombardieri S, Jonsson R, et al. Classification criteria for Sjogren's syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Ann Rheum Dis. 2002;61(6):554–8. Ramos-Casals M, Brito-Zeron P, Siso-Almirall A, Bosch X. Primary Sjogren syndrome. BMJ. 2012;344:e3821. Fox RI. Sjogren's syndrome. Lancet. 2005;366(9482):321–31. Qin B, Wang J, Yang Z, et al. Epidemiology of primary Sjogren's syndrome: a systematic review and meta-analysis. Ann Rheum Dis. 2015;74(11):1983–9. Patel R, Shahane A. The epidemiology of Sjogren's syndrome. Clin Epidemiol. 2014;6:247–55. Maleki-Fischbach M, Kastsianok L, Koslow M, Chan ED. Manifestations and management of Sjogren's disease. Arthritis Res Ther. 2024;26(1):43. Seror R, Ravaud P, Bowman SJ, et al. EULAR Sjogren's syndrome disease activity index: development of a consensus systemic disease activity index for primary Sjogren's syndrome. Ann Rheum Dis. 2010;69(6):1103–9. Ramos-Casals M, Brito-Zeron P, Solans R, et al. Systemic involvement in primary Sjogren's syndrome evaluated by the EULAR-SS disease activity index: analysis of 921 Spanish patients (GEAS-SS Registry). Rheumatology (Oxford). 2014;53(2):321–31. Cui YY, Abdukiyum M, Xu XF, et al. Efficacy and safety of total glucosides of paeony in treating primary Sjogren's syndrome: a propensity-matched study. Eur Rev Med Pharmacol Sci. 2024;28(10):3523–31. Gottenberg JE, Ravaud P, Puechal X, et al. Effects of hydroxychloroquine on symptomatic improvement in primary Sjogren syndrome: the JOQUER randomized clinical trial. JAMA. 2014;312(3):249–58. Zhang W, Chen Z, Li XM, Gao J, Zhao Y. [Recommendations for the diagnosis and treatment of Sjogren's syndrome in China]. Zhonghua nei ke za zhi. 2023;62(9):1059–67. Brito-Zeron P, Retamozo S, Ramos-Casals M. Sjogren syndrome. Med Clin (Barc). 2023;160(4):163–71. Stadler P, Feldmann HJ, Creighton C, et al. Clinical evidence for correlation of insufficient tissue oxygen supply (hypoxia) and tumor-associated proteolysis in squamous cell carcinoma of the head and neck. Int J Biol Markers. 2000;15(3):235–6. Fraga CG, Croft KD, Kennedy DO, Tomas-Barberan FA. The effects of polyphenols and other bioactives on human health. Food Funct. 2019;10(2):514–28. Bouarab Chibane L, Degraeve P, Ferhout H, Bouajila J, Oulahal N. Plant antimicrobial polyphenols as potential natural food preservatives. J Sci Food Agric. 2019;99(4):1457–74. Baydar NG, Baydar H. Phenolic compounds, antiradical activity and antioxidant capacity of oil-bearing rose (Rosa damascena Mill.) extracts. Ind Crops Prod. 2013;41:375–80. Eman MH. Antimicrobial activity of Rosa damascena petals extracts and chemical composition by gas chromatography-mass spectrometry (GC/MS) analysis. Afr J Microbiol Res. 2014;8(24):2359–67. O'Day K. Bengal rose as an aid in the diagnosis of kerato-conjunctivitis sicca (Sjogren's syndrome). Med J Aust. 1951;2(21):708–9. Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13. Bai F, Wang C, Fan X, et al. Novel biomarkers related to oxidative stress and immunity in chronic kidney disease. Heliyon. 2024;10(6):e27754. Chen D, Xie Z, Yang J, et al. Stratification and prognostic evaluation of breast cancer subtypes defined by obesity-associated genes. Discov Oncol. 2024;15(1):133. Zhang LC, Li N, Chen JL, et al. Molecular network mechanism in cerebral ischemia-reperfusion rats treated with human urine stem cells. Heliyon. 2024;10(7):e27508. Fasano S, Mauro D, Macaluso F, et al. Pathogenesis of primary Sjogren's syndrome beyond B lymphocytes. Clin Exp Rheumatol. 2020;38(Suppl 126):315–23. Sene D, Ismael S, Forien M, et al. Ectopic Germinal Center-Like Structures in Minor Salivary Gland Biopsy Tissue Predict Lymphoma Occurrence in Patients With Primary Sjogren's Syndrome. Arthritis Rheumatol. 2018;70(9):1481–8. Bruno D, Tolusso B, Lugli G et al. B-Cell Activation Biomarkers in Salivary Glands Are Related to Lymphomagenesis in Primary Sjogren's Disease: A Pilot Monocentric Exploratory Study. Int J Mol Sci 2024;25(6). Jafarinia M, Sadat Hosseini M, Kasiri N, et al. Quercetin with the potential effect on allergic diseases. Allergy Asthma Clin Immunol. 2020;16:36. Saccon TD, Nagpal R, Yadav H, et al. Senolytic Combination of Dasatinib and Quercetin Alleviates Intestinal Senescence and Inflammation and Modulates the Gut Microbiome in Aged Mice. J Gerontol Biol Sci Med Sci. 2021;76(11):1895–905. Li Y, Yao J, Han C, et al. Quercetin, Inflammation and Immunity. Nutrients. 2016;8(3):167. Gruse J, Kanitz E, Weitzel JM, et al. Quercetin Feeding in Newborn Dairy Calves Cannot Compensate Colostrum Deprivation: Study on Metabolic, Antioxidative and Inflammatory Traits. PLoS ONE. 2016;11(1):e0146932. Lee HN, Shin SA, Choo GS, et al. Anti–inflammatory effect of quercetin and galangin in LPS–stimulated RAW264.7 macrophages and DNCB–induced atopic dermatitis animal models. Int J Mol Med. 2018;41(2):888–98. Kaneider NC, Mosheimer B, Reinisch N, Patsch JR, Wiedermann CJ. Inhibition of thrombin-induced signaling by resveratrol and quercetin: effects on adenosine nucleotide metabolism in endothelial cells and platelet-neutrophil interactions. Thromb Res. 2004;114(3):185–94. Hajizadeh-Sharafabad F, Zahabi ES, Malekahmadi M, Zarrin R, Alizadeh M. Carotenoids supplementation and inflammation: a systematic review and meta-analysis of randomized clinical trials. Crit Rev Food Sci Nutr. 2021;62(29):8161–77. Kaulmann A, Bohn T. Carotenoids, inflammation, and oxidative stress—implications of cellular signaling pathways and relation to chronic disease prevention. Nutr Res. 2014;34(11):907–29. Lampronti I, Dechecchi MC, Rimessi A, et al. beta-Sitosterol Reduces the Expression of Chemotactic Cytokine Genes in Cystic Fibrosis Bronchial Epithelial Cells. Front Pharmacol. 2017;8:236. Barrera MJ, Aguilera S, Castro I, et al. Tofacitinib counteracts IL-6 overexpression induced by deficient autophagy: implications in Sjogren's syndrome. Rheumatology (Oxford). 2021;60(4):1951–62. Castro I, Albornoz N, Aguilera S, et al. Aberrant MUC1 accumulation in salivary glands of Sjogren's syndrome patients is reversed by TUDCA in vitro. Rheumatology (Oxford). 2020;59(4):742–53. Sisto M, Tamma R, Ribatti D, Lisi S. IL-6 Contributes to the TGF-beta1-Mediated Epithelial to Mesenchymal Transition in Human Salivary Gland Epithelial Cells. Arch Immunol Ther Exp (Warsz). 2020;68(5):27. Chen C, Liang Y, Zhang Z, Zhang Z, Yang Z. Relationships between increased circulating YKL-40, IL-6 and TNF-alpha levels and phenotypes and disease activity of primary Sjogren's syndrome. Int Immunopharmacol. 2020;88:106878. Debreceni IL, Barr JY, Upton EM, Chen YG, Lieberman SM. IL-27 promotes pathogenic T cells in a mouse model of Sjogren's disease. Clin Immunol. 2024;264:110260. Additional Declarations No competing interests reported. Supplementary Files CONSORTChecklistExperimentalStudy.doc 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. 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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-4793586","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336166630,"identity":"245c19f0-ea6e-46a3-897c-9aa2eae3d47a","order_by":0,"name":"Xi Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACNmbmAwckKmx47O8fPkCcFj72tsQHFmfS5BhusCUQp0WO54yxQWXLYWOGGzwGRDpMIsFM4mYDc2Lj7J6PN94w2MnpNhDWkiY5cwdbYrPM2c2WcxiSjc0OENZyTFryDE9iG0PuNmkehgOJ2whrSWyT/tsmkdjDkPOMSC08h5kNJNsMjCUkctiI1MLexvhA4kyCnAHPMWPLOQZE+EW+mf8DMCr/8xiwNz+88abCTo6gFhQgQWzUIGshVccoGAWjYBSMCAAA6LlBn4XBAD0AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Xi","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-07-24 08:24:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4793586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4793586/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61935704,"identity":"e08c9e69-d525-43eb-b083-412c97e52a95","added_by":"auto","created_at":"2024-08-07 09:08:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":923257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompound-Target Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis network consisted of 78 nodes (5 compound nodes and 73 target gene nodes) and 92 edges. Nodes with more connections have higher degree values.\u003c/p\u003e\n\u003cp\u003eThe key compounds in the network were quercetin (degree = 68), beta-carotene (degree = 11), beta-sitosterol (degree = 9), and demethoxycapillarisin (degree = 3).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/978ca0a8c7c85f3920cf7466.png"},{"id":61936404,"identity":"7e895fe8-d153-42b0-b436-af0eef29eb01","added_by":"auto","created_at":"2024-08-07 09:16:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23305556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-Protein Interaction Network for Predicted Target Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTRING database was used to construct the Protein-Protein Interaction network for 73 predicted target genes. In this network, 72 target proteins exhibited protein-protein interactions, represented by 1351 edges, while one isolated target protein (CHRM3) did not interact with any other target proteins.\u003c/p\u003e\n\u003cp\u003eNode sizes vary gradually based on their centrality and connectivity. Specifically, node size decreases as centrality and connectivity decrease.\u003c/p\u003e\n\u003cp\u003eEdges: Edge colors and thickness vary gradually based on their confidence and importance. The sequence of changes is from high to low confidence and importance, with colors transitioning from red, orange, yellow, green, cyan, blue, purple, to black, while the edge thickness decreases accordingly.\u003c/p\u003e\n\u003cp\u003eThe PPI network results were visualized and ranked by degree using Cytoscape 3.10.2 software.The top 10 proteins in the PPI network are IL6 (degree = 65), TNF (degree = 64), AKT1 (degree = 63), ALB (degree = 62), IL1B (degree = 61), TP53 (degree = 60), JUN (degree = 58), TGFB1 (degree = 58), BCL2 (degree = 58), and ESR1 (degree = 57).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/c4d2089a56aa911a0d98685c.png"},{"id":61937214,"identity":"c7d2d5ed-00a4-40a9-9f7c-633e12aabd61","added_by":"auto","created_at":"2024-08-07 09:24:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2855813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Gene Ontology enrichment analysis indicated that 73 human predicted target genes are involved in 1190 biological processes, 273 cellular components, and 403 molecular functions (p \u0026lt; 0.05, q \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/5f7477d9ef42d5d26345379f.png"},{"id":61935708,"identity":"250e0fff-685e-4e67-b4c6-a4b7b2af3814","added_by":"auto","created_at":"2024-08-07 09:08:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1828115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Distribution of Target Genes in Different Cellular Components or Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most enriched cellular components or pathways were \"protein binding\", \"nucleus\", \"cytosol\", \"identical protein binding\", and \"cytoplasm\".\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/764552ef1f6e9b82c0be45b7.png"},{"id":61935706,"identity":"2f222ca6-506a-44f8-9b21-a4b7370cf5a2","added_by":"auto","created_at":"2024-08-07 09:08:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1213124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Main Corresponding Molecular Functions of Target Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main corresponding molecular functions were \"protein binding\", \"identical protein binding\", \"enzyme binding\", \"protein homodimerization activity\", and \"DNA binding\".\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/89a980ccfa0efba366e9e949.png"},{"id":61937215,"identity":"64d34485-1926-4a18-af94-fb3ad2e5946e","added_by":"auto","created_at":"2024-08-07 09:24:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1522062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Main Biological Processes Involved of Target Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main biological processes involved were \"positive regulation of transcription from RNA polymerase II promoter\", \"positive regulation of transcription, DNA-templated\", \"positive regulation of gene expression\", \"signal transduction\", and \"positive regulation of cell proliferation\".\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/4d1578137a0305bec81e4548.png"},{"id":61936398,"identity":"5fa81932-6803-4fc8-b186-065b017b3b99","added_by":"auto","created_at":"2024-08-07 09:16:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":949698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Main Cellular Components Involved of Target Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main cellular components involved were \"nucleus\", \"cytosol\", \"cytoplasm\", \"extracellular space\", and \"nucleoplasm\".\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/78adabceefefe6b74bc50646.png"},{"id":61936399,"identity":"aca5ebb6-c3a4-43e8-b4f2-c7b7eac401dd","added_by":"auto","created_at":"2024-08-07 09:16:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3929883,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Bar Plots of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe visualization bar plots of KEGG gene enrichment analysis. The plots showed that 148 pathways were significantly enriched (p.adjust \u0026lt; 0.05, q \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/8a1049367facc95485c1ce40.png"},{"id":61937216,"identity":"026da6a2-af3d-4a8e-adde-92574b2dbbe3","added_by":"auto","created_at":"2024-08-07 09:24:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4039283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Bubble Plots of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe visualization bubble plots of KEGG gene enrichment analysis.\u003c/p\u003e\n\u003cp\u003eIn the bubble plots, each bubble represents a KEGG pathway, with the size proportional to the number of genes (Count) and the color indicating the p.adjust value. The color ranges from white (non-significant) to deep red (significant), with deeper colors indicating higher significance.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/837ac2e90f21990a15443cad.png"},{"id":61935716,"identity":"422f64c4-7259-4d15-aec1-ca205a92c559","added_by":"auto","created_at":"2024-08-07 09:08:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":69783317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization of Molecular Docking Results Between Key Compounds and Enriched Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 10-1A shows the overall docking of Beta-Carotene with EGFR (8a27), and Figure 10-1B shows the local docking view of Beta-Carotene with EGFR (8a27).\u003c/p\u003e\n\u003cp\u003eFigure 10-2A shows the overall docking of Quercetin with EGFR (8a27), and Figure 10-2B shows the local docking view of Quercetin with EGFR (8a27).\u003c/p\u003e\n\u003cp\u003eFigure 10-3A shows the overall docking of Demethoxycapillarisin with MMP9 (8k5v), and Figure 10-3B shows the local docking view of Demethoxycapillarisin with MMP9 (8k5v).\u003c/p\u003e\n\u003cp\u003eFigure 10-4A shows the overall docking of Beta-Sitosterol with TP53 (6va5), and Figure 10-4B shows the local docking view of Beta-Sitosterol with TP53 (6va5).\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/847efaba7291a583d1593a39.png"},{"id":61936402,"identity":"c7ad3b69-7d7c-4861-96ec-69bb453f0dee","added_by":"auto","created_at":"2024-08-07 09:16:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1424256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn Affinity Heat Map of the Four Ligand Compounds and the Eight Protein Targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn affinity heat map of the four ligand compounds (\"quercetin\", \"beta-carotene\", \"beta-sitosterol\", \"demethoxycapillarisin\") and the eight protein targets (\"EGFR\", \"MMP9\", \"MYC\", \"TP53\", \"IFNG\", \"IL1B\", \"IL6\", \"CCL2\") was created using R software.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/10fd56de0fc078702218c3b8.png"},{"id":61935713,"identity":"8f0272db-2ead-4f04-8bab-54995d110bc0","added_by":"auto","created_at":"2024-08-07 09:08:46","extension":"doc","order_by":22,"title":"","display":"","copyAsset":false,"role":"supplement","size":231936,"visible":true,"origin":"","legend":"","description":"","filename":"CONSORTChecklistExperimentalStudy.doc","url":"https://assets-eu.researchsquare.com/files/rs-4793586/v1/a5b8af48c5aad8c6f3a32d83.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Mechanism of Rose in Treating Sjögren's Syndrome Based on Network Pharmacology and Molecular Docking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSj\u0026ouml;gren's Syndrome (SS) is a complex systemic autoimmune disease characterized by dysfunction of the exocrine glands, particularly the salivary and lacrimal glands, and by chronic lymphocytic infiltration of the glandular parenchyma\u003csup\u003e1\u003c/sup\u003e. This results in dryness of the major mucosal surfaces, such as the mouth, eyes, nose, pharynx, larynx, and vagina\u003csup\u003e2\u003c/sup\u003e. SS is marked by lymphocytic infiltration of exocrine glands and a variety of systemic manifestations. It can occur as a primary disease (primary) or in association with other autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, dermatomyositis, or systemic sclerosis\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn primary SS, the female-to-male ratio is 9:1, with a peak incidence around the age of fifty\u003csup\u003e4\u003c/sup\u003e, the prevalence in the general population ranges from approximately 0.02\u0026ndash;2.7%\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe systemic manifestations of SS are common and may involve various domains such as skin, kidneys, joints, muscles, peripheral nervous system, central nervous system, hematology, and glands\u003csup\u003e6\u003c/sup\u003e. The EULAR SS Disease Activity Index categorizes systemic disease activity from low to high across 12 domains (skin, kidneys, joints, muscles, peripheral nervous system, central nervous system, hematology, glands, constitutional, lymphadenopathy, lungs, biology)\u003csup\u003e7\u003c/sup\u003e. The most common and active the EULAR SS Disease Activity Index domains are joints (56%), glands (34%), lungs (15%), and skin (13%)\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCommon immunomodulators, including hydroxychloroquine, prednisone, methotrexate, mycophenolate mofetil, and azathioprine, often fail to improve functional parameters of the salivary and lacrimal glands in clinical trials for primary SS\u003csup\u003e9,10\u003c/sup\u003e. Additionally, the use of immunosuppressants in the early stages of SS-related xerostomia is not recommended due to the risks of immune disorders, tumors, and liver and kidney damage\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have shown that traditional Chinese medicine can effectively reduce inflammatory markers such as IL-6, IL-10, erythrocyte sedimentation rate, and C-reactive protein in patients with SS, thereby alleviating local symptoms and preventing further disease progression\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRosa contain high concentrations of phenolic compounds\u003csup\u003e13\u003c/sup\u003e, which can be subdivided into several subclasses such as flavonoids, phenolic acids, stilbenes, and lignans\u003csup\u003e14\u003c/sup\u003e. These compounds primarily exhibit antioxidant and antibacterial activities \u003csup\u003e15\u0026ndash;17\u003c/sup\u003e. As early as 1951, research demonstrated that Rosa indica can aid in the diagnosis of keratoconjunctivitis sicca\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, this study employs network pharmacology's multi-component, multi-target, and multi-pathway research approach to investigate the effective components, therapeutic targets, and mechanisms of action of rose in treating SS (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\u003eWork-flow Diagram\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of Active Components and Targets of Rosa from TCMSP Database:\u003c/h2\u003e \u003cp\u003eActive components of Rosa were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/tcmsp.php\u003c/span\u003e\u003cspan address=\"https://old.tcmsp-e.com/tcmsp.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using \"Rosa\" as the keyword. The selection criteria were oral bioavailability\u0026thinsp;\u0026ge;\u0026thinsp;30% and drug-likeness\u0026thinsp;\u0026ge;\u0026thinsp;0.18. The active components of Rosa were screened as candidate compounds\u003csup\u003e19\u003c/sup\u003e. Targets of the candidate compounds were identified from the TCMSP database, and human genes encoding the target molecules were identified from the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://old.tcmsp-e.com/tcmsp.php\" target=\"_blank\"\u003ewww.genecards.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.genecards.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using gene names to refer to target molecules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of SS-Related Target Genes in GeneCards and OMIM Databases:\u003c/h2\u003e \u003cp\u003eSS-related targets were screened using \"SS\" as the keyword in the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the OMIM database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the results were compared.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction and Analysis of the \"Drug-Candidate Compound-Target-Disease\" Network Using Cytoscape 3.10.2:\u003c/h3\u003e\n\u003cp\u003eThe molecular targets of Rosa candidate compounds and SS -related targets were summarized to predict the targets for Rosa in treating SS. The \"Compound-Target\" network was constructed and analyzed using Cytoscape 3.10.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Nodes in the network represent candidate compounds and potential targets, while edges represent associations between candidate compounds and potential targets. The main components for treating SS were identified through network analysis.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and Analysis of the Protein-Protein Interaction (PPI) Network Using STRING Database and Cytoscape 3.10.2:\u003c/h2\u003e \u003cp\u003ePredicted targets for Rosa in treating SS were analyzed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with the species specified as Homo sapiens, a minimum interaction score of 0.4, and default values for other parameters. The PPI network was constructed and analyzed using Cytoscape 3.10.2, and \"highly connected targets\" were selected as key targets.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene Ontology\u003c/b\u003e (\u003cb\u003eGO) Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment Analysis\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eGene GO enrichment analysis was performed using DAVID 2021 (Dec. 2021), DAVID Knowledgebase (v2023q4, updated quarterly). KEGG pathway enrichment analysis of 73 predicted target genes was conducted using R (R 4.4.0 GUI 1.80 Big Sur Intel build (8376)) and R Studio (version 2024.04.1\u0026thinsp;+\u0026thinsp;748 (2024.04.1\u0026thinsp;+\u0026thinsp;748)). Cytoscape 3.10.2 was used to construct networks of key genes from KEGG enrichment analysis, identifying key targets in signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking:\u003c/h2\u003e \u003cp\u003eThe top 20 genes ranked by KEGG were included in the PPI network, and overlapping genes were selected as target genes for molecular docking. The three-dimensional structures of pre-target genes were downloaded from the RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://old.tcmsp-e.com/tcmsp.php\" target=\"_blank\"\u003ewww.rcsb.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with corresponding ligands as positive references. Molecular docking of protein targets and compound ligands was performed using AutoDock Vina 1.1.2 version (May 11, 2011) and AutoDockTools 1.5.7 version. The highest-scoring conformations were visualized using PyMOL (TM) Molecular Graphics System, Version 3.0.0, showcasing the molecular docking of receptors with the highest binding affinity to their ligands.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDetection of Rosa Components in the TCMSP Database:\u003c/h2\u003e \u003cp\u003eA total of 121 components were detected in Rosa from the TCMSP database. By applying the screening criteria of \"oral bioavailability\u0026thinsp;\u0026ge;\u0026thinsp;30% and drug-likeness\u0026thinsp;\u0026ge;\u0026thinsp;0.18\", 10 components met the requirements. These qualifying components were arranged in ascending order of their oral bioavailability values, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. From the TCMSP database, 189 targets corresponding to these 10 qualifying components were identified. The human gene codes for these targets were determined using the UniProt database, and the results are also listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop receptor-ligand binding affinities of four compounds and their associated parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDBID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLigand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIUPAC Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3ifd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC40H56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8a27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC40H56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFNG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1hig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC40H56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1alu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC40H56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8k5v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC40H56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5i4z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC40H56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6va5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC40H56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,3,3-trimethyl-2-[(1E,3E,5E,7E,9E,11E,13E,15E,17E)-3,7,12,16-tetramethyl-18-(2,6,6-trimethylcyclohexen-1-yl)octadeca-1,3,5,7,9,11,13,15,17-nonaenyl]cyclohexene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3ifd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8a27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFNG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1hig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5r7w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1alu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8k5v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5i4z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6va5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC29H50O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3S,8S,9S,10R,13R,14S,17R)-17-[(2R,5R)-5-ethyl-6-methylheptan-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3ifd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8a27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFNG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1hig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5r7w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1alu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8k5v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5i4z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6va5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemethoxycapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,7-dihydroxy-2-(4-hydroxyphenoxy)chromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3ifd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8a27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFNG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1hig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5r7w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1alu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8k5v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5i4z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6va5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC15H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.7\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of SS-Related Targets in the GeneCards, OMIM, and DisGeNET Databases:\u003c/h2\u003e \u003cp\u003eUsing \"SS\" as the keyword in the GeneCards database, SS-related targets were identified by locating the list of genes associated with SS. The median relevance score was used as the screening criterion, with target genes having a relevance score\u0026thinsp;\u0026ge;\u0026thinsp;the median being selected, resulting in 629 relevant target genes \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the OMIM database, using \"SS\" as the keyword, a list of genes associated with SS was identified, resulting in 644 relevant target genes. Similarly, using \"SS\" as the keyword in the DisGeNET database and applying the median relevance score as the screening criterion, 481 relevant target genes were identified\u003csup\u003e20\u0026ndash;22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe target genes from the three databases were then merged, and duplicates were removed, resulting in a total of 1526 target genes associated with SS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of Target Genes from TCMSP and SS-Related Genes:\u003c/h2\u003e \u003cp\u003eThe 233 target genes related to Rosa from the TCMSP database were merged with the 1526 SS-related genes, resulting in 73 overlapping target genes associated with the five candidate compounds. A \"Compound-Target\" network was constructed using Cytoscape 3.10.2 software. This network consisted of 78 nodes (5 compound nodes and 73 target gene nodes) and 92 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nodes with more connections have higher degree values. The key compounds in the network were quercetin (degree\u0026thinsp;=\u0026thinsp;68), beta-carotene (degree\u0026thinsp;=\u0026thinsp;11), beta-sitosterol (degree\u0026thinsp;=\u0026thinsp;9), and demethoxycapillarisin (degree\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of PPI Network Results:\u003c/h2\u003e \u003cp\u003eThe 73 predicted targets were imported into the STRING database to construct the PPI network. In this network, 72 target proteins exhibited protein-protein interactions, represented by 1351 edges, with one isolated target (CHRM3) that did not interact with any other targets. The PPI network results were ranked by degree using Cytoscape 3.10.2 software (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The top 10 proteins in the PPI network were IL6 (degree\u0026thinsp;=\u0026thinsp;65), TNF (degree\u0026thinsp;=\u0026thinsp;64), AKT1 (degree\u0026thinsp;=\u0026thinsp;63), ALB (degree\u0026thinsp;=\u0026thinsp;62), IL1B (degree\u0026thinsp;=\u0026thinsp;61), TP53 (degree\u0026thinsp;=\u0026thinsp;60), JUN (degree\u0026thinsp;=\u0026thinsp;58), TGFB1 (degree\u0026thinsp;=\u0026thinsp;58), BCL2 (degree\u0026thinsp;=\u0026thinsp;58), and ESR1 (degree\u0026thinsp;=\u0026thinsp;57).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Enrichment Results:\u003c/h2\u003e \u003cp\u003eGO gene enrichment analysis was conducted using DAVID 2021 (Dec. 2021) and the DAVID Knowledgebase (v2023q4, updated quarterly). The GO enrichment analysis indicated that 73 human predicted target genes are involved in 1190 biological processes, 273 cellular components, and 403 molecular functions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The visualization of the GO enrichment analysis results is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Using R (R 4.4.0 GUI 1.80 Big Sur Intel build (8376)) and RStudio (version 2024.04.1\u0026thinsp;+\u0026thinsp;748 (2024.04.1\u0026thinsp;+\u0026thinsp;748)), the distribution of these genes in different cellular components or pathways was analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The most enriched cellular components or pathways were \"protein binding\", \"nucleus\", \"cytosol\", \"identical protein binding\", and \"cytoplasm\". The main corresponding molecular functions were \"protein binding\", \"identical protein binding\", \"enzyme binding\", \"protein homodimerization activity\", and \"DNA binding\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The main biological processes involved were \"positive regulation of transcription from RNA polymerase II promoter\", \"positive regulation of transcription, DNA-templated\", \"positive regulation of gene expression\", \"signal transduction\", and \"positive regulation of cell proliferation\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The main cellular components involved were \"nucleus\", \"cytosol\", \"cytoplasm\", \"extracellular space\", and \"nucleoplasm\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKEGG gene enrichment analysis of the 73 predicted target genes was performed using R (R 4.4.0 GUI 1.80 Big Sur Intel build (8376)) and RStudio (version 2024.04.1\u0026thinsp;+\u0026thinsp;748 (2024.04.1\u0026thinsp;+\u0026thinsp;748)) to reveal significantly enriched pathways under specific conditions. These results help us understand which pathways play important roles in specific biological contexts. The KEGG gene enrichment analysis showed that 148 pathways were significantly enriched (p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05, q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The visualization of these results is shown in bar plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) and bubble plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In the bubble plots, each bubble represents a KEGG pathway, with the size proportional to the number of genes (Count) and the color indicating the p.adjust value. The color ranges from white (non-significant) to deep red (significant), with deeper colors indicating higher significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the bubble plots, \"Lipid and atherosclerosis\" (Count\u0026thinsp;=\u0026thinsp;28) showed the largest bubble, indicating that this pathway contains a large number of significant genes, with both p.adjust and q values less than 0.001, demonstrating high significance in the enrichment analysis. Other pathways with both p.adjust and q values less than 0.001 and containing the most genes include \"Fluid shear stress and atherosclerosis\" (Count\u0026thinsp;=\u0026thinsp;22), \"AGE-RAGE signaling pathway in diabetic complications\" (Count\u0026thinsp;=\u0026thinsp;21), \"TNF signaling pathway\" (Count\u0026thinsp;=\u0026thinsp;19), \"Hepatitis C\" (Count\u0026thinsp;=\u0026thinsp;19), and \"Hepatitis B\" (Count\u0026thinsp;=\u0026thinsp;19).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Molecular Docking Results:\u003c/h2\u003e \u003cp\u003eThe \"Compound-Target\" network results indicated that the key compounds in the network were \"quercetin\", \"beta-carotene\", \"beta-sitosterol\", and \"demethoxycapillarisin\". Among the top-ranked genes in the PPI network and those in the KEGG enrichment analysis, 8 overlapping genes were identified: \"EGFR\", \"MMP9\", \"MYC\", \"TP53\", \"IFNG\", \"IL1B\", \"IL6\", and \"CCL2\". Molecular docking was performed for these eight protein targets and four compound ligands (\"quercetin\", \"beta-carotene\", \"beta-sitosterol\", \"demethoxycapillarisin\") using AutoDock Vina 1.1.2 version (May 11, 2011) and AutoDockTools 1.5.7 version. Each ligand-receptor pair was docked three times, and the average of the three results was taken, with the values rounded to one decimal place for comparison. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the basic information and average binding free energies (affinity in kcal/mol) for each compound-ligand and receptor.\u003c/p\u003e \u003cp\u003eThe molecular docking results were visualized using PyMOL (TM) Molecular Graphics System, Version 3.0.0. The visualization of the molecular docking of receptors with the highest binding affinity to their ligands is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e (Figs.\u0026nbsp;\u0026lt;link rid=\"fig10\"\u0026gt;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u0026lt;/link\u0026gt;\u003c/span\u003e-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u0026lt;link rid=\"fig10\"\u0026gt;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u0026lt;/link\u0026gt;\u003c/span\u003e-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u0026lt;link rid=\"fig10\"\u0026gt;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u0026lt;/link\u0026gt;\u003c/span\u003e-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u0026lt;link rid=\"fig10\"\u0026gt;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u0026lt;/link\u0026gt;\u003c/span\u003e-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u0026lt;link rid=\"fig10\"\u0026gt;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u0026lt;/link\u0026gt;\u003c/span\u003e-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, and \u0026lt;link rid=\"fig10\"\u0026gt;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u0026lt;/link\u0026gt;\u003c/span\u003e-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u0026lt;link rid=\"fig10\"\u0026gt;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u0026lt;/link\u0026gt;\u003c/span\u003e-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows the overall docking of Beta-Carotene with EGFR (8a27), and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows the local docking view of Beta-Carotene with EGFR (8a27).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows the overall docking of Quercetin with EGFR (8a27), and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows the local docking view of Quercetin with EGFR (8a27).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA shows the overall docking of Demethoxycapillarisin with MMP9 (8k5v), and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB shows the local docking view of Demethoxycapillarisin with MMP9 (8k5v).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA shows the overall docking of Beta-Sitosterol with TP53 (6va5), and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB shows the local docking view of Beta-Sitosterol with TP53 (6va5). The molecular docking results for Beta-Carotene with EGFR (8a27) indicated 10 active rotational bonds during the docking process.\u003c/p\u003e \u003cp\u003eAn affinity heat map of the four ligand compounds (\"quercetin\", \"beta-carotene\", \"beta-sitosterol\", \"demethoxycapillarisin\") and the eight protein targets (\"EGFR\", \"MMP9\", \"MYC\", \"TP53\", \"IFNG\", \"IL1B\", \"IL6\", \"CCL2\") was created using R (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized network pharmacology and molecular docking methods to investigate the active ingredients, targets, and mechanisms of action of Rosa for the treatment of Sjogren's syndrome.\u003c/p\u003e \u003cp\u003eBy constructing a \"compound-target\" interaction network, the key active compounds identified were quercetin, beta-carotene, beta-sitosterol, and demethoxycapillarisin. Sjogren's syndrome is an autoimmune disease characterized by exocrine gland damage and lymphocyte infiltration. Pathological manifestations in SS exocrine gland tissues include CD4\u0026thinsp;+\u0026thinsp;T and B lymphocyte infiltration, changes in follicular structures, fibrosis, underdevelopment, or loss of gland function\u003csup\u003e23\u003c/sup\u003e. Additionally, approximately 25\u0026thinsp;\u0026plusmn;\u0026thinsp;5% of SS salivary glands form ectopic germinal centers (GCs), special sites for B cell activation and antibody maturation in non-lymphoid organs\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne of the more severe aspects of SS is the high risk of developing malignant non-Hodgkin's lymphoma, with an incidence rate of about 2%-5%, which is a major cause of decreased survival rates in SS patients\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eQuercetin, also known as 3,3\u0026prime;,4\u0026prime;,5,7-pentahydroxyflavone, is a dietary flavonoid with unique biological properties. Its anti-inflammatory, antioxidant, and anticancer activities are major mechanisms of action\u003csup\u003e26\u003c/sup\u003e. The anti-inflammatory activity of quercetin has been reported in numerous studies\u003csup\u003e27,28\u003c/sup\u003e. Quercetin can downregulate the expression of inflammatory factors by inhibiting the production of inflammation mediators induced by lipopolysaccharides in macrophages and by reducing the synthesis of inflammatory factors through inhibiting the phosphorylation of related inflammatory enzymes and enhancing the activity of antioxidant enzymes\u003csup\u003e29\u0026ndash;31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeta-carotene is a low-molecular-weight lipophilic pigment produced by photosynthetic organisms and fungi. In addition to its provitamin A and antioxidant activities, its anti-inflammatory activity has been extensively studied. A meta-analysis of randomized clinical trials indicated that carotenoids significantly reduce the levels of the inflammatory marker C-reactive protein\u003csup\u003e32\u003c/sup\u003e. Beta-carotene is an effective reactive oxygen species scavenger and may interact with transcription factors such as nuclear factor κB or nuclear factor erythroid 2-related factor 2 which are related to inflammation inhibition and oxidative stress, respectively\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeta-sitosterol has been widely used as an anti-inflammatory agent in traditional North African medicine. It can inhibit the expression of the pro-inflammatory neutrophil chemoattractant interleukin IL-8 in bronchial epithelial cells exposed to Pseudomonas aeruginosa\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results of PPI, GO enrichment, and KEGG enrichment analyses indicate that Rosa may exert therapeutic effects on SS through multiple targets and mechanisms. According to the PPI network analysis and KEGG results, the key targets for Rosa in the treatment of SS are IL6, TNF, AKT1, ALB, IL1B, TP53, JUN, TGFB1, BCL2, and ESR1.\u003c/p\u003e \u003cp\u003eStudies have confirmed that IL-6 is associated with the pathogenesis of SS and can mediate the activation of the Janus kinase\u0026ndash;signal transducer and activator of transcription signaling pathway\u003csup\u003e35,36\u003c/sup\u003e. Levels of IL-1β and IL-6 have been shown to be related to the symptoms of decreased gland secretion in SS\u003csup\u003e36\u003c/sup\u003e. IL-6 can act as an intermediate product promoting glandular cell transformation mediated by TGF-β1\u003csup\u003e37\u003c/sup\u003e. Furthermore, elevated levels of TNF-α in the peripheral blood of SS patients are significantly correlated with systemic inflammation markers\u003csup\u003e38\u003c/sup\u003e. Research on the roles of other genes in SS is currently limited.\u003c/p\u003e \u003cp\u003eBesides the key target genes identified in this study, there are other cytokines and pathways closely related to the pathogenesis of SS. IL-27 promotes disease progression in local lacrimal gland inflammation in SS, affecting both CD4 and CD8 T cells\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study demonstrates the various therapeutic effects of Rosa on SS through interactions between its active ingredients and multiple targets. However, this research did not investigate the impact of drug formulations and dosages on efficacy in experimental and clinical settings. Additionally, the possibility that other components of Rosa, not screened in this study, may have therapeutic effects on SS cannot be ruled out, necessitating further experimental validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study systematically elucidated the potential key active ingredients, targets, and related mechanisms of Rosa for the treatment of SS using network pharmacology. The combined approach of network pharmacology and molecular docking provides an effective model for studying the anti-disease mechanisms of traditional Chinese medicine. The findings suggest that Rosa may exert therapeutic effects on SS through anti-inflammatory and antioxidant mechanisms.\u003c/p\u003e \u003cp\u003eThe results provide a theoretical basis and further research directions for the potential use of Rosa in the treatment of SS. However, these findings require further experimental and clinical validation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGO\u003c/b\u003e\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\"\u003e\u003cb\u003eKEGG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePPI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSj\u0026ouml;gren's Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTCMSP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTraditional Chinese Medicine Systems Pharmacology Database and Analysis Platform\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e1. Ethics approval and consent to participate - Not applicable\u003c/p\u003e\n\u003cp\u003e2. Consent for publication - Not applicable\u003c/p\u003e\n\u003cp\u003e3. Availability of data and material - All data used in this study are publicly available and can be accessed from the following databases: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (http://tcmspw.com/tcmsp.php), GeneCards (https://www.genecards.org/), and OMIM (https://omim.org/). The datasets supporting the conclusions of this article are available in these repositories.\u003c/p\u003e\n\u003cp\u003e4. Competing interests - I declare that I have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5. Funding - Not applicable\u003c/p\u003e\n\u003cp\u003e6. Authors\u0026apos; contributions - XZ has undertaken the tasks of data collection and processing, manuscript preparation, image collection, table creation, and other activities related to the composition of this article. XZ also certifies that the aforementioned information is accurate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e7. Acknowledgements - Given my limited experience with submissions, I extend my sincere gratitude to the editors of BMC Medical Genomics for their patient guidance.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVitali C, Bombardieri S, Jonsson R, et al. Classification criteria for Sjogren's syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Ann Rheum Dis. 2002;61(6):554\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos-Casals M, Brito-Zeron P, Siso-Almirall A, Bosch X. Primary Sjogren syndrome. BMJ. 2012;344:e3821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox RI. Sjogren's syndrome. Lancet. 2005;366(9482):321\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin B, Wang J, Yang Z, et al. Epidemiology of primary Sjogren's syndrome: a systematic review and meta-analysis. Ann Rheum Dis. 2015;74(11):1983\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel R, Shahane A. The epidemiology of Sjogren's syndrome. Clin Epidemiol. 2014;6:247\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaleki-Fischbach M, Kastsianok L, Koslow M, Chan ED. Manifestations and management of Sjogren's disease. Arthritis Res Ther. 2024;26(1):43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeror R, Ravaud P, Bowman SJ, et al. EULAR Sjogren's syndrome disease activity index: development of a consensus systemic disease activity index for primary Sjogren's syndrome. Ann Rheum Dis. 2010;69(6):1103\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos-Casals M, Brito-Zeron P, Solans R, et al. Systemic involvement in primary Sjogren's syndrome evaluated by the EULAR-SS disease activity index: analysis of 921 Spanish patients (GEAS-SS Registry). Rheumatology (Oxford). 2014;53(2):321\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui YY, Abdukiyum M, Xu XF, et al. Efficacy and safety of total glucosides of paeony in treating primary Sjogren's syndrome: a propensity-matched study. Eur Rev Med Pharmacol Sci. 2024;28(10):3523\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGottenberg JE, Ravaud P, Puechal X, et al. Effects of hydroxychloroquine on symptomatic improvement in primary Sjogren syndrome: the JOQUER randomized clinical trial. JAMA. 2014;312(3):249\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Chen Z, Li XM, Gao J, Zhao Y. [Recommendations for the diagnosis and treatment of Sjogren's syndrome in China]. Zhonghua nei ke za zhi. 2023;62(9):1059\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrito-Zeron P, Retamozo S, Ramos-Casals M. Sjogren syndrome. Med Clin (Barc). 2023;160(4):163\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStadler P, Feldmann HJ, Creighton C, et al. Clinical evidence for correlation of insufficient tissue oxygen supply (hypoxia) and tumor-associated proteolysis in squamous cell carcinoma of the head and neck. Int J Biol Markers. 2000;15(3):235\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraga CG, Croft KD, Kennedy DO, Tomas-Barberan FA. The effects of polyphenols and other bioactives on human health. Food Funct. 2019;10(2):514\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouarab Chibane L, Degraeve P, Ferhout H, Bouajila J, Oulahal N. Plant antimicrobial polyphenols as potential natural food preservatives. J Sci Food Agric. 2019;99(4):1457\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaydar NG, Baydar H. Phenolic compounds, antiradical activity and antioxidant capacity of oil-bearing rose (Rosa damascena Mill.) extracts. Ind Crops Prod. 2013;41:375\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEman MH. Antimicrobial activity of Rosa damascena petals extracts and chemical composition by gas chromatography-mass spectrometry (GC/MS) analysis. Afr J Microbiol Res. 2014;8(24):2359\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Day K. Bengal rose as an aid in the diagnosis of kerato-conjunctivitis sicca (Sjogren's syndrome). Med J Aust. 1951;2(21):708\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRu J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai F, Wang C, Fan X, et al. Novel biomarkers related to oxidative stress and immunity in chronic kidney disease. Heliyon. 2024;10(6):e27754.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen D, Xie Z, Yang J, et al. Stratification and prognostic evaluation of breast cancer subtypes defined by obesity-associated genes. Discov Oncol. 2024;15(1):133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang LC, Li N, Chen JL, et al. Molecular network mechanism in cerebral ischemia-reperfusion rats treated with human urine stem cells. Heliyon. 2024;10(7):e27508.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFasano S, Mauro D, Macaluso F, et al. Pathogenesis of primary Sjogren's syndrome beyond B lymphocytes. Clin Exp Rheumatol. 2020;38(Suppl 126):315\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSene D, Ismael S, Forien M, et al. Ectopic Germinal Center-Like Structures in Minor Salivary Gland Biopsy Tissue Predict Lymphoma Occurrence in Patients With Primary Sjogren's Syndrome. Arthritis Rheumatol. 2018;70(9):1481\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruno D, Tolusso B, Lugli G et al. B-Cell Activation Biomarkers in Salivary Glands Are Related to Lymphomagenesis in Primary Sjogren's Disease: A Pilot Monocentric Exploratory Study. Int J Mol Sci 2024;25(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJafarinia M, Sadat Hosseini M, Kasiri N, et al. Quercetin with the potential effect on allergic diseases. Allergy Asthma Clin Immunol. 2020;16:36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaccon TD, Nagpal R, Yadav H, et al. Senolytic Combination of Dasatinib and Quercetin Alleviates Intestinal Senescence and Inflammation and Modulates the Gut Microbiome in Aged Mice. J Gerontol Biol Sci Med Sci. 2021;76(11):1895\u0026ndash;905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Yao J, Han C, et al. Quercetin, Inflammation and Immunity. Nutrients. 2016;8(3):167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGruse J, Kanitz E, Weitzel JM, et al. Quercetin Feeding in Newborn Dairy Calves Cannot Compensate Colostrum Deprivation: Study on Metabolic, Antioxidative and Inflammatory Traits. PLoS ONE. 2016;11(1):e0146932.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HN, Shin SA, Choo GS, et al. Anti\u0026ndash;inflammatory effect of quercetin and galangin in LPS\u0026ndash;stimulated RAW264.7 macrophages and DNCB\u0026ndash;induced atopic dermatitis animal models. Int J Mol Med. 2018;41(2):888\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaneider NC, Mosheimer B, Reinisch N, Patsch JR, Wiedermann CJ. Inhibition of thrombin-induced signaling by resveratrol and quercetin: effects on adenosine nucleotide metabolism in endothelial cells and platelet-neutrophil interactions. Thromb Res. 2004;114(3):185\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajizadeh-Sharafabad F, Zahabi ES, Malekahmadi M, Zarrin R, Alizadeh M. Carotenoids supplementation and inflammation: a systematic review and meta-analysis of randomized clinical trials. Crit Rev Food Sci Nutr. 2021;62(29):8161\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaulmann A, Bohn T. Carotenoids, inflammation, and oxidative stress\u0026mdash;implications of cellular signaling pathways and relation to chronic disease prevention. Nutr Res. 2014;34(11):907\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLampronti I, Dechecchi MC, Rimessi A, et al. beta-Sitosterol Reduces the Expression of Chemotactic Cytokine Genes in Cystic Fibrosis Bronchial Epithelial Cells. Front Pharmacol. 2017;8:236.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrera MJ, Aguilera S, Castro I, et al. Tofacitinib counteracts IL-6 overexpression induced by deficient autophagy: implications in Sjogren's syndrome. Rheumatology (Oxford). 2021;60(4):1951\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastro I, Albornoz N, Aguilera S, et al. Aberrant MUC1 accumulation in salivary glands of Sjogren's syndrome patients is reversed by TUDCA in vitro. Rheumatology (Oxford). 2020;59(4):742\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSisto M, Tamma R, Ribatti D, Lisi S. IL-6 Contributes to the TGF-beta1-Mediated Epithelial to Mesenchymal Transition in Human Salivary Gland Epithelial Cells. Arch Immunol Ther Exp (Warsz). 2020;68(5):27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Liang Y, Zhang Z, Zhang Z, Yang Z. Relationships between increased circulating YKL-40, IL-6 and TNF-alpha levels and phenotypes and disease activity of primary Sjogren's syndrome. Int Immunopharmacol. 2020;88:106878.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebreceni IL, Barr JY, Upton EM, Chen YG, Lieberman SM. IL-27 promotes pathogenic T cells in a mouse model of Sjogren's disease. Clin Immunol. 2024;264:110260.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sjögren's Syndrome, Rose, molecular docking, network pharmacology","lastPublishedDoi":"10.21203/rs.3.rs-4793586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4793586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eModern medicine has yet to cure the xerostomia and other symptoms caused by the early onset of Sj\u0026ouml;gren's Syndrome (SS). Rose, a common flower used in traditional Chinese medicine, is investigated in this study using network pharmacology and molecular docking techniques to explore its potential mechanisms of action against SS.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThe active components and targets of rose were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The genes encoding these targets were identified using the UniProt database. Additionally, SS-related targets were identified from the GeneCards and OMIM databases. By intersecting the compound targets with SS targets, the predicted targets for rose in the treatment of SS were obtained. A \"candidate compound-target\" network was constructed using Cytoscape 3.10.2, and a protein-protein interaction network was built. Further analysis of active compounds and their targets was performed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using R software. Finally, molecular docking techniques were employed to validate the affinity between the candidate compounds and key targets.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eQuercetin, beta-carotene, beta-sitosterol, and demethoxycapillarisin in rose interacted with IL6, TNF, AKT1, ALB, IL1B, TP53, JUN, TGFB1, BCL2, and ESR1. These findings indicate that rose exerts therapeutic effects on peripheral glandular damage in SS and its associated cardiovascular diseases and tumorigenesis through anti-inflammatory and antioxidant pathways.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eFrom a network pharmacology perspective, this study systematically identified the main active ingredients, targets, and specific mechanisms of rose in treating SS, providing a theoretical basis and research direction for further exploration of rose's therapeutic mechanisms in SS.\u003c/p\u003e","manuscriptTitle":"The Mechanism of Rose in Treating Sjögren's Syndrome Based on Network Pharmacology and Molecular Docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-07 09:08:41","doi":"10.21203/rs.3.rs-4793586/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":"efc8b09c-d1b8-40e1-9133-ce2eba9e4302","owner":[],"postedDate":"August 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-07T09:08:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-07 09:08:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4793586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4793586","identity":"rs-4793586","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}