Network pharmacological analysis and molecular mechanism of resveratrol inhibiting inflammation

Network pharmacological analysis and molecular mechanism of resveratrol inhibiting inflammation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Network pharmacological analysis and molecular mechanism of resveratrol inhibiting inflammation Hui Deng, Xiao-Liang Yang, Kuai Liang, Hong-Yu Wang, Jing Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7153466/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 Objective To explore the protective effect and mechanism of resveratrol in the treatment of chronic inflammatory diseases through network pharmacology, machine learning and molecular docking techniques. Methods TCMSP, Pharm Mapper, SEA and SwissTargetPrediction and GEO databases were used to identify potential targets associated with resveratrol and chronic inflammatory diseases. These include Alzheimer's disease (AD), atherosclerosis (AS), chronic obstructive pulmonary disease (COPD), hepatitis B (HB), multiple sclerosis (MS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). The protein interaction network was constructed using STRING platform, and the KEGG pathway enrichment analysis was performed using R software. Machine learning was used to screen core genes and make molecular docking with resveratrol. GEO database was used to verify the expression of core genes and ROC curve analysis was performed. Results Resveratrol had strong binding force with the core targets (BIRC3, CA3, PGR, CXCL8, TNF, TNFSF10 and NFKBIA). These targets were significantly up-regulated in the gene expression data of the corresponding GEO database and showed good diagnostic value (the area under ROC curve ranged from 0.680 to 0.959). Conclusion These results provide a new molecular target and theoretical basis for the application of resveratrol in the treatment of inflammatory diseases. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Biological sciences/Immunology Health sciences/Rheumatology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Inflammation is a defensive response of the body to stimuli such as infections, autoimmune diseases, chemical agents, and physical factors, aimed at eliminating harmful substances to maintain health 1 .However, dysregulated inflammatory responses can lead to excessive or prolonged tissue damage, contributing to the development of chronic inflammatory diseases 2 , including Alzheimer's disease (AD), atherosclerosis (AS), chronic obstructive pulmonary disease (COPD), hepatitis B (HB), multiple sclerosis (MS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE).The pathogenesis of these diseases is complex and involves interactions among multiple genes, which remain incompletely understood. Currently available pharmacological treatments have shown limited efficacy in controlling or curing these conditions, failing to achieve optimal therapeutic outcomes. Therefore, the development of anti-inflammatory drugs is of critical importance. Traditional Chinese medicine extracts contain a plethora of bioactive compounds, such as saponins, flavonoids, alkaloids, and polyphenols, which have been reported to modulate immune and inflammatory responses 3 . Resveratrol (Res) is a naturally occurring polyphenolic phytochemical widely distributed in plants like grapes, Polygonum cuspidatum, peanuts, mulberries, and blueberries 4 . Res exhibits various biological functions, including anti-inflammatory, anti-tumor, antibacterial, cardiovascular protective, antioxidant, and modulation of glucose and lipid metabolism 5 .It has been extensively applied in research related to cancer, tumors, metabolic diseases, cardiovascular diseases, and rheumatic autoimmune diseases. In RA, Res effectively activates Sirt1, thereby modulating multiple immune cells and signaling pathways involved in inflammation, such as macrophage differentiation, the NF-κB signaling pathway, the AP-1 signaling pathway, and the MAPK signaling pathway 6 . Research by Kong et al. 7 demonstrated that Res enhances the antioxidant capacity and estrogen levels in an AD model by activating the Nrf2/heme oxygenase-1 (HO-1) signaling pathway. Nanoformulations of Res combined with selenium have been shown to maximize the efficacy of Res against AD through their antioxidant properties and anti-inflammatory effects that improve neurocognitive function and regulate signaling pathways 8 . Additionally, Res has been found to effectively inhibit the replication and infection of several viruses, including hepatitis B virus and influenza virus. A study by Pan et al. revealed that Res inhibited toxicity in HepG2.2.15 cells in vitro and reduced hepatitis B virus replication 9 . Some preclinical studies indicate that Res may exert cardiovascular protective effects by lowering plasma triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) levels, while increasing high-density lipoprotein cholesterol (HDL-C) levels 10 . Res has been shown to upregulate the expression of low-density lipoprotein receptors (LDL-R) in hepatocytes in vitro, further contributing to the reduction of blood LDL-C levels 11 . Furthermore, the antioxidant properties of Res lead to decreased LDL oxidation (a direct contributor to atherosclerosis) and induce several endogenous antioxidant systems and anti-inflammatory characteristics 12 . Given the current insufficient understanding of the specific mechanisms underlying the effects of Res, this study employs a combination of network pharmacology, machine learning, and molecular docking to explore and elucidate the mechanisms by which Res inhibits chronic inflammatory diseases. In this research, network pharmacology is utilized to predict potential mechanisms, machine learning algorithms are employed to identify key targets, and molecular docking is applied for simulative analysis to estimate binding stability. This integrative approach not only aids in revealing the interaction patterns between Res and chronic inflammatory diseases but also provides a solid theoretical foundation for clarifying its mechanisms of action. The ultimate goal is to uncover new insights into how Res modulates inflammatory responses. Results Role of Resveratrol in the Treatment of Alzheimer's Disease. AD is characterized by neuronal loss and the disruption of neural networks. Microglia, the primary immune phagocytes in the brain, initially exhibit anti-inflammatory functions and help clear the accumulation of pathological proteins such as β-amyloid and tau 13 , 14 . However, as the disease progresses, microglia gradually shift towards a pro-inflammatory phenotype, thereby promoting the onset of chronic inflammatory responses. Li et al. found that Res can alleviate inflammation and oxidative stress in microglial cell lines through the STAT1 and Nrf2/Keap1/SLC7A11 pathways 15 . A recent randomized double-blind trial 16 revealed that there were no significant differences in the levels of Aβ40 in the blood and cerebrospinal fluid of patients treated with Res, whereas the placebo group exhibited a marked decrease in Aβ40 levels in both blood and cerebrospinal fluid by the end of the study compared to baseline. This indicates a protective effect of Res against AD. The 67 intersection targets of Res and AD were imported into the STRING platform to construct a protein-protein interaction (PPI) network (Fig. 1A). The KEGG enrichment analysis of the intersected targets was conducted using R software (Fig. 1B), highlighting pathways that may be involved in the therapeutic effects of Res on AD, such as the PI3K-Akt signaling pathway, MAPK signaling pathway, and TNF signaling pathway.To further identify core targets, three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were employed. By conducting intersection analysis of the results from these three algorithms, one candidate core gene, BIRC3, was ultimately identified as the most promising (Fig. 1C). Molecular docking results indicated that the binding energy between Res and BIRC3 was − 5.19 kcal/mol, suggesting a strong binding affinity between the two (Fig. 1D). Additionally, BIRC3 was found to be significantly upregulated in AD patients (Fig. 1E) and exhibited good diagnostic value, with an area under the curve (AUC) of 0.860 (Fig. 1F). Role of Resveratrol in the Treatment of Atherosclerosis. AS is a chronic inflammatory disease affecting the arterial walls, and its underlying mechanisms are not yet fully understood. It is currently believed to be primarily associated with endothelial cell damage, lipid accumulation, foam cell formation, inflammatory responses, and oxidative stress 17 . Recent studies suggest that Res may exert beneficial effects on cardiovascular health by mitigating excessive inflammatory responses. The STRING platform integrated a total of 57 intersection targets of Res and AS to establish a protein-protein interaction (PPI) network (Fig. 2 A). Enrichment analysis results indicated that potential pathways associated with Res treatment for AS include lipid atherosclerosis, the AGE-RAGE signaling pathway, the TNF signaling pathway, and the IL-17 signaling pathway (Fig. 2 B). Three machine learning algorithms identified three promising candidate core genes: TNF, DPP4, and NOX4 (Fig. 2 C). Molecular docking results revealed that the binding energies between Res and the target proteins (TNF, DPP4, and NOX4) were − 7.32 kcal/mol, -5.51 kcal/mol, and − 5.59 kcal/mol, respectively (Fig. 2 D). Furthermore, TNF expression was found to be upregulated in AS (Fig. 2 E) and exhibited excellent diagnostic value, with an area under the curve (AUC) of 0.959 (Fig. 2 F). Role of Resveratrol in the Treatment of Chronic Obstructive Pulmonary Disease. COPD is an inflammatory lung disease characterized by complex pathological features and unclear etiology 18 . Res protects the lungs by activating SIRT1 and reducing oxidative stress through Nrf2-mediated antioxidant enzymes, showing potential as an alternative to corticosteroids in COPD and cancer treatment 19 . Figure 3 A illustrates the protein-protein interaction (PPI) network constructed from 26 intersection targets of Res and COPD. Enrichment analysis results suggest that Res may influence the progression of COPD through the AGE-RAGE signaling pathway and the HIF-1 signaling pathway (Fig. 3 B). Utilizing three machine learning algorithms, four core genes were ultimately identified (Fig. 3 C). Molecular docking revealed that the binding energies between Res and the target proteins (EDN1, BCL2A1, MPO, and CA3) were − 4.30 kcal/mol, -5.03 kcal/mol, -4.95 kcal/mol, and − 6.54 kcal/mol, respectively (Fig. 3 D). Notably, CA3 was found to be highly expressed in COPD and demonstrated good diagnostic value, with an area under the curve (AUC) of 0.789 (Fig. 3 E and 3 F). Role of Resveratrol in the Treatment of Hepatitis B. Res has demonstrated potential efficacy in the treatment of hepatitis B (HB) through various mechanisms, including the inhibition of HB virus replication, reduction of liver damage, and enhancement of immune function 9 . Figure 4 A presents the protein-protein interaction (PPI) network constructed from 25 intersection targets of Res and HB. Enrichment analysis results indicate that the pathways potentially involved in Res's treatment of HB include the HBV signaling pathway and the estrogen receptor signaling pathway (Fig. 4 B).Using three machine learning algorithms, two core targets were identified (Fig. 4 C). Molecular docking results revealed that the binding energies between Res and the target proteins (TUBB3 and PGR) were − 4.42 kcal/mol and − 4.68 kcal/mol, respectively (Fig. 4 D). Additionally, PGR expression was significantly elevated in HB patients (Fig. 4 E), with an area under the curve (AUC) of 0.680 (Fig. 4 F). Role of Resveratrol in Multiple Sclerosis. Multiple sclerosis (MS) is an immune-mediated inflammatory and demyelinating disease of the central nervous system (CNS). The protein-protein interaction (PPI) network constructed from the 12 intersection targets of Res and MS is illustrated in Fig. 5 A. Enrichment analysis results suggest that Res may exert anti-MS effects through pathways such as the TNF signaling pathway, NF-κB pathway, and IL-17 signaling pathway (Fig. 5 B). Three machine learning algorithms identified the core targets CXCL8 and YWHAG (Fig. 5 C). Molecular docking results indicated that the binding energies between Res and the target proteins (CXCL8 and YWHAG) were − 6.33 kcal/mol and − 3.36 kcal/mol, respectively (Fig. 5 D). Notably, CXCL8 was significantly upregulated in MS (Fig. 5 E) and demonstrated good diagnostic value, with an area under the curve (AUC) of 0.877 (Fig. 5 F). Role of Resveratrol in the Treatment of Rheumatoid Arthritis. Studies have indicated that Res can alleviate joint inflammation and protect articular cartilage in rheumatoid arthritis (RA) through multiple pathways, positioning it as a promising novel phytotherapy for RA 6 . Figure 6 A presents the network diagram of 84 core targets associated with Res and RA. Enrichment analysis results suggest that Res may exert anti-RA effects via the PI3K-Akt and FoxO signaling pathways (Fig. 6 B).Three machine learning algorithms identified the core targets TNFSF10 and EGFR (Fig. 6 C). Molecular docking results revealed that the binding energies between Res and the target proteins (TNFSF10 and EGFR) were − 6.39 kcal/mol and − 4.96 kcal/mol, respectively (Fig. 6 D). Notably, TNFSF10 expression was found to be upregulated in RA (Fig. 6 E) and demonstrated excellent diagnostic value, with an area under the curve (AUC) of 0.935 (Fig. 6 F). Role of Resveratrol in the Treatment of Systemic Lupus Erythematosus. SLE is a diffuse connective tissue disease characterized by prominent immune-mediated inflammation, which can lead to damage in vital organs such as the kidneys and hematologic system. Figure 7 A illustrates the network diagram of 49 intersection targets associated with Res and SLE. Enrichment analysis results indicate that the pathways potentially involved in Res's treatment of SLE include the IL-17 signaling pathway and the MAPK signaling pathway (Fig. 7 B).Machine learning algorithms identified the core targets HERC5 and NFKBIA (Fig. 7 C). Molecular docking results demonstrated that the binding energies between Res and the target proteins (HERC5 and NFKBIA) were − 4.70 kcal/mol and − 5.35 kcal/mol, respectively (Fig. 7 D). Notably, NFKBIA was found to be highly expressed in SLE (Fig. 7 E) and exhibited good diagnostic value, with an area under the curve (AUC) of 0.824 (Fig. 7 F). Discussion This study utilized network pharmacology, machine learning, and molecular docking techniques to explore the potential molecular pathways and key targets regulating the effects of Res on chronic inflammatory diseases, elucidating the significance of seven key genes: BIRC3, CA3, PGR, CXCL8, TNF, TNFSF10, and NFKBIA in chronic inflammatory conditions. BIRC3 (Baculoviral IAP repeat-containing protein 3), also known as cIAP2 (cellular inhibitor of apoptosis protein 2), is a protein involved in the regulation of apoptosis (programmed cell death) and inflammatory responses. BIRC3 is currently found to be highly expressed in various cancers, including gallbladder cancer, gastric cancer, renal cell carcinoma, and hepatocellular carcinoma 20 , 21 . BIRC3 is part of the NF-κB signaling pathway, where it stabilizes and activates the TAB2/TAB3 proteins within the TAK1 complex, thereby promoting the phosphorylation of the IKK complex, leading to the degradation of IκBα. This process releases NF-κB transcription factors into the nucleus, inducing the expression of genes encoding pro-inflammatory mediators and facilitating inflammation and disease progression 22 . Additionally, BIRC3 functions as an E3 ubiquitin ligase, modulating the immune response in rheumatoid arthritis (RA) by regulating TNF receptor signaling and the production of inflammatory cytokines. It interacts with apoptotic signaling pathways to prevent cell death, thereby participating in the proliferation and survival of fibroblast-like synoviocytes (FLS) 23 . CA3 (Carbonic Anhydrase III) is an isoenzyme that is highly expressed in skeletal muscle, particularly abundant in type I fibers and present at very low levels in type II fibers 24 , 25 . CA3 exhibits hydration activity that regulates intracellular pH and possesses antioxidant properties, functioning as an immunomodulator by inhibiting the secretion of inflammatory cytokines. Research has shown that as ulcerative colitis worsens, the levels of CA3 decrease in both colonic tissue and serum 26 , indicating its anti-inflammatory effects. In patients with skeletal muscle atrophy due to COPD, CA3 levels are significantly reduced compared to normal controls; however, data from this study indicate that CA3 expression is upregulated in COPD. The reduction of type I fibers in the peripheral skeletal muscle of COPD patients leads to decreased CA3 levels, while the upregulation of CA3 expression may be attributed to the higher generation of reactive oxygen species (ROS) in the remaining type I fibers compared to normal levels. PGR (Progesterone Receptor) is a nuclear receptor transcription factor primarily expressed in the granulosa cells of ovulatory follicles, playing a crucial role in the normal functioning of the reproductive system 27 . Additionally, researchers have observed that the risk of developing RA in pregnant women is reduced by 2 to 5 times compared to non-pregnant women, which may be associated with elevated levels of estrogen, progesterone, and adrenal corticosteroids during pregnancy 28 . Pregnancy-level progesterone can induce T cells to produce IL-4 and IL-10, promoting the proliferation and differentiation of T helper (Th) cells. During pregnancy, lymphocytes express progesterone receptors and release progesterone-induced blocking factor (PIBF), which exhibits strong anti-natural killer (NK) cell activity and also secretes IL-10, contributing to anti-RA effects 29 . Progesterone facilitates the differentiation of T cells from human fetal umbilical cord blood into regulatory T (Treg) cells while inhibiting their differentiation into Th17 cells 30 , thereby reducing the production of pro-inflammatory factors. In multiple sclerosis (MS), Tregs express high levels of estrogen and progesterone receptors, and each hormone enhances the immunosuppressive function of Tregs in vitro 31 . Therefore, understanding how progesterone and PGR precisely regulate the balance between Treg and Th17 cells is particularly important. CXCL8, also known as IL-8, is one of the key chemokines responsible for the recruitment of neutrophils, produced and released by various cell types in response to a range of stimuli, including microbial products, tissue damage, and hypoxia 32 . CXCL8 exerts multiple biological functions by binding to its two receptors, CXCR1 and CXCR2, which include promoting inflammatory responses, angiogenesis, mitosis, and cellular proliferation 33 . CXCL8 and its receptors are involved in the pathogenesis of various diseases, including rheumatoid arthritis, inflammatory bowel disease, chronic obstructive pulmonary disease, asthma, and cancer 34 . The neutrophil extracellular traps induced by CXCL8 exacerbate atherosclerosis by activating NF-κB signaling in macrophages 35 . Elevated levels of CXCL8, neutrophils, and neutrophil-derived enzymes have been detected in the blood of patients with chronic inflammatory demyelinating diseases such as MS 36 . Recent studies in 2024 have shown that serum levels of CXCL8 are reduced in patients with relapsing-remitting MS, and the concentration of CXCL8 is negatively correlated with anti-EBNA IgG levels. This observation may be explained by a compensatory anti-inflammatory regulatory mechanism in MS, warranting further investigation to elucidate the underlying mechanisms 37 . TNF (Tumor Necrosis Factor) and TNFSF10 (TNF-related apoptosis-inducing ligand, also known as TRAIL) are both cytokines belonging to the tumor necrosis factor superfamily. During acute inflammation, TNF rapidly activates the NF-κB pathway, promoting the inflammatory response. Conversely, NFKBIA (IκBα, NF-κB inhibitor α) acts as a negative feedback regulator, helping to control the excessive activation of NF-κB and maintain appropriate levels of inflammation 38 . The role of TNFSF10 is relatively complex; it can reduce inflammatory stimuli by inducing apoptosis under specific conditions and influence the inflammatory process by modulating immune cell functions. In this study, NFKBIA was found to be highly expressed in SLE. However, it was observed that the expression of IκB-α mRNA in the spleen of lupus-susceptible mice was lower compared to wild-type mice 39 . We speculate that post-translational modifications or other mechanisms may be affecting the functional stability of NFKBIA. Methods Prediction of Targets for Resveratrol in Inflammatory Diseases. Using "resveratrol" as the search keyword, we obtained resveratrol-related targets from several pharmacological databases and analysis platforms, including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) ( https://old.tcmsp-e.com/index.php ), Pharm Mapper, SEA, and SwissTargetPrediction. After removing non-human targets and integrating the identified targets from these four pharmacological databases, duplicate targets were eliminated. Subsequently, we accessed the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/GEO/ ) and input the keywords "Alzheimer's disease," "atherosclerosis," "chronic obstructive pulmonary disease," "Hepatitis B," "Multiple sclerosis," "rheumatoid arthritis," and "systemic lupus erythematosus" to download relevant datasets. The downloaded datasets were then processed for normalization using the "limma" package in R version 4.2.1. We set the filtering criteria at P 0.5 to identify differentially expressed genes (DEGs) associated with these inflammatory diseases. Intersection Targets and Construction of Protein-Protein Interaction (PPI) Network. By intersecting the targets of Res with those associated with inflammatory diseases, we identified potential targets through which Res exerts its anti-inflammatory effects. The STRING database ( https://cn.string-db.org/ ) was utilized to construct a protein-protein interaction (PPI) network for these potential targets, with a confi-dence threshold set at 0.4. The resulting PPI network was visualized graphically. Additionally, the expression levels of the intersected targets in the GEO datasets were illustrated using a heatmap generated with the "ComplexHeatmap" package in R version 4.2.1. GO and KEGG Enrichment Analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using R version 4.2.1 along with relevant R packages (clusterProfiler and org.Hs.eg.db) 40 , 41 . A significance threshold of P < 0.05 was established, and the results were visualized using bar plots and bubble plots to illustrate the enriched bio-logical processes and pathways. Machine Learning for Core Gene Selection. The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) methods were employed to classify key targets. To differentiate between the control and disease groups, 5-fold cross-validation was performed using the "glmnet" package. The SVM-REF algorithm was utilized to generate a hyperplane with the maximum margin in the feature space to distinguish positive and negative instances. The "e1071" and "svmRadial" packages in R were used to conduct SVM-RFE analysis for selecting high-quality genes. Recursive partitioning was applied to construct binary trees in the Random Forest (RF) model. The "RandomForest" package was utilized to build the RF classification model, ranking the key genes based on the Gini index to identify feature expression targets. Validation of Core Gene Expression and ROC Curve Analysis. The Wilcoxon test was employed to compare differences between the control and disease groups, with P < 0.05 indi-cating statistical significance. The "pROC" package in R was used to generate Receiver Operating Characteristic (ROC) curves and calculate the area under the curve (AUC) to assess diagnostic value. Molecular Docking. The molecular structure of Res was obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). The protein structures of the core genes associated with the inflammatory diseases were retrieved from the RCSB PDB database ( https://www.rcsb.org/ ). Molecular docking of the receptor and ligand was performed using AutoDockTools software, and the results were visualized using PyMOL software. Conclusions In summary, this study systematically identified potential therapeutic targets of Res in AD, AS, COPD, HB, MS, RA, and SLE, and validated their binding capabilities through molecular docking. These findings offer new insights and targets for the treatment of inflammatory diseases. Future research could further validate the efficacy of these targets and explore the synergistic effects of Res in combination with other therapies, aiming to provide more options for clinical treatment. Declarations Acknowledgements The authors wish to thank Sichuan Taikang Hospital for assistance during the present study. Funding This research received no external funding. Author contributions statement Conceptualization, H.-D., J.-L.C. and K.-H.L.; Investigation, H.-D., X.-L.Y and K.-L; Methodology, H.-D., X.-L.Y. and H.-Y.W; Supervision, H.-D., J.-L.C. and K.-H.L; Visualization, H.-D. and J.J.; Writing—Original draft, H.-D.; Writing—Review and editing, H.-D., J.-L.C. and K.-H.L. All authors have read and agreed to the published version of the manuscript. Data availability All data generated or analysed during this study are included in this published article [and its supplementary information files]. Competing interests The authors declare no competing interest. References Aggarwal, B. B., Vijayalekshmi, R. V. & Sung, B. Targeting inflammatory pathways for prevention and therapy of cancer: short-term friend, long-term foe. Clin Cancer Res 15 , 425-430, doi:10.1158/1078-0432.CCR-08-0149 (2009). Liu, T., Zhang, L., Joo, D. & Sun, S.-C. NF-κB signaling in inflammation. Signal Transduct Target Ther 2 , 17023-17023, doi:10.1038/sigtrans.2017.23 (2017). 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Neutrophil extracellular traps induced by IL-8 aggravate atherosclerosis via activation NF-κB signaling in macrophages. Cell Cycle 18 , 2928-2938, doi:10.1080/15384101.2019.1662678 (2019). Cambier, S., Gouwy, M. & Proost, P. The chemokines CXCL8 and CXCL12: molecular and functional properties, role in disease and efforts towards pharmacological intervention. Cellular & Molecular Immunology 20 , 217-251, doi:10.1038/s41423-023-00974-6 (2023). Košćak Lukač, J. et al. Serum Concentrations of Chemokines CCL20, CXCL8 and CXCL10 in Relapsing-Remitting Multiple Sclerosis and Their Association with Presence of Antibodies against Epstein-Barr Virus. Int J Mol Sci 25 , doi:10.3390/ijms25158064 (2024). Yu, H., Lin, L., Zhang, Z., Zhang, H. & Hu, H. Targeting NF-κB pathway for the therapy of diseases: mechanism and clinical study. Signal Transduct Target Ther 5 , 209, doi:10.1038/s41392-020-00312-6 (2020). Kalergis, A. M. et al. Modulation of nuclear factor-kappaB activity can influence the susceptibility to systemic lupus erythematosus. Immunology 128 , e306-e314, doi:10.1111/j.1365-2567.2008.02964.x (2009). Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28 , 27-30 (2000). Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16 , 284-287, doi:10.1089/omi.2011.0118 (2012). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7153466","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495650015,"identity":"88428ca8-1bc1-41ed-8cec-3d14744362f3","order_by":0,"name":"Hui Deng","email":"","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Deng","suffix":""},{"id":495650017,"identity":"dae5f2d3-c949-4c6e-a68e-17e4acd6a4b6","order_by":1,"name":"Xiao-Liang Yang","email":"","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Liang","middleName":"","lastName":"Yang","suffix":""},{"id":495650019,"identity":"b4c4eea4-5d7e-4a23-ade1-b3223549b014","order_by":2,"name":"Kuai Liang","email":"","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kuai","middleName":"","lastName":"Liang","suffix":""},{"id":495650021,"identity":"d3f54f81-ba2f-4d9a-b8bf-f7db8bf16ff6","order_by":3,"name":"Hong-Yu Wang","email":"","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong-Yu","middleName":"","lastName":"Wang","suffix":""},{"id":495650022,"identity":"4dc24f34-7107-47c0-86bb-4895ccd543fc","order_by":4,"name":"Jing Jiang","email":"","orcid":"","institution":"The Eighth People's Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Jiang","suffix":""},{"id":495650025,"identity":"a3f3be0f-78e9-406b-9693-3a3d8de50d4e","order_by":5,"name":"Jian-Li Cui","email":"","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian-Li","middleName":"","lastName":"Cui","suffix":""},{"id":495650027,"identity":"59ee3dcf-ece9-47d8-9d74-68e25cc8cdda","order_by":6,"name":"Hong-Kun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACPmYGNhBtwMDA2HDwQwWQyQ5i4tHChtDCfPCxxBkGBh5mQloY4FrYkg1424jRws5j9uDjjlpjg9s9ZhKS82wS9zMzH3w4g8FOTheHPjZmHnPDmWeOmxncOWMmUbgtLbGHmS3ZcANDsrHZAZxazKR5247ZGNzIAdqy7TBQC4+Z5AOGA4nbiNLCO4d4LTVmBjfSgN5vgGrZgFcLW5nkzLYDxpI3koGBfCzNuOcw0C8zDHD7hZ//8DaJj211hn03EoFRWWMj297efPBhT4WdHC4tUHAYXcAAr3IQqCOoYhSMglEwCkYwAACn1VZvEHz8qQAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hong-Kun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-18 03:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7153466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7153466/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88326021,"identity":"f59067c7-b19d-46ce-8253-92e0189700b7","added_by":"auto","created_at":"2025-08-05 09:48:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1679008,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of intersection targets. \u0026nbsp;(B) KEGG enrichment analysis. (C) Select key differential genes using multiple machine learning algorithms. (D) Molecular docking results of Res and BIRC3(***\u003cem\u003eP\u003c/em\u003e＜0.001). (E) Expression of BIRC3. (F) ROC curve\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/6720225fb451b1e82cf329ca.jpg"},{"id":88325587,"identity":"a7abe3c0-bca9-4ac7-b246-8879ebbdf5ba","added_by":"auto","created_at":"2025-08-05 09:40:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1772541,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of intersection targets. (B) KEGG enrichment analysis. (C) Select key differential genes using multiple machine learning algorithms. (D) Molecular docking results of Res and TNF. (E) Expression of TNF(***\u003cem\u003eP\u003c/em\u003e＜0.001). (F) ROC curve\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/fba0b9786f97ff954b91a86e.jpg"},{"id":88325589,"identity":"7993c4d3-3c0e-4585-a7e5-915e70d6667c","added_by":"auto","created_at":"2025-08-05 09:40:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1499941,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of intersection targets. (B) KEGG enrichment analysis. (C) Select key differential genes using multiple machine learning algorithms. (D) Molecular docking results of Res and CA3. (E) Expression of CA3(***\u003cem\u003eP\u003c/em\u003e＜0.001). (F) ROC curve.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/5ce3b3695baa47ef5760c052.jpg"},{"id":88325604,"identity":"a9050f83-74ad-4ca6-a8a7-de9d674639e1","added_by":"auto","created_at":"2025-08-05 09:40:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1513046,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of intersection targets. (B) KEGG enrichment analysis. (C) Select key differential genes using multiple machine learning algorithms. (D) Molecular docking results of Res and PGR. (E) Expression of PGR (*\u003cem\u003eP\u003c/em\u003e＜0.05). (F) ROC curve.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/4f12e3a68198acfda3233e7e.jpg"},{"id":88325611,"identity":"2f8ea26b-d320-4254-bfbc-68dc95be8439","added_by":"auto","created_at":"2025-08-05 09:40:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1271283,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of intersection targets. (B) KEGG enrichment analysis. (C) Select key differential genes using multiple machine learning algorithms. (D) Molecular docking results of Res and CXCL8. (E) Expression of CXCL8(***\u003cem\u003eP\u003c/em\u003e＜0.001). (F) ROC curve.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/cce8da52462698250816fb1f.jpg"},{"id":88325592,"identity":"4b00f2c5-bccb-4ea9-8f2c-39c7d35768b3","added_by":"auto","created_at":"2025-08-05 09:40:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1647232,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of intersection targets. (B) KEGG enrichment analysis. (C) Select key differential genes using multiple machine learning algorithms. (D) Molecular docking results of Res and TNFSF10. (E) Expression of TNFSF10(***\u003cem\u003eP\u003c/em\u003e＜0.001). (F) ROC curve\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/0505c62480640742371c579c.jpg"},{"id":88325599,"identity":"9b5cda3f-5796-4b77-983c-99e3c19b356b","added_by":"auto","created_at":"2025-08-05 09:40:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1547652,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram of intersection targets. (B) KEGG enrichment analysis. (C) Select key differential genes using multiple machine learning algorithms.(D) Molecular docking results of Res and NFKBIA.(E) Expression of NFKBIA(***\u003cem\u003eP\u003c/em\u003e＜0.001). (F) ROC curve\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/2fce0c6a04080a176a5de46b.jpg"},{"id":88328340,"identity":"99344963-c999-44e8-b83e-caa3bcae7c6e","added_by":"auto","created_at":"2025-08-05 10:12:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11693527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7153466/v1/000aa24a-9217-4339-9c90-f2496c92140d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network pharmacological analysis and molecular mechanism of resveratrol inhibiting inflammation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInflammation is a defensive response of the body to stimuli such as infections, autoimmune diseases, chemical agents, and physical factors, aimed at eliminating harmful substances to maintain health \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.However, dysregulated inflammatory responses can lead to excessive or prolonged tissue damage, contributing to the development of chronic inflammatory diseases \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, including Alzheimer's disease (AD), atherosclerosis (AS), chronic obstructive pulmonary disease (COPD), hepatitis B (HB), multiple sclerosis (MS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE).The pathogenesis of these diseases is complex and involves interactions among multiple genes, which remain incompletely understood. Currently available pharmacological treatments have shown limited efficacy in controlling or curing these conditions, failing to achieve optimal therapeutic outcomes. Therefore, the development of anti-inflammatory drugs is of critical importance.\u003c/p\u003e\u003cp\u003eTraditional Chinese medicine extracts contain a plethora of bioactive compounds, such as saponins, flavonoids, alkaloids, and polyphenols, which have been reported to modulate immune and inflammatory responses \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Resveratrol (Res) is a naturally occurring polyphenolic phytochemical widely distributed in plants like grapes, Polygonum cuspidatum, peanuts, mulberries, and blueberries \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Res exhibits various biological functions, including anti-inflammatory, anti-tumor, antibacterial, cardiovascular protective, antioxidant, and modulation of glucose and lipid metabolism \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.It has been extensively applied in research related to cancer, tumors, metabolic diseases, cardiovascular diseases, and rheumatic autoimmune diseases. In RA, Res effectively activates Sirt1, thereby modulating multiple immune cells and signaling pathways involved in inflammation, such as macrophage differentiation, the NF-κB signaling pathway, the AP-1 signaling pathway, and the MAPK signaling pathway \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Research by Kong et al. \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e demonstrated that Res enhances the antioxidant capacity and estrogen levels in an AD model by activating the Nrf2/heme oxygenase-1 (HO-1) signaling pathway. Nanoformulations of Res combined with selenium have been shown to maximize the efficacy of Res against AD through their antioxidant properties and anti-inflammatory effects that improve neurocognitive function and regulate signaling pathways \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, Res has been found to effectively inhibit the replication and infection of several viruses, including hepatitis B virus and influenza virus. A study by Pan et al. revealed that Res inhibited toxicity in HepG2.2.15 cells in vitro and reduced hepatitis B virus replication \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Some preclinical studies indicate that Res may exert cardiovascular protective effects by lowering plasma triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) levels, while increasing high-density lipoprotein cholesterol (HDL-C) levels \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Res has been shown to upregulate the expression of low-density lipoprotein receptors (LDL-R) in hepatocytes in vitro, further contributing to the reduction of blood LDL-C levels \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Furthermore, the antioxidant properties of Res lead to decreased LDL oxidation (a direct contributor to atherosclerosis) and induce several endogenous antioxidant systems and anti-inflammatory characteristics \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the current insufficient understanding of the specific mechanisms underlying the effects of Res, this study employs a combination of network pharmacology, machine learning, and molecular docking to explore and elucidate the mechanisms by which Res inhibits chronic inflammatory diseases. In this research, network pharmacology is utilized to predict potential mechanisms, machine learning algorithms are employed to identify key targets, and molecular docking is applied for simulative analysis to estimate binding stability. This integrative approach not only aids in revealing the interaction patterns between Res and chronic inflammatory diseases but also provides a solid theoretical foundation for clarifying its mechanisms of action. The ultimate goal is to uncover new insights into how Res modulates inflammatory responses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eRole of Resveratrol in the Treatment of Alzheimer's Disease.\u003c/b\u003e AD is characterized by neuronal loss and the disruption of neural networks. Microglia, the primary immune phagocytes in the brain, initially exhibit anti-inflammatory functions and help clear the accumulation of pathological proteins such as β-amyloid and tau \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, as the disease progresses, microglia gradually shift towards a pro-inflammatory phenotype, thereby promoting the onset of chronic inflammatory responses. Li et al. found that Res can alleviate inflammation and oxidative stress in microglial cell lines through the STAT1 and Nrf2/Keap1/SLC7A11 pathways \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A recent randomized double-blind trial \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e revealed that there were no significant differences in the levels of Aβ40 in the blood and cerebrospinal fluid of patients treated with Res, whereas the placebo group exhibited a marked decrease in Aβ40 levels in both blood and cerebrospinal fluid by the end of the study compared to baseline. This indicates a protective effect of Res against AD. The 67 intersection targets of Res and AD were imported into the STRING platform to construct a protein-protein interaction (PPI) network (Fig.\u0026nbsp;1A). The KEGG enrichment analysis of the intersected targets was conducted using R software (Fig.\u0026nbsp;1B), highlighting pathways that may be involved in the therapeutic effects of Res on AD, such as the PI3K-Akt signaling pathway, MAPK signaling pathway, and TNF signaling pathway.To further identify core targets, three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were employed. By conducting intersection analysis of the results from these three algorithms, one candidate core gene, BIRC3, was ultimately identified as the most promising (Fig.\u0026nbsp;1C). Molecular docking results indicated that the binding energy between Res and BIRC3 was \u0026minus;\u0026thinsp;5.19 kcal/mol, suggesting a strong binding affinity between the two (Fig.\u0026nbsp;1D). Additionally, BIRC3 was found to be significantly upregulated in AD patients (Fig.\u0026nbsp;1E) and exhibited good diagnostic value, with an area under the curve (AUC) of 0.860 (Fig.\u0026nbsp;1F).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of Resveratrol in the Treatment of Atherosclerosis.\u003c/b\u003e AS is a chronic inflammatory disease affecting the arterial walls, and its underlying mechanisms are not yet fully understood. It is currently believed to be primarily associated with endothelial cell damage, lipid accumulation, foam cell formation, inflammatory responses, and oxidative stress\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Recent studies suggest that Res may exert beneficial effects on cardiovascular health by mitigating excessive inflammatory responses. The STRING platform integrated a total of 57 intersection targets of Res and AS to establish a protein-protein interaction (PPI) network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Enrichment analysis results indicated that potential pathways associated with Res treatment for AS include lipid atherosclerosis, the AGE-RAGE signaling pathway, the TNF signaling pathway, and the IL-17 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Three machine learning algorithms identified three promising candidate core genes: TNF, DPP4, and NOX4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Molecular docking results revealed that the binding energies between Res and the target proteins (TNF, DPP4, and NOX4) were \u0026minus;\u0026thinsp;7.32 kcal/mol, -5.51 kcal/mol, and \u0026minus;\u0026thinsp;5.59 kcal/mol, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Furthermore, TNF expression was found to be upregulated in AS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and exhibited excellent diagnostic value, with an area under the curve (AUC) of 0.959 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of Resveratrol in the Treatment of Chronic Obstructive Pulmonary Disease.\u003c/b\u003e COPD is an inflammatory lung disease characterized by complex pathological features and unclear etiology \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Res protects the lungs by activating SIRT1 and reducing oxidative stress through Nrf2-mediated antioxidant enzymes, showing potential as an alternative to corticosteroids in COPD and cancer treatment \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates the protein-protein interaction (PPI) network constructed from 26 intersection targets of Res and COPD. Enrichment analysis results suggest that Res may influence the progression of COPD through the AGE-RAGE signaling pathway and the HIF-1 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Utilizing three machine learning algorithms, four core genes were ultimately identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Molecular docking revealed that the binding energies between Res and the target proteins (EDN1, BCL2A1, MPO, and CA3) were \u0026minus;\u0026thinsp;4.30 kcal/mol, -5.03 kcal/mol, -4.95 kcal/mol, and \u0026minus;\u0026thinsp;6.54 kcal/mol, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Notably, CA3 was found to be highly expressed in COPD and demonstrated good diagnostic value, with an area under the curve (AUC) of 0.789 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of Resveratrol in the Treatment of Hepatitis B.\u003c/b\u003e Res has demonstrated potential efficacy in the treatment of hepatitis B (HB) through various mechanisms, including the inhibition of HB virus replication, reduction of liver damage, and enhancement of immune function\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA presents the protein-protein interaction (PPI) network constructed from 25 intersection targets of Res and HB. Enrichment analysis results indicate that the pathways potentially involved in Res's treatment of HB include the HBV signaling pathway and the estrogen receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).Using three machine learning algorithms, two core targets were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Molecular docking results revealed that the binding energies between Res and the target proteins (TUBB3 and PGR) were \u0026minus;\u0026thinsp;4.42 kcal/mol and \u0026minus;\u0026thinsp;4.68 kcal/mol, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Additionally, PGR expression was significantly elevated in HB patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), with an area under the curve (AUC) of 0.680 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of Resveratrol in Multiple Sclerosis.\u003c/b\u003e Multiple sclerosis (MS) is an immune-mediated inflammatory and demyelinating disease of the central nervous system (CNS). The protein-protein interaction (PPI) network constructed from the 12 intersection targets of Res and MS is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. Enrichment analysis results suggest that Res may exert anti-MS effects through pathways such as the TNF signaling pathway, NF-κB pathway, and IL-17 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Three machine learning algorithms identified the core targets CXCL8 and YWHAG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Molecular docking results indicated that the binding energies between Res and the target proteins (CXCL8 and YWHAG) were \u0026minus;\u0026thinsp;6.33 kcal/mol and \u0026minus;\u0026thinsp;3.36 kcal/mol, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Notably, CXCL8 was significantly upregulated in MS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) and demonstrated good diagnostic value, with an area under the curve (AUC) of 0.877 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of Resveratrol in the Treatment of Rheumatoid Arthritis.\u003c/b\u003e Studies have indicated that Res can alleviate joint inflammation and protect articular cartilage in rheumatoid arthritis (RA) through multiple pathways, positioning it as a promising novel phytotherapy for RA \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA presents the network diagram of 84 core targets associated with Res and RA. Enrichment analysis results suggest that Res may exert anti-RA effects via the PI3K-Akt and FoxO signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).Three machine learning algorithms identified the core targets TNFSF10 and EGFR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Molecular docking results revealed that the binding energies between Res and the target proteins (TNFSF10 and EGFR) were \u0026minus;\u0026thinsp;6.39 kcal/mol and \u0026minus;\u0026thinsp;4.96 kcal/mol, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Notably, TNFSF10 expression was found to be upregulated in RA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) and demonstrated excellent diagnostic value, with an area under the curve (AUC) of 0.935 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of Resveratrol in the Treatment of Systemic Lupus Erythematosus.\u003c/b\u003e SLE is a diffuse connective tissue disease characterized by prominent immune-mediated inflammation, which can lead to damage in vital organs such as the kidneys and hematologic system. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA illustrates the network diagram of 49 intersection targets associated with Res and SLE. Enrichment analysis results indicate that the pathways potentially involved in Res's treatment of SLE include the IL-17 signaling pathway and the MAPK signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).Machine learning algorithms identified the core targets HERC5 and NFKBIA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Molecular docking results demonstrated that the binding energies between Res and the target proteins (HERC5 and NFKBIA) were \u0026minus;\u0026thinsp;4.70 kcal/mol and \u0026minus;\u0026thinsp;5.35 kcal/mol, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Notably, NFKBIA was found to be highly expressed in SLE (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eE) and exhibited good diagnostic value, with an area under the curve (AUC) of 0.824 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized network pharmacology, machine learning, and molecular docking techniques to explore the potential molecular pathways and key targets regulating the effects of Res on chronic inflammatory diseases, elucidating the significance of seven key genes: BIRC3, CA3, PGR, CXCL8, TNF, TNFSF10, and NFKBIA in chronic inflammatory conditions.\u003c/p\u003e\u003cp\u003eBIRC3 (Baculoviral IAP repeat-containing protein 3), also known as cIAP2 (cellular inhibitor of apoptosis protein 2), is a protein involved in the regulation of apoptosis (programmed cell death) and inflammatory responses. BIRC3 is currently found to be highly expressed in various cancers, including gallbladder cancer, gastric cancer, renal cell carcinoma, and hepatocellular carcinoma \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. BIRC3 is part of the NF-κB signaling pathway, where it stabilizes and activates the TAB2/TAB3 proteins within the TAK1 complex, thereby promoting the phosphorylation of the IKK complex, leading to the degradation of IκBα. This process releases NF-κB transcription factors into the nucleus, inducing the expression of genes encoding pro-inflammatory mediators and facilitating inflammation and disease progression \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Additionally, BIRC3 functions as an E3 ubiquitin ligase, modulating the immune response in rheumatoid arthritis (RA) by regulating TNF receptor signaling and the production of inflammatory cytokines. It interacts with apoptotic signaling pathways to prevent cell death, thereby participating in the proliferation and survival of fibroblast-like synoviocytes (FLS) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCA3 (Carbonic Anhydrase III) is an isoenzyme that is highly expressed in skeletal muscle, particularly abundant in type I fibers and present at very low levels in type II fibers \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. CA3 exhibits hydration activity that regulates intracellular pH and possesses antioxidant properties, functioning as an immunomodulator by inhibiting the secretion of inflammatory cytokines. Research has shown that as ulcerative colitis worsens, the levels of CA3 decrease in both colonic tissue and serum \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, indicating its anti-inflammatory effects. In patients with skeletal muscle atrophy due to COPD, CA3 levels are significantly reduced compared to normal controls; however, data from this study indicate that CA3 expression is upregulated in COPD. The reduction of type I fibers in the peripheral skeletal muscle of COPD patients leads to decreased CA3 levels, while the upregulation of CA3 expression may be attributed to the higher generation of reactive oxygen species (ROS) in the remaining type I fibers compared to normal levels.\u003c/p\u003e\u003cp\u003ePGR (Progesterone Receptor) is a nuclear receptor transcription factor primarily expressed in the granulosa cells of ovulatory follicles, playing a crucial role in the normal functioning of the reproductive system \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Additionally, researchers have observed that the risk of developing RA in pregnant women is reduced by 2 to 5 times compared to non-pregnant women, which may be associated with elevated levels of estrogen, progesterone, and adrenal corticosteroids during pregnancy \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Pregnancy-level progesterone can induce T cells to produce IL-4 and IL-10, promoting the proliferation and differentiation of T helper (Th) cells. During pregnancy, lymphocytes express progesterone receptors and release progesterone-induced blocking factor (PIBF), which exhibits strong anti-natural killer (NK) cell activity and also secretes IL-10, contributing to anti-RA effects \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Progesterone facilitates the differentiation of T cells from human fetal umbilical cord blood into regulatory T (Treg) cells while inhibiting their differentiation into Th17 cells \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, thereby reducing the production of pro-inflammatory factors. In multiple sclerosis (MS), Tregs express high levels of estrogen and progesterone receptors, and each hormone enhances the immunosuppressive function of Tregs in vitro \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding how progesterone and PGR precisely regulate the balance between Treg and Th17 cells is particularly important.\u003c/p\u003e\u003cp\u003eCXCL8, also known as IL-8, is one of the key chemokines responsible for the recruitment of neutrophils, produced and released by various cell types in response to a range of stimuli, including microbial products, tissue damage, and hypoxia \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. CXCL8 exerts multiple biological functions by binding to its two receptors, CXCR1 and CXCR2, which include promoting inflammatory responses, angiogenesis, mitosis, and cellular proliferation \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. CXCL8 and its receptors are involved in the pathogenesis of various diseases, including rheumatoid arthritis, inflammatory bowel disease, chronic obstructive pulmonary disease, asthma, and cancer \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The neutrophil extracellular traps induced by CXCL8 exacerbate atherosclerosis by activating NF-κB signaling in macrophages \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Elevated levels of CXCL8, neutrophils, and neutrophil-derived enzymes have been detected in the blood of patients with chronic inflammatory demyelinating diseases such as MS \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Recent studies in 2024 have shown that serum levels of CXCL8 are reduced in patients with relapsing-remitting MS, and the concentration of CXCL8 is negatively correlated with anti-EBNA IgG levels. This observation may be explained by a compensatory anti-inflammatory regulatory mechanism in MS, warranting further investigation to elucidate the underlying mechanisms \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTNF (Tumor Necrosis Factor) and TNFSF10 (TNF-related apoptosis-inducing ligand, also known as TRAIL) are both cytokines belonging to the tumor necrosis factor superfamily. During acute inflammation, TNF rapidly activates the NF-κB pathway, promoting the inflammatory response. Conversely, NFKBIA (IκBα, NF-κB inhibitor α) acts as a negative feedback regulator, helping to control the excessive activation of NF-κB and maintain appropriate levels of inflammation \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The role of TNFSF10 is relatively complex; it can reduce inflammatory stimuli by inducing apoptosis under specific conditions and influence the inflammatory process by modulating immune cell functions. In this study, NFKBIA was found to be highly expressed in SLE. However, it was observed that the expression of IκB-α mRNA in the spleen of lupus-susceptible mice was lower compared to wild-type mice \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. We speculate that post-translational modifications or other mechanisms may be affecting the functional stability of NFKBIA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePrediction of Targets for Resveratrol in Inflammatory Diseases.\u003c/b\u003e Using \"resveratrol\" as the search keyword, we obtained resveratrol-related targets from several pharmacological databases and analysis platforms, including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://old.tcmsp-e.com/index.php\u003c/span\u003e\u003cspan address=\"https://old.tcmsp-e.com/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Pharm Mapper, SEA, and SwissTargetPrediction. After removing non-human targets and integrating the identified targets from these four pharmacological databases, duplicate targets were eliminated. Subsequently, we accessed the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/GEO/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/GEO/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and input the keywords \"Alzheimer's disease,\" \"atherosclerosis,\" \"chronic obstructive pulmonary disease,\" \"Hepatitis B,\" \"Multiple sclerosis,\" \"rheumatoid arthritis,\" and \"systemic lupus erythematosus\" to download relevant datasets. The downloaded datasets were then processed for normalization using the \"limma\" package in R version 4.2.1. We set the filtering criteria at P \u0026lt; 0.05 and |log2FC| \u0026gt;0.5 to identify differentially expressed genes (DEGs) associated with these inflammatory diseases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntersection Targets and Construction of Protein-Protein Interaction (PPI) Network.\u003c/b\u003e By intersecting the targets of Res with those associated with inflammatory diseases, we identified potential targets through which Res exerts its anti-inflammatory effects. The STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to construct a protein-protein interaction (PPI) network for these potential targets, with a confi-dence threshold set at 0.4. The resulting PPI network was visualized graphically. Additionally, the expression levels of the intersected targets in the GEO datasets were illustrated using a heatmap generated with the \"ComplexHeatmap\" package in R version 4.2.1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGO and KEGG Enrichment Analysis.\u003c/b\u003e Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using R version 4.2.1 along with relevant R packages (clusterProfiler and org.Hs.eg.db)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. A significance threshold of P \u0026lt; 0.05 was established, and the results were visualized using bar plots and bubble plots to illustrate the enriched bio-logical processes and pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMachine Learning for Core Gene Selection.\u003c/b\u003e The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) methods were employed to classify key targets. To differentiate between the control and disease groups, 5-fold cross-validation was performed using the \"glmnet\" package. The SVM-REF algorithm was utilized to generate a hyperplane with the maximum margin in the feature space to distinguish positive and negative instances. The \"e1071\" and \"svmRadial\" packages in R were used to conduct SVM-RFE analysis for selecting high-quality genes. Recursive partitioning was applied to construct binary trees in the Random Forest (RF) model. The \"RandomForest\" package was utilized to build the RF classification model, ranking the key genes based on the Gini index to identify feature expression targets.\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of Core Gene Expression and ROC Curve Analysis.\u003c/b\u003e The Wilcoxon test was employed to compare differences between the control and disease groups, with P \u0026lt; 0.05 indi-cating statistical significance. The \"pROC\" package in R was used to generate Receiver Operating Characteristic (ROC) curves and calculate the area under the curve (AUC) to assess diagnostic value.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular Docking.\u003c/b\u003e The molecular structure of Res was obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The protein structures of the core genes associated with the inflammatory diseases were retrieved from the RCSB PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Molecular docking of the receptor and ligand was performed using AutoDockTools software, and the results were visualized using PyMOL software.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study systematically identified potential therapeutic targets of Res in AD, AS, COPD, HB, MS, RA, and SLE, and validated their binding capabilities through molecular docking. These findings offer new insights and targets for the treatment of inflammatory diseases. Future research could further validate the efficacy of these targets and explore the synergistic effects of Res in combination with other therapies, aiming to provide more options for clinical treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank\u0026nbsp;Sichuan Taikang Hospital\u0026nbsp;for assistance during the present study.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003eAuthor\u0026nbsp;contributions\u0026nbsp;statement\u003c/p\u003e\n\u003cp\u003eConceptualization, H.-D., J.-L.C.\u0026nbsp;and\u0026nbsp;K.-H.L.; Investigation, H.-D., X.-L.Y and K.-L; Methodology, H.-D., X.-L.Y.\u0026nbsp;and H.-Y.W; Supervision, H.-D., J.-L.C.\u0026nbsp;and\u0026nbsp;K.-H.L; Visualization, H.-D. and J.J.; Writing\u0026mdash;Original draft, H.-D.; Writing\u0026mdash;Review and editing, H.-D., J.-L.C.\u0026nbsp;and\u0026nbsp;K.-H.L. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAggarwal, B. B., Vijayalekshmi, R. V. \u0026amp; Sung, B. Targeting inflammatory pathways for prevention and therapy of cancer: short-term friend, long-term foe. \u003cem\u003eClin Cancer Res\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 425-430, doi:10.1158/1078-0432.CCR-08-0149 (2009).\u003c/li\u003e\n\u003cli\u003eLiu, T., Zhang, L., Joo, D. \u0026amp; Sun, S.-C. 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KEGG: kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 27-30 (2000).\u003c/li\u003e\n\u003cli\u003eYu, G., Wang, L.-G., Han, Y. \u0026amp; He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. \u003cem\u003eOMICS\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 284-287, doi:10.1089/omi.2011.0118 (2012).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7153466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7153466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo explore the protective effect and mechanism of resveratrol in the treatment of chronic inflammatory diseases through network pharmacology, machine learning and molecular docking techniques.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCMSP, Pharm Mapper, SEA and SwissTargetPrediction and GEO databases were used to identify potential targets associated with resveratrol and chronic inflammatory diseases. These include Alzheimer's disease (AD), atherosclerosis (AS), chronic obstructive pulmonary disease (COPD), hepatitis B (HB), multiple sclerosis (MS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). The protein interaction network was constructed using STRING platform, and the KEGG pathway enrichment analysis was performed using R software. Machine learning was used to screen core genes and make molecular docking with resveratrol. GEO database was used to verify the expression of core genes and ROC curve analysis was performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResveratrol had strong binding force with the core targets (BIRC3, CA3, PGR, CXCL8, TNF, TNFSF10 and NFKBIA). These targets were significantly up-regulated in the gene expression data of the corresponding GEO database and showed good diagnostic value (the area under ROC curve ranged from 0.680 to 0.959).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese results provide a new molecular target and theoretical basis for the application of resveratrol in the treatment of inflammatory diseases.\u003c/p\u003e","manuscriptTitle":"Network pharmacological analysis and molecular mechanism of resveratrol inhibiting inflammation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 09:40:03","doi":"10.21203/rs.3.rs-7153466/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":"ecc49a6a-288a-4ebf-a623-9610f239433f","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52638641,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":52638642,"name":"Health sciences/Diseases"},{"id":52638643,"name":"Biological sciences/Drug discovery"},{"id":52638644,"name":"Biological sciences/Immunology"},{"id":52638645,"name":"Health sciences/Rheumatology"}],"tags":[],"updatedAt":"2025-08-05T09:40:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 09:40:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7153466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7153466","identity":"rs-7153466","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}