Network pharmacological analysis and experimental study of melatonin in chronic prostatitis/chronic pelvic pain syndrome

Network pharmacological analysis and experimental study of melatonin in chronic prostatitis/chronic pelvic pain syndrome | 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 Network pharmacological analysis and experimental study of melatonin in chronic prostatitis/chronic pelvic pain syndrome Yanan Wang, Yonfeng Lao, Rongxin Li, Chengyu You, Liangliang Qing, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3794889/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective: This study aims to explore the potential mechanisms of melatonin (MT) in treating chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) using network pharmacology and molecular docking. Methods: The target genes of MT were acquired from the Swiss Target Prediction, Superpred, SEA, and PharmMapper databases and the CP/CPPS targets were collected based on OMIM, Disgenet, and Genecards databases. The intersection of MT and CP/CPPS target genes was analyzed. A PPI network was constructed using Cytoscape to identify core targets. The shared targets underwent GO and KEGG enrichment analyses by Using R software. Molecular docking of MT with core targets was performed using AutoDock and PyMol. And using cell experiments to verify the potential effect of MT in CP/CPPS. Results: Network pharmacology analysis reveals 284 shared targets between MT and CP/CPPS, with AKT1, SRC, HSP90AA1, PTGS2, BCL2L1, ALB, CASP3, NFKB1, HIF1A, and ESR1 identified as key targets. Enrichment analysis indicates that MT affects CP/CPPS through various biological processes, and pathway analysis emphasizes the significance of PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and apoptosis pathways. Molecular docking confirms strong binding between MT and core targets. Cell experiments demonstrate that MT can inhibit the secretion of IL-1β, IL-6, and TNF-α in LPS induced RWPE-1 cells, alleviate inflammation, and suppress cell apoptosis and oxidative stress. Conclusion: Network pharmacology, molecular docking and cell experiments showed that MT could play a role in CP/CPPS by regulating multiple targets and pathways. This provides valuable insights for a more in-depth investigation into the molecular mechanisms and clinical applications of MT in CP/CPPS treatment. Melatonin prostatitis Network pharmacology Molecular docking RWPE-1 LPS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Prostatitis, a prevalent and distinctive genitourinary syndrome, afflicts adult males across all age groups, with an incidence ranging from 8.4–25% [ 1 , 2 ].Despite its ubiquity, the etiological underpinnings of prostatitis remain elusive. Clinically, it manifests through a spectrum of symptoms, including pelvic pain, irritative urinary tract symptoms, and prostatic inflammation, carrying potential ramifications for male fertility and sexual function[ 3 , 4 ]. Characterized by a high incidence and a propensity for recurrence, prostatitis significantly compromises the affected individuals' quality of life (QoL)[ 5 ]. The National Institutes of Health (NIH) in the United States classifies prostatitis into four types: Type I, Acute Bacterial Prostatitis (ABP); Type II, Chronic Bacterial Prostatitis (CBP); Type III, Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) and Type IV, Asymptomatic Inflammatory Prostatitis (AIP). Among these, CP/CPPS stands out as the most prevalent, constituting approximately 90% of all prostatitis cases[ 6 , 7 ]. The pathogenesis of CP/CPPS remains elusive and is characterized by multifactorial etiology. Current hypotheses regarding the pathogenic mechanisms of CP/CPPS include defects in the integrity and function of the urogenital system, autoimmunity, latent infections, endocrine imbalances, urinary dysfunction, peripheral and central sensitization, neuroplasticity, pelvic floor muscle spasms, and psychosocial factors[ 8 , 9 ]. Oxidative stress, immune dysregulation, and alterations in the local microenvironment are considered pivotal pathogenic factors in CP/CPPS [ 10 ]. Existing therapeutic approaches encompass antibiotics, α-adrenergic blockers, anti-inflammatory agents, herbal remedies, and hormone therapy [ 11 ], However, due to the lack of precise targeting of the underlying causes, satisfactory therapeutic outcomes are often challenging to achieve. Consequently, there is an urgent need to explore novel and effective treatments specifically targeting the pathogenic mechanisms, representing a focal area of research in the clinical management of CP/CPPS. N-acetyl-5-methoxytryptamine (Melatonin, MT), a neuroendocrine hormone synthesized by the pineal gland and various other cell types, including macrophages, monocytes, and lymphocytes, exerts various physiological functions through G protein-coupled MT1 and MT2 receptors, both in a dependent and independent manner. These functions encompass antioxidative, anti-inflammatory, antitumor, and immune-regulatory effects[ 12 ]. Studies have demonstrated that MT can alleviate epithelial cell and smooth muscle cell atrophy and inflammatory infiltration in the prostate tissues of diabetic rats[ 13 ],inhibit the NLRP3 inflammasome signaling pathway, thereby alleviating prostatitis and pelvic pain [ 14 ]and ameliorate symptoms such as nocturia and urinary frequency in patients with benign prostatic hyperplasia[ 15 ]. However, there remains a relative scarcity of foundational and clinical research data on MT's role in the treatment of CP/CPPS, and its underlying mechanisms are yet to be elucidated, warranting further investigation. Network pharmacology is grounded in the construction of a comprehensive network involving "disease-gene-target-drug" interactions. Utilizing various databases and software tools, this approach establishes multilayered networks to explore the correlation between drug components and disease targets, elucidating the mechanisms of drug action. Molecular docking techniques start from known structures of receptors and ligands. By calculating relevant parameters, these techniques predict the binding affinity and binding modes of receptors and ligands, facilitating the identification of potential drug targets. This study integrates network pharmacology and molecular docking techniques, complemented by cellular experiments, to explore the potential mechanistic insights into the therapeutic efficacy of MT in treating CP/CPPS. The objective is to furnish a scientific foundation for clinical applications and to advance the development of novel therapeutic agents. The technical strategy workflow is delineated in Fig. 1 . Materials and Methods 1 Network pharmacology analysis 1.1 MT Potential Target Prediction MT-related information, including its name and structure, was systematically retrieved from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) (Fig. 2 A). Subsequently, predictive analyses of MT's binding targets were conducted using the SwissTargetPrediction database ( http://swisstargetprediction.ch/ ), Super-PRED database ( https://prediction.charite.de/ ), SEA database ( http://sea.bkslab.org ), and PharmMapper database ( http://www.lilab-ecust.cn/pharmmapper/ ). Through the integration of target genes obtained from these databases, duplicate entries were removed to ascertain the potential targets of MT. 1.2 Disease Potential Target Prediction Disease-related targets were gathered through searches on Genecards ( http://Genecards.Org ), Disgenet ( https://www.disgenet.org/ ), and OMIM ( https://www.omim.org/ ) databases using the keyword "chronic prostatitis with chronic pelvic pain syndrome." In Genecards, a relevance score threshold of ≥ 10 was set to filter for disease-associated targets. The obtained disease-related targets were compiled and duplicates were subsequently removed. 1.3Common Targets of MT and CP/CPPS Utilizing the online plotting tool platform provided by Xiantao Academic ( https://www.xiantaozi.com/ ), the targets of MT and CP/CPPS were inputted to generate a Venn diagram. The intersection of the two sets revealed the common targets shared between MT and CP/CPPS. 1.4Construction of Protein-Protein Interaction (PPI) Network and Core Target Selection The common targets were queried in the STRING database ( https://cn.string-db.org/ ), specifying the protein species as "Homo sapiens," and setting the minimum interaction threshold to 0.400. Subsequently, a Protein-Protein Interaction (PPI) network diagram was constructed. After removing unrelated nodes, the network was imported into Cytoscape (version 3.10.1) software for topological analysis using the Network Analyzer tool, with nodes sorted based on their degree values. The top 10 core targets of this network were identified using the Maximum Clique Centrality (MCC) algorithm from the CytoHubba plugin. Additionally, the core network of this network was obtained using the Molecular Complex Detection (MCOD) plugin. 1.5 GO and KEGG Enrichment Analysis Utilizing R software (version 4.3.1) and the cluster Profile bioinformatics package, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on the target genes, applying significance criteria of P-value < 0.05 and Q-value < 0.05. 1.6 Molecular Docking Molecular docking validations of drugs and core targets were conducted using Autodock-Vina (Version 1.1.2) software. The three-dimensional structures of target proteins were obtained from the RCSB PDB protein structure database ( https://www.rcsb.org ). After processing the receptor and ligand using Autodocktools (Version 1.5.6) for semi-flexible docking, the docking models with the lowest binding energy were selected for visualization. PyMOL (Version 2.3.0) was employed for the study of their interactions and further visual analysis. 2 Cell experiments 2.1Drugs and Reagents MT was purchased from GlpBio (Shanghai, China, Catalog Number: 73-31-4, purity > 98.5%). Fetal bovine serum was obtained from ExCell Bio (Jiangsu, China, Catalog Number: FSP500). High-glucose DMEM was purchased from Gibco (USA, Lot Number: 6123011). DMSO was acquired from BioSharp (Anhui, China, Catalog Number: BL165B). LPS (lipopolysaccharide) was obtained from BioSharp (Anhui, China, Catalog Number: BS904). CCK-8 (Cell Counting Kit-8) was purchased from BioSharp (Anhui, China, Catalog Number: BS350B). The Reactive Oxygen Species (ROS) detection kit was procured from Jiancheng Bio (Nanjing, China, Catalog Number: E004-1-1), and the apoptosis detection kit was obtained from Union-Biotech (Shanghai, China, Catalog Number: AT107). The MT solution was prepared according to the manufacturer's instructions, with the stock solution (500 mg of MT) prepared in Dimethyl sulfoxide (DMSO). Subsequent dilutions were made using the base culture medium according to the experimental protocol. The final solvent concentration was ≤ 0.1% (v/v), having no impact on the experimental procedures[ 16 ]. 2.2 Cell Culture The RWPE-1 cell was obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China), catalog number: SCSP-5025. The cells were cultured in high-glucose DMEM supplemented with 10% fetal bovine serum. The cells were maintained at 37°C in a humidified incubator with 5% CO2. Passages were performed when the cells reached approximately 90% confluency. 2.3 Cell Viability Assay The cell toxicity analysis comprised two parts as per experimental requirements. In the first part, cells were treated with different concentrations of MT (0, 100, 200, 300, 400, 500µM) for 24 hours. In the second part, cells were pre-treated with MT (100, 200, or 500µM) for 4 hours, followed by stimulation with LPS at a concentration of 10µg/ml for an additional 24 hours. Subsequently, after the specified treatments, cells were incubated with 10µL of CCK-8 reagent per well at 37°C for 2 hours. The optical density (OD) values at 450 nm were measured using a microplate reader. Cell viability was calculated using the formula: Cell Viability= (As - Ab/Ac – Ab) ×100%. As: Absorbance of experimental wells (culture medium, cells, drug, CCK8); Ac: Absorbance of control wells (culture medium, cells, CCK8); Ab: Absorbance of blank wells (culture medium, CCK8). 2.4 Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) RWPE-1 cells were randomly divided into three groups: (a) RWPE-1 group (normal control); (b) LPS group (LPS-induced inflammation, treated with LPS 10µg/ml for 24 hours[ 17 ]);(c) MT + LPS group (pre-treated with MT 500µM for 4 hours, followed by LPS treatment at 10µg/ml for 24 hours). In brief, total RNA was extracted from RWPE-1 cells using SteadyPure Fast RNA Extraction Kit (Hunan, China, Catalog Number: AG21023) and reverse transcribed using Evo M-MLV Reverse Transcription PreMix Kit (Hunan, China, Catalog Number: AG11728). Subsequently, real-time quantitative PCR (RT-qPCR) was performed on a Bio-RAD real-time PCR system using SYBR Green Pro Taq HS PreMix Kit (Hunan, China, Catalog Number: AG11701) according to the manufacturer's instructions. The primer sequences are provided in Table 1 . The relative mRNA expression levels of the target genes were calculated using the 2 −ΔΔCt method, with GAPDH as the internal control for normalization. Table 1 Primer sequence Primer Sequences(5’to3’) IL-1β IL-6 TNF-α GAPDH F: TGATGGCTTATTACAGTGGCA R: TCGTGCACATAAGCCTCGTT F: AGAGGCACTGGCAGAAAACA R: TCACCAGGCAAGTCTCCTCA F: GAGGCCAAGCCCTGGTATG R: CGGGCCGATTGATCTCAGC F: ATCGTGGAAGGACTCATGACCA R: ATCGTGGAAGGACTCATGACCA 2.5 Measurement of Intracellular ROS The intracellular ROS levels, considered a crucial indicator of cellular oxidative stress, were detected using 2',7'-dichlorofluorescin diacetate (DCFH-DA). RWPE-1 cells (1×10 4 cells/well) were pre-incubated with MT (500µM) for 4 hours, followed by stimulation with or without LPS at a concentration of 10µg/ml for an additional 24 hours. DCFH-DA (10µmol/L) was added, and cells were treated in the dark at 37°C for 30 minutes. Subsequently, cells were washed twice with PBS. The intracellular ROS was observed using a fluorescence microscope, and the fluorescence intensity of DCF indicated the ROS levels. 2.6 Cell Apoptosis Logarithmically growing RWPE-1 cells were digested, suspended, and then seeded into a 6-well plate. The cell density was adjusted to approximately 5×10 5 cells/well. After the intervention, cell samples were prepared following the instructions of the Annexin V-APC/PI Apoptosis Kit. Flow cytometry was employed using a Beckman flow cytometer to detect the samples. The data were processed using FlowJo software (V10.8.1, Stanford University Laboratory, Stanford, CA, USA). 2.7Statistical Analysis All data are presented as mean SD or SEM. Statistical analysis was conducted using one-way analysis of variance (ANOVA) with GraphPad Prism software (version 9.5.1). Statistically significant differences are denoted as *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. Results 1 MT and CP/CPPS-Related Target Genes In this study, the prediction of MT targets was performed using the SwissTargetPrediction, Super-PRE, SEA, and PharmMapper databases, resulting in a total of 504 target genes (Fig. 2 B). Prediction of CP/CPPS targets was conducted using Genecards, Disgenet, and OMIM databases, leading to 3954 related target genes after eliminating duplicates (Fig. 2 C). Utilizing the Venn diagram tool on Xiantao Academic, the intersecting targets between MT and CP/CPPS were identified, totaling 284 genes (Fig. 2 D). 2 Construction of PPI Network and Analysis of Core Targets The 284 common target genes were imported into the STRING database to generate a PPI network (Fig. 2 E). This network comprised 284 nodes and 4946 edges, with one independent node removed for visualization in Cytoscape software. Using the CytoHubba plugin and the MCC algorithm, the top 10 hub genes based on degree value were identified (Fig. 3 A): CASP3, HIF1A, HSP90AA1, AKT1, NFKB1, BCL2L1, SRC, ESR1, ALB, and PTGS2. Further analysis involved module division and clustering analysis using MCODE on the PPI network. A total of 9 modules were obtained, with 3 modules having scores>5. Module one (score = 35.846) included 40 nodes and 1398 edges (Fig. 3 B); module two (score = 8.824) comprised 35 nodes and 300 edges (Fig. 3 C); module three (score = 5.931) consisted of 30 nodes and 172 edges (Fig. 3 D). 3 GO and KEGG Enrichment Analysis 3.1 Enrichment Analysis of Common Targets in GO and KEGG Using R software, we conducted GO functional analysis and KEGG pathway analysis for the 284 common targets of MT and CP/CPPS. The results of GO functional analysis (Fig. 4 A) showed that the 284 common targets were enriched in a total of 3234 GO terms (P < 0.05), including 2887 biological process terms (BP), 100 cellular component terms (CC), and 247 molecular function terms (MF). The top 10 enriched terms were selected for visualization. GO analysis indicated that biological processes such as positive regulation of the MAPK cascade, wound healing, cellular response to chemical stress, response to lipopolysaccharide, and response to reactive oxygen species play crucial roles in MT's treatment of CP/CPPS. Cellular components primarily involved membrane rafts, vesicles, and membrane microdomains. Molecular functions included protein serine/threonine kinase activity, endopeptidase activity, protein tyrosine kinase activity, transmembrane receptor protein kinase activity, transcription coactivator binding, nuclear receptor activity, and ligand-activated transcription factor activity. The results of KEGG enrichment analysis (Fig. 4 B) showed enrichment in 177 pathways, including cancer-related pathways, PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and Apoptosis signaling pathways. The top 30 enriched pathways were selected for visualization. 3.2 Enrichment Analysis of Key Module Targets After Filtering Using R software, we conducted GO and KEGG analysis for the module with the highest clustering analysis score obtained from the MCODE plugin (Fig. 4 C and Fig. 4 D). The results suggest that MT may improve CP/CPPS by modulating signaling pathways such as FoxO, PI3K-Akt, HIF-1, AMPK, Apoptosis, TGF-β, and TNF. 4 Molecular docking Molecular docking was performed between MT and the 10 core targets (CASP3, HIF1A, HSP90AA1, AKT1, NFKB1, BCL2L1, SRC, ESR1, ALB, PTGS2) to calculate the binding energy. Lower negative binding energies indicate more stable binding, and a binding energy below − 1.2 kcal/mol suggests good binding affinity[ 18 ]. The docking results indicated that MT exhibited high binding affinity with the target proteins (Table 2 ). The docking results were visualized for further analysis (Fig. 5 ). Table 2 Binding energy of melatonin and core target Targets PDB ID Binding energy (kcal/mol) AKT1 SRC HSP90AA1 PTGS2 BCL2L1 ALB CASP3 NFKB1 HIF1A ESR1 5KCV 2H8H 7KRJ 5F19 3SP7 4BKE 1CP3 1SVC 4H6J 6PIT -7.6 -7.3 -7.2 -6.9 -6.9 -6.6 -6.4 -5.8 -5.8 -5.5 5 Experimental Validation Results 5.1 MT Inhibits LPS-Induced Cytotoxicity In this study, the impact of MT on cell viability was assessed using the CCK-8 assay. The results revealed that MT at different concentrations (0-500µM) did not exhibit toxicity to RWPE-1 cells (Fig. 6 A). Subsequently, the study investigated the influence of MT on LPS-induced cytotoxicity in RWPE-1 cells, and the results demonstrated a significant inhibition of LPS-induced cytotoxicity by MT (Fig. 6 B). 5.2 MT Suppresses LPS-Induced Inflammation in RWPE-1 Cells To investigate the anti-inflammatory effect of MT on RWPE-1 cells, we utilized the RT-qPCR method to measure the mRNA levels of IL-1β, IL-6, and TNF-α after LPS treatment. In comparison to the control group, the LPS-treated group showed an upregulation in the expression of IL-1β, IL-6, and TNF-α. However, MT attenuated the levels of inflammatory factors such as IL-1β, IL-6, and TNF-α (Fig. 7 ). 5.3 MT Suppresses LPS-Induced Intracellular ROS Generation The ability of LPS to induce oxidative stress was determined by measuring the levels of ROS in RWPE-1 cells. Fluorescence microscopy images (Fig. 8 A) and quantitative data (Fig. 8 B) demonstrated that the production of intracellular ROS was lower in the MT + LPS group compared to the LPS group. The study results indicate that MT effectively inhibits the excessive production of ROS induced by LPS in RWPE-1 cells, highlighting its potent antioxidant properties. 5.4 MT Suppresses LPS-Induced Apoptosis in RWPE-1 Cells Cell apoptosis was assessed using flow cytometry, and the results (Fig. 9 ) demonstrated a significant reduction in the number of apoptotic cells with MT treatment compared to the LPS group. Discussion CP/CPPS is a complex and multifaceted condition with diverse symptoms, believed to result from various etiological factors such as oxidative stress, immune system dysregulation, and alterations in the local microenvironment[ 10 ]. In CP/CPPS patients, the occurrence of inflammation is attributed to the damage of prostatic stromal and epithelial cells. Prolonged exposure of prostate tissue to the inflammatory environment can lead to the substantial release of ROS, causing structural and functional impairments of prostatic proteins and DNA modifications[ 19 ].MT, known for its roles in antioxidation, anti-inflammation, anti-tumor activity, and immune modulation, has the potential to mitigate oxidative damage. MT functions by scavenging free radicals and activating antioxidant enzymes[ 12 ]. Previous research has indicated that MT, by activating Sirt1, inhibits the NLRP3 inflammasome signaling pathway, thereby alleviating inflammation and pelvic pain in experimental autoimmune prostatitis mice. This suggests a therapeutic potential of MT in treating CP/CPPS[ 14 ]. This study employed network pharmacology and molecular docking methods, utilizing relevant databases and software, to preliminarily investigate the potential mechanisms of MT in treating CP/CPPS. The study obtained a total of 284 common targets of MT and CP/CPPS, indicating a good targeting effect of MT in treating CP/CPPS. Through PPI network analysis, the key core targets of MT in treating CP/CPPS were identified as AKT1, SRC, HSP90AA1, PTGS2, BCL2L1, ALB, CASP3, NFKB1, HIF1A and ESR1. AKT1 regulates and controls macrophages, with main pathways involving the promotion of anti-inflammatory cytokine production, phagocytosis, and autophagy to maintain cellular balance[ 20 ]. SRC primarily controls cell proliferation, metabolism, and survival. It plays a crucial role in macrophage-mediated autoimmune responses, inflammation factor production, and induction of cell migration. SRC inhibitors can suppress immune reactions and have anti-inflammatory effects[ 21 ].HSP90AA1 as a stress-induced protein, it can regulate protein kinases and maintain cellular homeostasis[ 22 ]. PTGS2, also known as cyclooxygenase-2 (COX-2), is involved in the conversion of arachidonic acid to prostaglandins and is expressed during the inflammatory process. Studies have confirmed the association between COX-2 inhibitors and the occurrence of CP/CPPS[ 23 ].BCL2L1,an anti-apoptotic BCL2 protein that counteracts pro-apoptotic BH3 proteins (such as BID, BIM, and NOXA) or directly isolates effectors BAK and BAX proteins [ 24 , 25 ].CASP3, a key factor downstream in the process of apoptosis, plays a crucial role. In the cytoplasm, free Bax proteins can directly form Bax-Bax homodimers on the mitochondrial membrane, altering the permeability of the mitochondrial membrane. This alteration activates the caspase cascade, leading to the release of cytochrome C into the cytoplasm, initiating the apoptotic process and triggering a chain reaction of cellular apoptosis [ 26 ].ALB, an inflammation-related receptor that can bind and transport pro-inflammatory factors and cytokines, stimulating immune cell activation and promoting an inflammatory response [ 27 ].Under hypoxic conditions, HIF1A remains in a stable state and can activate the transcription and expression of related target genes, resulting in a cascade of cellular processes such as damage, proliferation, apoptosis, invasion, and metastasis[ 28 ]. NFKB1 is one of the key inducible transcription factors involved in various signaling pathways, including cell apoptosis and inflammatory responses [ 29 ]. ESR1 is a subtype of estrogen receptor belonging to the estrogen receptor family. Estrogen can induce prostate inflammation through the mediation of ESR1 [ 30 ].In summary, the analysis of core targets suggests that MT may improve CP/CPPS by regulating inflammation, cell apoptosis, and oxidative stress. The KEGG pathway enrichment analysis reveals that signaling pathways such as PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and Apoptosis may play a crucial role in the action of MT during the development of CP/CPPS. The Ras signaling pathway is pivotal in various physiological processes, including cell proliferation, migration, and apoptosis. Once Ras protein binds to GTP, it becomes activated, recruits Raf to the cell membrane, and subsequently activates the MAPK pathway through phosphorylation of MAPK[ 31 ].The MAPK signaling pathway can be activated by reactive oxygen species and numerous inflammatory factors, further promoting the production of other inflammatory factors[ 32 ].Inhibitors of MAPK have been shown to effectively alleviate inflammation and neuropathic pain in animal models[ 33 ]. The PI3K/Akt signaling pathway plays a crucial role in cell apoptosis and autophagy. Ras can activate PI3K, and activated PI3K directly acts on downstream factor Akt, thereby activating the NF-κB signaling pathway[ 34 ] and the FOXO signaling pathway[ 35 ], Additionally, FoxO proteins are critical regulatory factors in cellular responses to oxidative stress, providing resistance against oxidative stress [ 36 ]. The HIF-1 signaling pathway is involved in the regulation of genes responding to low oxygen levels and can promote the expression of factors such as heme oxygenase-1 (HO-1) and vascular endothelial growth factor (VEGF). HO-1 has antioxidative, anti-inflammatory, and vasodilatory effects [ 37 ].Oxidative stress can lead to lipid peroxidation damage in prostate tissue, subsequently activating COX-2, resulting in the upregulation of prostaglandins (PG), especially PGE2. PGE2 can inhibit the release of β-endorphin, which has analgesic effects, thereby causing pain. COX-2 is regulated by the MAPK and NF-κB signaling pathways, and inhibition of the MAPK and NF-κB pathways can suppress COX-2 expression, thus alleviating pain[ 38 ].The epidermal growth factor receptor (EGFR) is widely distributed in the body's epithelial tissues and plays a crucial regulatory role in the inflammatory process[ 39 ]. In vitro experiments have demonstrated that EGFR can inhibit LPS-induced iNOS expression and the production of IL-1β, IL-6, and TNF-α through NF-κB inactivation.[ 40 ]。 The results of cell experiments indicate that MT can inhibit the production of inflammatory factors such as IL-1β, IL-6, and TNF-α induced by LPS in RWPE-1 cells. Additionally, it reduces cell apoptosis and the generation of ROS. Conclusion This study utilized network pharmacology and molecular docking to reveal the potential mechanisms of MT in treating CP/CPPS. It is speculated that MT may act on targets such as AKT1, SRC, HSP90AA1, PTGS2, BCL2L1, ALB, CASP3, NFKB1, HIF1A, and ESR1, regulating inflammation, oxidative stress, and cell apoptosis through signaling pathways including PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and Apoptosis. Cell experiments demonstrated that MT can inhibit the levels of inflammatory factors such as IL-1β, IL-6, TNF-α, as well as reduce ROS and cell apoptosis induced by LPS in RWPE-1 cells improving CP/CPPS. Given the limitations of cell experiments, future research may employ animal experiments to further investigate these mechanisms and enhance the accuracy of experimental predictions. Abbreviations MT Melatonin CP/CPPS Chronic prostatitis/chronic pelvic pain syndrome RWPE-1 Human prostate epithelial cells LPS Lipopolysaccharide AKT1 Protein Kinase B SRC Proto-Oncogene Tyrosine-Protein Kinase Src HSP90AA1 Heat Shock Protein 90 Alpha Family Class A Member 1 PTGS2 Prostaglandin-Endoperoxide Synthase 2 BCL2L1 BCL2 Like 1 ALB Albumin CASP3 Caspase 3 NFKB1 Nuclear Factor Kappa B Subunit 1 HIF1A Hypoxia Inducible Factor 1 Subunit Alpha ESR1 Estrogen Receptor 1 PI3K Phosphatidylinositol 3-Kinas MAPK Mitogen-Activated Protein Kinase Ras Rat Sarcoma Protein FoxO Forkhead Box O EGFR Epidermal Growth Factor Receptor IL-1β Interleukin 1 Beta IL-6 Interleukin 6 TNF-α Tumor Necrosis Factor Alpha NLRP3 NOD-like receptor family pyrin domain containing 3 Declarations Funding Source This study was funded by the National Natural Science Foundation of China (Grant Number: 82160148). Availability of Data and Materials All data generated and analyzed during this study are included in this published article. Competing interests None. Author Contribution Yanan Wang,Yonfeng Lao and Rongxin Li: Conceptualization, Methodology, Software, Datacuration,Visualization, Experimental yerification Writing-Original draft.Chengyu You and Liangliang Qing: Conceptualization, Methodology, Software, Datacuration. Xi Xiao and Shuai Liu : Software, Datacuration. Wenyun Wang and Yu Zhao: Software,Validation.Zhilong Dong: Supervision, Writing-Reviewing and Editing, Funding acquisition.All authors have read and approved the manuscript. References Mandar R, Korrovits P, Rahu K, Rahu M, Sibul EL, Mehik A, Punab M (2020) Dramatically deteriorated quality of life in men with prostatitis-like symptoms. 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FEBS J 283 (10):1812-1822. doi:10.1111/febs.13627 McPherson SJ, Ellem SJ, Patchev V, Fritzemeier KH, Risbridger GP (2006) The role of Eralpha and ERbeta in the prostate: insights from genetic models and isoform-selective ligands. Ernst Schering Found Symp Proc (1):131-147. doi:10.1007/2789_2006_020 Meng LQ, Yang FY, Wang MS, Shi BK, Chen DX, Chen D, Zhou Q, He QB, Ma LX, Cheng WL, Xing NZ (2018) Quercetin protects against chronic prostatitis in rat model through NF-kappaB and MAPK signaling pathways. Prostate 78 (11):790-800. doi:10.1002/pros.23536 Park KR, Nam D, Yun HM, Lee SG, Jang HJ, Sethi G, Cho SK, Ahn KS (2011) beta-Caryophyllene oxide inhibits growth and induces apoptosis through the suppression of PI3K/AKT/mTOR/S6K1 pathways and ROS-mediated MAPKs activation. Cancer Lett 312 (2):178-188. doi:10.1016/j.canlet.2011.08.001 Mahurkar-Joshi S, Rankin CR, Videlock EJ, Soroosh A, Verma A, Khandadash A, Iliopoulos D, Pothoulakis C, Mayer EA, Chang L (2021) The Colonic Mucosal MicroRNAs, MicroRNA-219a-5p, and MicroRNA-338-3p Are Downregulated in Irritable Bowel Syndrome and Are Associated With Barrier Function and MAPK Signaling. Gastroenterology 160 (7):2409-2422 e2419. doi:10.1053/j.gastro.2021.02.040 Jiang Y, Fang B, Xu B, Chen L (2020) The RAS-PI3K-AKT-NF-kappaB pathway transcriptionally regulates the expression of BCL2 family and IAP family genes and inhibits apoptosis in fibrous epulis. J Clin Lab Anal 34 (3):e23102. doi:10.1002/jcla.23102 Wang Y, Yang R, Yan F, Jin Y, Liu X, Wang T (2022) Medicarpin Protects Cerebral Microvascular Endothelial Cells Against Oxygen-Glucose Deprivation/Reoxygenation-Induced Injury via the PI3K/Akt/FoxO Pathway: A Study of Network Pharmacology Analysis and Experimental Validation. Neurochem Res 47 (2):347-357. doi:10.1007/s11064-021-03449-0 Storz P (2011) Forkhead homeobox type O transcription factors in the responses to oxidative stress. Antioxid Redox Signal 14 (4):593-605. doi:10.1089/ars.2010.3405 Shi J, Yu T, Song K, Du S, He S, Hu X, Li X, Li H, Dong S, Zhang Y, Xie Z, Li C, Yu J (2021) Dexmedetomidine ameliorates endotoxin-induced acute lung injury in vivo and in vitro by preserving mitochondrial dynamic equilibrium through the HIF-1a/HO-1 signaling pathway. Redox Biol 41:101954. doi:10.1016/j.redox.2021.101954 Yao C, Li G, Cai M, Qian Y, Wang L, Xiao L, Thaiss F, Shi B (2017) Prostate cancer downregulated SIRP-alpha modulates apoptosis and proliferation through p38-MAPK/NF-kappaB/COX-2 signaling. Oncol Lett 13 (6):4995-5001. doi:10.3892/ol.2017.6070 Zhang Y, Wang L, Zhang M, Jin M, Bai C, Wang X (2012) Potential mechanism of interleukin-8 production from lung cancer cells: an involvement of EGF-EGFR-PI3K-Akt-Erk pathway. J Cell Physiol 227 (1):35-43. doi:10.1002/jcp.22722 Elkamhawy A, Hassan AHE, Paik S, Sup Lee Y, Lee HH, Shin JS, Lee KT, Roh EJ (2019) EGFR inhibitors from cancer to inflammation: Discovery of 4-fluoro-N-(4-(3-(trifluoromethyl)phenoxy)pyrimidin-5-yl)benzamide as a novel anti-inflammatory EGFR inhibitor. Bioorg Chem 86:112-118. doi:10.1016/j.bioorg.2019.01.017 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2024 Reviews received at journal 01 Apr, 2024 Reviewers agreed at journal 04 Feb, 2024 Reviewers agreed at journal 15 Jan, 2024 Reviewers invited by journal 10 Jan, 2024 Editor assigned by journal 08 Jan, 2024 Submission checks completed at journal 08 Jan, 2024 First submitted to journal 22 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3794889","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266051843,"identity":"f2dda9e4-a204-40e3-80dc-6ce1f5231a0b","order_by":0,"name":"Yanan Wang","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Wang","suffix":""},{"id":266051844,"identity":"8ab19142-edcf-4f7b-a837-45a8bf435224","order_by":1,"name":"Yonfeng Lao","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yonfeng","middleName":"","lastName":"Lao","suffix":""},{"id":266051845,"identity":"9f69a4b1-c1af-49ce-bb1f-035fa17f9c9a","order_by":2,"name":"Rongxin Li","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Rongxin","middleName":"","lastName":"Li","suffix":""},{"id":266051846,"identity":"e5373996-e190-4c28-9e45-8d039da80acb","order_by":3,"name":"Chengyu You","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chengyu","middleName":"","lastName":"You","suffix":""},{"id":266051847,"identity":"b9e228ea-737c-4742-81d8-af0d19147197","order_by":4,"name":"Liangliang Qing","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Liangliang","middleName":"","lastName":"Qing","suffix":""},{"id":266051848,"identity":"77c52196-f2ce-4cba-9f33-abd70168c830","order_by":5,"name":"Xi Xiao","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Xiao","suffix":""},{"id":266051849,"identity":"669905e1-7318-4c80-9dbb-fec76356429c","order_by":6,"name":"Shuai Liu","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Liu","suffix":""},{"id":266051850,"identity":"da6a5757-f897-4eaa-be96-08a95a3500e6","order_by":7,"name":"Wenyun Wang","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Wenyun","middleName":"","lastName":"Wang","suffix":""},{"id":266051851,"identity":"8fd70e0a-c8b6-4e9d-a807-be358f168e12","order_by":8,"name":"Yu Zhao","email":"","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhao","suffix":""},{"id":266051852,"identity":"ea9eb615-a198-4ac1-987b-30e486f1f245","order_by":9,"name":"Zhilong Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3RsQrCMBCA4ZRKXEK7tiD6CgGhkw/TIDgpOEkHhysVHVRcFXwIJ3FMCGSKuHZUXB3s5iTWWWnr5pBvzk+4O4QM4x9xG3j27BC3nohzGI2rJFYs1rjX9BeqS89aVUoSSbBs07Qf+JeJXV44JwGSkB7bQD+IGGDkzuZhYeJzAWLrddgKqVHKDg3k6eOuMKEiBn6j+S9xsk+Zxoh6g5JEWsBJKNlOomDIpnaFRFkgCM/HV7UAVUp8nScbeC8Zd71QK1I6i3OS8p5BfsrWVWSPaNx0Z8vi5AP57blhGIbx1QtRRVdW0akGWQAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Hospital of Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Zhilong","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2023-12-23 04:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3794889/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3794889/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49411284,"identity":"e4afb7c2-403c-4bcc-80d7-88f46f1b01fb","added_by":"auto","created_at":"2024-01-10 10:24:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5399983,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/55734ad3c414545768b223e1.jpg"},{"id":49412132,"identity":"e15a39f2-a89e-4598-a1e9-a9a673f45152","added_by":"auto","created_at":"2024-01-10 10:40:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9640197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003e2D, 3D structures and related information of melatonin. \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram for the targets of melatonin.\u003cstrong\u003e (C) \u003c/strong\u003eVenn diagram for the targets of CP/CPPS. \u003cstrong\u003e(D) \u003c/strong\u003eVenn diagram for the targets of melatonin and CP/CPPS.\u003cstrong\u003e (E)\u003c/strong\u003eProtein-protein interaction network of melatonin acting on CP/CPPS\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/42c72bf0f17b0263f949edbc.jpg"},{"id":49411280,"identity":"ac678360-9065-4ad6-a722-2cd3d8e1ebfd","added_by":"auto","created_at":"2024-01-10 10:24:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8137937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e PPI network diagram of hub genes. \u003cstrong\u003e(B) \u003c/strong\u003emodule 1. \u003cstrong\u003e(C)\u003c/strong\u003e module 2.\u003cstrong\u003e (D)\u003c/strong\u003e module 3\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/a49c8e8dbd5b75eceff1b325.jpg"},{"id":49411836,"identity":"54dc0a08-6df6-4960-ac14-3587261c7b98","added_by":"auto","created_at":"2024-01-10 10:32:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7828628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e GO functional analysis of melatonin acting on CP/CPPS. \u003cstrong\u003e(B)\u003c/strong\u003e KEGG pathway analysis of melatonin acting on CP/CPPS. \u003cstrong\u003e(C)\u003c/strong\u003e GO functional analysis of Module 1. \u003cstrong\u003e(D) \u003c/strong\u003eKEGG pathway analysis of Module 1\u003c/p\u003e\n\u003cp\u003eBP: Biological process; CC: Cellular components; MF: Molecular function\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/bf504c3dc3282b4cc438c873.jpg"},{"id":49411288,"identity":"40e769a1-23f2-44de-8514-10bf7b8ebc2d","added_by":"auto","created_at":"2024-01-10 10:24:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7414533,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular interaction of melatonin and the hub genes.\u003cstrong\u003e (A)\u003c/strong\u003emelatonin-AKT1. \u003cstrong\u003e(B)\u003c/strong\u003e melatonin-SRC. \u003cstrong\u003e(C)\u003c/strong\u003e melatonin-HSP90AA1.\u003cstrong\u003e (D) \u003c/strong\u003emelatonin-PTGS2.\u003cstrong\u003e(E) \u003c/strong\u003emelatonin-BCL2L1.\u003cstrong\u003e (F) \u003c/strong\u003emelatonin-ALB. \u003cstrong\u003e(G)\u003c/strong\u003e melatonin-CASP3.\u003cstrong\u003e (H)\u003c/strong\u003e melatonin-NFKB1.\u003cstrong\u003e(I) \u003c/strong\u003emelatonin-HIF1A.\u003cstrong\u003e (J)\u003c/strong\u003e melatonin-ESR1\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/63490146ee90bafcc9e1c3bb.jpg"},{"id":49412131,"identity":"ebd47d39-74a2-43f6-a74f-6b7dcdccf2fc","added_by":"auto","created_at":"2024-01-10 10:40:39","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":807639,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of melatonin treatment on LPS-induced cytotoxicity in RWPE-1 cells. \u003cstrong\u003e(A)\u003c/strong\u003eRWPE-1 cells were treated by melatonin (0, 100, 200, 300, 400, or 500μM) for 24 h. Cell viability was measured by CCK8 assay. \u003cstrong\u003e(B)\u003c/strong\u003e Cells were subjected to melatonin (100, 200 or 500μM) for 4 h, and then exposed to LPS (10ug/ml) for 24 h. Cell viability was determined by CCK8 assay\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/6c363633dcb3709d77bc10ad.jpg"},{"id":49411839,"identity":"92901f5b-69fb-400b-9e62-73419979dfeb","added_by":"auto","created_at":"2024-01-10 10:32:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1044899,"visible":true,"origin":"","legend":"\u003cp\u003eMelatonin reduces LPS-induced inflammatory factors ROS production in RWPE-1cells. \u003cstrong\u003e(A)\u003c/strong\u003e Relative mRNA expression of IL-1β. \u003cstrong\u003e(B)\u003c/strong\u003e Relative mRNA expression of IL-6. \u003cstrong\u003e(C)\u003c/strong\u003e Relative mRNA expression of TNF-α\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/1e98849017166bf92dbbed46.jpg"},{"id":49411285,"identity":"afea947b-2d7b-408c-8b88-27381dce7c5a","added_by":"auto","created_at":"2024-01-10 10:24:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1696885,"visible":true,"origin":"","legend":"\u003cp\u003eMelatonin reduces LPS-induced ROS production in RWPE-1cells. \u003cstrong\u003e(A)\u003c/strong\u003eThe ROS generation was measured according to the Experimental Section and determined by a fluorescent microscope (Magnification is 100×). \u003cstrong\u003e(B)\u003c/strong\u003eThe relative fluorescence intensity (fold of Control group)\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/a523697213bb00e8276f06be.jpg"},{"id":49411287,"identity":"b452913a-c27e-4679-bfa6-cc150be59614","added_by":"auto","created_at":"2024-01-10 10:24:40","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2232749,"visible":true,"origin":"","legend":"\u003cp\u003eMelatonin reduces LPS-induced cell apoptosis in RWPE-1cells. \u003cstrong\u003e(A) \u003c/strong\u003eThe overall apoptosis rate was detected by flow cytometry. \u003cstrong\u003e(B) \u003c/strong\u003eApoptosis quantitative analysis\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/5972fb1119f8e3d97def5731.jpg"},{"id":49412678,"identity":"4ef4e44b-c61d-4513-a573-e5eab8829146","added_by":"auto","created_at":"2024-01-10 10:48:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1575022,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3794889/v1/e478cbaf-cd5b-434c-ab9d-3c445aae063d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network pharmacological analysis and experimental study of melatonin in chronic prostatitis/chronic pelvic pain syndrome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstatitis, a prevalent and distinctive genitourinary syndrome, afflicts adult males across all age groups, with an incidence ranging from 8.4\u0026ndash;25% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Despite its ubiquity, the etiological underpinnings of prostatitis remain elusive. Clinically, it manifests through a spectrum of symptoms, including pelvic pain, irritative urinary tract symptoms, and prostatic inflammation, carrying potential ramifications for male fertility and sexual function[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Characterized by a high incidence and a propensity for recurrence, prostatitis significantly compromises the affected individuals' quality of life (QoL)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The National Institutes of Health (NIH) in the United States classifies prostatitis into four types: Type I, Acute Bacterial Prostatitis (ABP); Type II, Chronic Bacterial Prostatitis (CBP); Type III, Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) and Type IV, Asymptomatic Inflammatory Prostatitis (AIP). Among these, CP/CPPS stands out as the most prevalent, constituting approximately 90% of all prostatitis cases[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pathogenesis of CP/CPPS remains elusive and is characterized by multifactorial etiology. Current hypotheses regarding the pathogenic mechanisms of CP/CPPS include defects in the integrity and function of the urogenital system, autoimmunity, latent infections, endocrine imbalances, urinary dysfunction, peripheral and central sensitization, neuroplasticity, pelvic floor muscle spasms, and psychosocial factors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Oxidative stress, immune dysregulation, and alterations in the local microenvironment are considered pivotal pathogenic factors in CP/CPPS [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Existing therapeutic approaches encompass antibiotics, α-adrenergic blockers, anti-inflammatory agents, herbal remedies, and hormone therapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], However, due to the lack of precise targeting of the underlying causes, satisfactory therapeutic outcomes are often challenging to achieve. Consequently, there is an urgent need to explore novel and effective treatments specifically targeting the pathogenic mechanisms, representing a focal area of research in the clinical management of CP/CPPS.\u003c/p\u003e \u003cp\u003eN-acetyl-5-methoxytryptamine (Melatonin, MT), a neuroendocrine hormone synthesized by the pineal gland and various other cell types, including macrophages, monocytes, and lymphocytes, exerts various physiological functions through G protein-coupled MT1 and MT2 receptors, both in a dependent and independent manner. These functions encompass antioxidative, anti-inflammatory, antitumor, and immune-regulatory effects[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have demonstrated that MT can alleviate epithelial cell and smooth muscle cell atrophy and inflammatory infiltration in the prostate tissues of diabetic rats[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e],inhibit the NLRP3 inflammasome signaling pathway, thereby alleviating prostatitis and pelvic pain [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]and ameliorate symptoms such as nocturia and urinary frequency in patients with benign prostatic hyperplasia[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, there remains a relative scarcity of foundational and clinical research data on MT's role in the treatment of CP/CPPS, and its underlying mechanisms are yet to be elucidated, warranting further investigation.\u003c/p\u003e \u003cp\u003eNetwork pharmacology is grounded in the construction of a comprehensive network involving \"disease-gene-target-drug\" interactions. Utilizing various databases and software tools, this approach establishes multilayered networks to explore the correlation between drug components and disease targets, elucidating the mechanisms of drug action. Molecular docking techniques start from known structures of receptors and ligands. By calculating relevant parameters, these techniques predict the binding affinity and binding modes of receptors and ligands, facilitating the identification of potential drug targets.\u003c/p\u003e \u003cp\u003eThis study integrates network pharmacology and molecular docking techniques, complemented by cellular experiments, to explore the potential mechanistic insights into the therapeutic efficacy of MT in treating CP/CPPS. The objective is to furnish a scientific foundation for clinical applications and to advance the development of novel therapeutic agents. The technical strategy workflow is delineated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1 Network pharmacology analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e1.1 MT Potential Target Prediction\u003c/h2\u003e \u003cp\u003eMT-related information, including its name and structure, was systematically retrieved 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) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Subsequently, predictive analyses of MT's binding targets were conducted using the SwissTargetPrediction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Super-PRED database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prediction.charite.de/\u003c/span\u003e\u003cspan address=\"https://prediction.charite.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), SEA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sea.bkslab.org\u003c/span\u003e\u003cspan address=\"http://sea.bkslab.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and PharmMapper database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lilab-ecust.cn/pharmmapper/\u003c/span\u003e\u003cspan address=\"http://www.lilab-ecust.cn/pharmmapper/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Through the integration of target genes obtained from these databases, duplicate entries were removed to ascertain the potential targets of MT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Disease Potential Target Prediction\u003c/h2\u003e \u003cp\u003eDisease-related targets were gathered through searches on Genecards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://Genecards.Org\u003c/span\u003e\u003cspan address=\"http://Genecards.Org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Disgenet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and OMIM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omim.org/\u003c/span\u003e\u003cspan address=\"https://www.omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases using the keyword \"chronic prostatitis with chronic pelvic pain syndrome.\" In Genecards, a relevance score threshold of \u0026ge;\u0026thinsp;10 was set to filter for disease-associated targets. The obtained disease-related targets were compiled and duplicates were subsequently removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.3Common Targets of MT and CP/CPPS\u003c/h2\u003e \u003cp\u003eUtilizing the online plotting tool platform provided by Xiantao Academic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.xiantaozi.com/\u003c/span\u003e\u003cspan address=\"https://www.xiantaozi.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the targets of MT and CP/CPPS were inputted to generate a Venn diagram. The intersection of the two sets revealed the common targets shared between MT and CP/CPPS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.4Construction of Protein-Protein Interaction (PPI) Network and Core Target Selection\u003c/h2\u003e \u003cp\u003eThe common targets were queried in 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), specifying the protein species as \"Homo sapiens,\" and setting the minimum interaction threshold to 0.400. Subsequently, a Protein-Protein Interaction (PPI) network diagram was constructed. After removing unrelated nodes, the network was imported into Cytoscape (version 3.10.1) software for topological analysis using the Network Analyzer tool, with nodes sorted based on their degree values. The top 10 core targets of this network were identified using the Maximum Clique Centrality (MCC) algorithm from the CytoHubba plugin. Additionally, the core network of this network was obtained using the Molecular Complex Detection (MCOD) plugin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.5 GO and KEGG Enrichment Analysis\u003c/h2\u003e \u003cp\u003eUtilizing R software (version 4.3.1) and the cluster Profile bioinformatics package, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on the target genes, applying significance criteria of P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Molecular Docking\u003c/h2\u003e \u003cp\u003eMolecular docking validations of drugs and core targets were conducted using Autodock-Vina (Version 1.1.2) software. The three-dimensional structures of target proteins were obtained from the RCSB PDB protein structure 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). After processing the receptor and ligand using Autodocktools (Version 1.5.6) for semi-flexible docking, the docking models with the lowest binding energy were selected for visualization. PyMOL (Version 2.3.0) was employed for the study of their interactions and further visual analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2 Cell experiments\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.1Drugs and Reagents\u003c/h2\u003e \u003cp\u003eMT was purchased from GlpBio (Shanghai, China, Catalog Number: 73-31-4, purity\u0026thinsp;\u0026gt;\u0026thinsp;98.5%). Fetal bovine serum was obtained from ExCell Bio (Jiangsu, China, Catalog Number: FSP500). High-glucose DMEM was purchased from Gibco (USA, Lot Number: 6123011). DMSO was acquired from BioSharp (Anhui, China, Catalog Number: BL165B). LPS (lipopolysaccharide) was obtained from BioSharp (Anhui, China, Catalog Number: BS904). CCK-8 (Cell Counting Kit-8) was purchased from BioSharp (Anhui, China, Catalog Number: BS350B). The Reactive Oxygen Species (ROS) detection kit was procured from Jiancheng Bio (Nanjing, China, Catalog Number: E004-1-1), and the apoptosis detection kit was obtained from Union-Biotech (Shanghai, China, Catalog Number: AT107). The MT solution was prepared according to the manufacturer's instructions, with the stock solution (500 mg of MT) prepared in Dimethyl sulfoxide (DMSO). Subsequent dilutions were made using the base culture medium according to the experimental protocol. The final solvent concentration was \u0026le;\u0026thinsp;0.1% (v/v), having no impact on the experimental procedures[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cell Culture\u003c/h2\u003e \u003cp\u003eThe RWPE-1 cell was obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China), catalog number: SCSP-5025. The cells were cultured in high-glucose DMEM supplemented with 10% fetal bovine serum. The cells were maintained at 37\u0026deg;C in a humidified incubator with 5% CO2. Passages were performed when the cells reached approximately 90% confluency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cell Viability Assay\u003c/h2\u003e \u003cp\u003eThe cell toxicity analysis comprised two parts as per experimental requirements. In the first part, cells were treated with different concentrations of MT (0, 100, 200, 300, 400, 500\u0026micro;M) for 24 hours. In the second part, cells were pre-treated with MT (100, 200, or 500\u0026micro;M) for 4 hours, followed by stimulation with LPS at a concentration of 10\u0026micro;g/ml for an additional 24 hours. Subsequently, after the specified treatments, cells were incubated with 10\u0026micro;L of CCK-8 reagent per well at 37\u0026deg;C for 2 hours. The optical density (OD) values at 450 nm were measured using a microplate reader. Cell viability was calculated using the formula:\u003c/p\u003e \u003cp\u003eCell Viability= (As - Ab/Ac \u0026ndash; Ab) \u0026times;100%. As: Absorbance of experimental wells (culture medium, cells, drug, CCK8); Ac: Absorbance of control wells (culture medium, cells, CCK8); Ab: Absorbance of blank wells (culture medium, CCK8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eRWPE-1 cells were randomly divided into three groups: (a) RWPE-1 group (normal control); (b) LPS group (LPS-induced inflammation, treated with LPS 10\u0026micro;g/ml for 24 hours[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]);(c) MT\u0026thinsp;+\u0026thinsp;LPS group (pre-treated with MT 500\u0026micro;M for 4 hours, followed by LPS treatment at 10\u0026micro;g/ml for 24 hours).\u003c/p\u003e \u003cp\u003eIn brief, total RNA was extracted from RWPE-1 cells using SteadyPure Fast RNA Extraction Kit (Hunan, China, Catalog Number: AG21023) and reverse transcribed using Evo M-MLV Reverse Transcription PreMix Kit (Hunan, China, Catalog Number: AG11728). Subsequently, real-time quantitative PCR (RT-qPCR) was performed on a Bio-RAD real-time PCR system using SYBR Green Pro Taq HS PreMix Kit (Hunan, China, Catalog Number: AG11701) according to the manufacturer's instructions. The primer sequences are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The relative mRNA expression levels of the target genes were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, with GAPDH as the internal control for normalization.\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\u003ePrimer sequence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequences(5\u0026rsquo;to3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1β\u003c/p\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TGATGGCTTATTACAGTGGCA\u003c/p\u003e \u003cp\u003eR: TCGTGCACATAAGCCTCGTT\u003c/p\u003e \u003cp\u003eF: AGAGGCACTGGCAGAAAACA\u003c/p\u003e \u003cp\u003eR: TCACCAGGCAAGTCTCCTCA\u003c/p\u003e \u003cp\u003eF: GAGGCCAAGCCCTGGTATG\u003c/p\u003e \u003cp\u003eR: CGGGCCGATTGATCTCAGC\u003c/p\u003e \u003cp\u003eF: ATCGTGGAAGGACTCATGACCA\u003c/p\u003e \u003cp\u003eR: ATCGTGGAAGGACTCATGACCA\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Measurement of Intracellular ROS\u003c/h2\u003e \u003cp\u003eThe intracellular ROS levels, considered a crucial indicator of cellular oxidative stress, were detected using 2',7'-dichlorofluorescin diacetate (DCFH-DA). RWPE-1 cells (1\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/well) were pre-incubated with MT (500\u0026micro;M) for 4 hours, followed by stimulation with or without LPS at a concentration of 10\u0026micro;g/ml for an additional 24 hours. DCFH-DA (10\u0026micro;mol/L) was added, and cells were treated in the dark at 37\u0026deg;C for 30 minutes. Subsequently, cells were washed twice with PBS. The intracellular ROS was observed using a fluorescence microscope, and the fluorescence intensity of DCF indicated the ROS levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Cell Apoptosis\u003c/h2\u003e \u003cp\u003eLogarithmically growing RWPE-1 cells were digested, suspended, and then seeded into a 6-well plate. The cell density was adjusted to approximately 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well. After the intervention, cell samples were prepared following the instructions of the Annexin V-APC/PI Apoptosis Kit. Flow cytometry was employed using a Beckman flow cytometer to detect the samples. The data were processed using FlowJo software (V10.8.1, Stanford University Laboratory, Stanford, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.7Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll data are presented as mean SD or SEM. Statistical analysis was conducted using one-way analysis of variance (ANOVA) with GraphPad Prism software (version 9.5.1). Statistically significant differences are denoted as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e1 MT and CP/CPPS-Related Target Genes\u003c/h2\u003e \u003cp\u003eIn this study, the prediction of MT targets was performed using the SwissTargetPrediction, Super-PRE, SEA, and PharmMapper databases, resulting in a total of 504 target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Prediction of CP/CPPS targets was conducted using Genecards, Disgenet, and OMIM databases, leading to 3954 related target genes after eliminating duplicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Utilizing the Venn diagram tool on Xiantao Academic, the intersecting targets between MT and CP/CPPS were identified, totaling 284 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2 Construction of PPI Network and Analysis of Core Targets\u003c/h2\u003e \u003cp\u003eThe 284 common target genes were imported into the STRING database to generate a PPI network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). This network comprised 284 nodes and 4946 edges, with one independent node removed for visualization in Cytoscape software. Using the CytoHubba plugin and the MCC algorithm, the top 10 hub genes based on degree value were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA): CASP3, HIF1A, HSP90AA1, AKT1, NFKB1, BCL2L1, SRC, ESR1, ALB, and PTGS2. Further analysis involved module division and clustering analysis using MCODE on the PPI network. A total of 9 modules were obtained, with 3 modules having scores\u003e5. Module one (score\u0026thinsp;=\u0026thinsp;35.846) included 40 nodes and 1398 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB); module two (score\u0026thinsp;=\u0026thinsp;8.824) comprised 35 nodes and 300 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC); module three (score\u0026thinsp;=\u0026thinsp;5.931) consisted of 30 nodes and 172 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3 GO and KEGG Enrichment Analysis\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.1 Enrichment Analysis of Common Targets in GO and KEGG\u003c/h2\u003e \u003cp\u003eUsing R software, we conducted GO functional analysis and KEGG pathway analysis for the 284 common targets of MT and CP/CPPS. The results of GO functional analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) showed that the 284 common targets were enriched in a total of 3234 GO terms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including 2887 biological process terms (BP), 100 cellular component terms (CC), and 247 molecular function terms (MF). The top 10 enriched terms were selected for visualization. GO analysis indicated that biological processes such as positive regulation of the MAPK cascade, wound healing, cellular response to chemical stress, response to lipopolysaccharide, and response to reactive oxygen species play crucial roles in MT's treatment of CP/CPPS. Cellular components primarily involved membrane rafts, vesicles, and membrane microdomains. Molecular functions included protein serine/threonine kinase activity, endopeptidase activity, protein tyrosine kinase activity, transmembrane receptor protein kinase activity, transcription coactivator binding, nuclear receptor activity, and ligand-activated transcription factor activity. The results of KEGG enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) showed enrichment in 177 pathways, including cancer-related pathways, PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and Apoptosis signaling pathways. The top 30 enriched pathways were selected for visualization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2 Enrichment Analysis of Key Module Targets After Filtering\u003c/h2\u003e \u003cp\u003eUsing R software, we conducted GO and KEGG analysis for the module with the highest clustering analysis score obtained from the MCODE plugin (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The results suggest that MT may improve CP/CPPS by modulating signaling pathways such as FoxO, PI3K-Akt, HIF-1, AMPK, Apoptosis, TGF-β, and TNF.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4 Molecular docking\u003c/h2\u003e \u003cp\u003eMolecular docking was performed between MT and the 10 core targets (CASP3, HIF1A, HSP90AA1, AKT1, NFKB1, BCL2L1, SRC, ESR1, ALB, PTGS2) to calculate the binding energy. Lower negative binding energies indicate more stable binding, and a binding energy below \u0026minus;\u0026thinsp;1.2 kcal/mol suggests good binding affinity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe docking results indicated that MT exhibited high binding affinity with the target proteins (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The docking results were visualized for further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\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\u003eBinding energy of melatonin and core target\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003cp\u003eSRC\u003c/p\u003e \u003cp\u003eHSP90AA1\u003c/p\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003cp\u003eBCL2L1\u003c/p\u003e \u003cp\u003eALB\u003c/p\u003e \u003cp\u003eCASP3\u003c/p\u003e \u003cp\u003eNFKB1\u003c/p\u003e \u003cp\u003eHIF1A\u003c/p\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5KCV\u003c/p\u003e \u003cp\u003e2H8H\u003c/p\u003e \u003cp\u003e7KRJ\u003c/p\u003e \u003cp\u003e5F19\u003c/p\u003e \u003cp\u003e3SP7\u003c/p\u003e \u003cp\u003e4BKE\u003c/p\u003e \u003cp\u003e1CP3\u003c/p\u003e \u003cp\u003e1SVC\u003c/p\u003e \u003cp\u003e4H6J\u003c/p\u003e \u003cp\u003e6PIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003cp\u003e-6.4\u003c/p\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003cp\u003e-5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5 Experimental Validation Results\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section4\"\u003e \u003ch2\u003e5.1 MT Inhibits LPS-Induced Cytotoxicity\u003c/h2\u003e \u003cp\u003eIn this study, the impact of MT on cell viability was assessed using the CCK-8 assay. The results revealed that MT at different concentrations (0-500\u0026micro;M) did not exhibit toxicity to RWPE-1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequently, the study investigated the influence of MT on LPS-induced cytotoxicity in RWPE-1 cells, and the results demonstrated a significant inhibition of LPS-induced cytotoxicity by MT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.2 MT Suppresses LPS-Induced Inflammation in RWPE-1 Cells\u003c/h2\u003e \u003cp\u003eTo investigate the anti-inflammatory effect of MT on RWPE-1 cells, we utilized the RT-qPCR method to measure the mRNA levels of IL-1β, IL-6, and TNF-α after LPS treatment. In comparison to the control group, the LPS-treated group showed an upregulation in the expression of IL-1β, IL-6, and TNF-α. However, MT attenuated the levels of inflammatory factors such as IL-1β, IL-6, and TNF-α (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.3 MT Suppresses LPS-Induced Intracellular ROS Generation\u003c/h2\u003e \u003cp\u003eThe ability of LPS to induce oxidative stress was determined by measuring the levels of ROS in RWPE-1 cells. Fluorescence microscopy images (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) and quantitative data (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) demonstrated that the production of intracellular ROS was lower in the MT\u0026thinsp;+\u0026thinsp;LPS group compared to the LPS group. The study results indicate that MT effectively inhibits the excessive production of ROS induced by LPS in RWPE-1 cells, highlighting its potent antioxidant properties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.4 MT Suppresses LPS-Induced Apoptosis in RWPE-1 Cells\u003c/h2\u003e \u003cp\u003eCell apoptosis was assessed using flow cytometry, and the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) demonstrated a significant reduction in the number of apoptotic cells with MT treatment compared to the LPS group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCP/CPPS is a complex and multifaceted condition with diverse symptoms, believed to result from various etiological factors such as oxidative stress, immune system dysregulation, and alterations in the local microenvironment[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In CP/CPPS patients, the occurrence of inflammation is attributed to the damage of prostatic stromal and epithelial cells. Prolonged exposure of prostate tissue to the inflammatory environment can lead to the substantial release of ROS, causing structural and functional impairments of prostatic proteins and DNA modifications[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].MT, known for its roles in antioxidation, anti-inflammation, anti-tumor activity, and immune modulation, has the potential to mitigate oxidative damage. MT functions by scavenging free radicals and activating antioxidant enzymes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous research has indicated that MT, by activating Sirt1, inhibits the NLRP3 inflammasome signaling pathway, thereby alleviating inflammation and pelvic pain in experimental autoimmune prostatitis mice. This suggests a therapeutic potential of MT in treating CP/CPPS[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study employed network pharmacology and molecular docking methods, utilizing relevant databases and software, to preliminarily investigate the potential mechanisms of MT in treating CP/CPPS. The study obtained a total of 284 common targets of MT and CP/CPPS, indicating a good targeting effect of MT in treating CP/CPPS. Through PPI network analysis, the key core targets of MT in treating CP/CPPS were identified as AKT1, SRC, HSP90AA1, PTGS2, BCL2L1, ALB, CASP3, NFKB1, HIF1A and ESR1. AKT1 regulates and controls macrophages, with main pathways involving the promotion of anti-inflammatory cytokine production, phagocytosis, and autophagy to maintain cellular balance[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. SRC primarily controls cell proliferation, metabolism, and survival. It plays a crucial role in macrophage-mediated autoimmune responses, inflammation factor production, and induction of cell migration. SRC inhibitors can suppress immune reactions and have anti-inflammatory effects[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].HSP90AA1 as a stress-induced protein, it can regulate protein kinases and maintain cellular homeostasis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. PTGS2, also known as cyclooxygenase-2 (COX-2), is involved in the conversion of arachidonic acid to prostaglandins and is expressed during the inflammatory process. Studies have confirmed the association between COX-2 inhibitors and the occurrence of CP/CPPS[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].BCL2L1,an anti-apoptotic BCL2 protein that counteracts pro-apoptotic BH3 proteins (such as BID, BIM, and NOXA) or directly isolates effectors BAK and BAX proteins [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].CASP3, a key factor downstream in the process of apoptosis, plays a crucial role. In the cytoplasm, free Bax proteins can directly form Bax-Bax homodimers on the mitochondrial membrane, altering the permeability of the mitochondrial membrane. This alteration activates the caspase cascade, leading to the release of cytochrome C into the cytoplasm, initiating the apoptotic process and triggering a chain reaction of cellular apoptosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].ALB, an inflammation-related receptor that can bind and transport pro-inflammatory factors and cytokines, stimulating immune cell activation and promoting an inflammatory response [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].Under hypoxic conditions, HIF1A remains in a stable state and can activate the transcription and expression of related target genes, resulting in a cascade of cellular processes such as damage, proliferation, apoptosis, invasion, and metastasis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. NFKB1 is one of the key inducible transcription factors involved in various signaling pathways, including cell apoptosis and inflammatory responses [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. ESR1 is a subtype of estrogen receptor belonging to the estrogen receptor family. Estrogen can induce prostate inflammation through the mediation of ESR1 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].In summary, the analysis of core targets suggests that MT may improve CP/CPPS by regulating inflammation, cell apoptosis, and oxidative stress.\u003c/p\u003e \u003cp\u003eThe KEGG pathway enrichment analysis reveals that signaling pathways such as PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and Apoptosis may play a crucial role in the action of MT during the development of CP/CPPS. The Ras signaling pathway is pivotal in various physiological processes, including cell proliferation, migration, and apoptosis. Once Ras protein binds to GTP, it becomes activated, recruits Raf to the cell membrane, and subsequently activates the MAPK pathway through phosphorylation of MAPK[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].The MAPK signaling pathway can be activated by reactive oxygen species and numerous inflammatory factors, further promoting the production of other inflammatory factors[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].Inhibitors of MAPK have been shown to effectively alleviate inflammation and neuropathic pain in animal models[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The PI3K/Akt signaling pathway plays a crucial role in cell apoptosis and autophagy. Ras can activate PI3K, and activated PI3K directly acts on downstream factor Akt, thereby activating the NF-κB signaling pathway[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and the FOXO signaling pathway[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], Additionally, FoxO proteins are critical regulatory factors in cellular responses to oxidative stress, providing resistance against oxidative stress [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The HIF-1 signaling pathway is involved in the regulation of genes responding to low oxygen levels and can promote the expression of factors such as heme oxygenase-1 (HO-1) and vascular endothelial growth factor (VEGF). HO-1 has antioxidative, anti-inflammatory, and vasodilatory effects [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].Oxidative stress can lead to lipid peroxidation damage in prostate tissue, subsequently activating COX-2, resulting in the upregulation of prostaglandins (PG), especially PGE2. PGE2 can inhibit the release of β-endorphin, which has analgesic effects, thereby causing pain. COX-2 is regulated by the MAPK and NF-κB signaling pathways, and inhibition of the MAPK and NF-κB pathways can suppress COX-2 expression, thus alleviating pain[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].The epidermal growth factor receptor (EGFR) is widely distributed in the body's epithelial tissues and plays a crucial regulatory role in the inflammatory process[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In vitro experiments have demonstrated that EGFR can inhibit LPS-induced iNOS expression and the production of IL-1β, IL-6, and TNF-α through NF-κB inactivation.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]。\u003c/p\u003e \u003cp\u003eThe results of cell experiments indicate that MT can inhibit the production of inflammatory factors such as IL-1β, IL-6, and TNF-α induced by LPS in RWPE-1 cells. Additionally, it reduces cell apoptosis and the generation of ROS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study utilized network pharmacology and molecular docking to reveal the potential mechanisms of MT in treating CP/CPPS. It is speculated that MT may act on targets such as AKT1, SRC, HSP90AA1, PTGS2, BCL2L1, ALB, CASP3, NFKB1, HIF1A, and ESR1, regulating inflammation, oxidative stress, and cell apoptosis through signaling pathways including PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and Apoptosis. Cell experiments demonstrated that MT can inhibit the levels of inflammatory factors such as IL-1β, IL-6, TNF-α, as well as reduce ROS and cell apoptosis induced by LPS in RWPE-1 cells improving CP/CPPS. Given the limitations of cell experiments, future research may employ animal experiments to further investigate these mechanisms and enhance the accuracy of experimental predictions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Melatonin\u003c/p\u003e\n\u003cp\u003eCP/CPPS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chronic prostatitis/chronic pelvic pain syndrome\u003c/p\u003e\n\u003cp\u003eRWPE-1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human prostate epithelial cells\u003c/p\u003e\n\u003cp\u003eLPS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lipopolysaccharide\u003c/p\u003e\n\u003cp\u003eAKT1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Protein Kinase B\u003c/p\u003e\n\u003cp\u003eSRC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Proto-Oncogene Tyrosine-Protein Kinase Src\u003c/p\u003e\n\u003cp\u003eHSP90AA1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Heat Shock Protein 90 Alpha Family Class A Member 1\u003c/p\u003e\n\u003cp\u003ePTGS2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Prostaglandin-Endoperoxide Synthase 2\u003c/p\u003e\n\u003cp\u003eBCL2L1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BCL2 Like 1\u003c/p\u003e\n\u003cp\u003eALB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Albumin\u003c/p\u003e\n\u003cp\u003eCASP3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Caspase 3\u003c/p\u003e\n\u003cp\u003eNFKB1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Nuclear Factor Kappa B Subunit 1\u003c/p\u003e\n\u003cp\u003eHIF1A\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hypoxia Inducible Factor 1 Subunit Alpha\u003c/p\u003e\n\u003cp\u003eESR1\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Estrogen Receptor 1\u003c/p\u003e\n\u003cp\u003ePI3K\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Phosphatidylinositol 3-Kinas\u003c/p\u003e\n\u003cp\u003eMAPK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mitogen-Activated Protein Kinase\u003c/p\u003e\n\u003cp\u003eRas\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Rat Sarcoma Protein\u003c/p\u003e\n\u003cp\u003eFoxO\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Forkhead Box O\u003c/p\u003e\n\u003cp\u003eEGFR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Epidermal Growth Factor Receptor\u003c/p\u003e\n\u003cp\u003eIL-1\u0026beta;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interleukin 1 Beta\u003c/p\u003e\n\u003cp\u003eIL-6\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interleukin 6\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tumor Necrosis Factor Alpha\u003c/p\u003e\n\u003cp\u003eNLRP3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;NOD-like receptor family pyrin domain containing 3\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Source\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (Grant Number: 82160148).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYanan Wang,Yonfeng Lao and Rongxin Li: Conceptualization, Methodology, Software, Datacuration,Visualization, Experimental yerification Writing-Original draft.Chengyu You and Liangliang Qing: Conceptualization, Methodology, Software, Datacuration. Xi Xiao and Shuai Liu : Software, Datacuration. Wenyun Wang and Yu Zhao: Software,Validation.Zhilong Dong: Supervision, Writing-Reviewing and Editing, Funding acquisition.All authors have read and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMandar R, Korrovits P, Rahu K, Rahu M, Sibul EL, Mehik A, Punab M (2020) Dramatically deteriorated quality of life in men with prostatitis-like symptoms. Andrology 8 (1):101-109. doi:10.1111/andr.12647\u003c/li\u003e\n\u003cli\u003eZhang J, Zhang X, Cai Z, Li N, Li H (2019) The Lifetime Risk and Prognosis of Chronic Prostatitis/Chronic Pelvic Pain Syndrome in the Middle-Aged Chinese Males. 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Bioorg Chem 86:112-118. doi:10.1016/j.bioorg.2019.01.017\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Melatonin, prostatitis, Network pharmacology, Molecular docking, RWPE-1, LPS","lastPublishedDoi":"10.21203/rs.3.rs-3794889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3794889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aims to explore the potential mechanisms of melatonin (MT) in treating chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) using network pharmacology and molecular docking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The target genes of MT were acquired from the Swiss Target Prediction, Superpred, SEA, and PharmMapper databases and the CP/CPPS targets were collected based on OMIM, Disgenet, and Genecards databases. The intersection of MT and CP/CPPS target genes was analyzed. A PPI network was constructed using Cytoscape to identify core targets. The shared targets underwent GO and KEGG enrichment analyses by Using R software. Molecular docking of MT with core targets was performed using AutoDock and PyMol. And using cell experiments to verify the potential effect of MT in CP/CPPS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eNetwork pharmacology analysis reveals 284 shared targets between MT and CP/CPPS, with AKT1, SRC, HSP90AA1, PTGS2, BCL2L1, ALB, CASP3, NFKB1, HIF1A, and ESR1 identified as key targets. Enrichment analysis indicates that MT affects CP/CPPS through various biological processes, and pathway analysis emphasizes the significance of PI3K-Akt, MAPK, Ras, FoxO, HIF-1, EGFR, and apoptosis pathways. Molecular docking confirms strong binding between MT and core targets. Cell experiments demonstrate that MT can inhibit the secretion of IL-1β, IL-6, and TNF-α in LPS induced RWPE-1 cells, alleviate inflammation, and suppress cell apoptosis and oxidative stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Network pharmacology, molecular docking and cell experiments showed that MT could play a role in CP/CPPS by regulating multiple targets and pathways. This provides valuable insights for a more in-depth investigation into the molecular mechanisms and clinical applications of MT in CP/CPPS treatment.\u003c/p\u003e","manuscriptTitle":"Network pharmacological analysis and experimental study of melatonin in chronic prostatitis/chronic pelvic pain syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 10:24:35","doi":"10.21203/rs.3.rs-3794889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-21T09:44:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-01T10:20:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7997095e-6044-4440-89de-dbc1f12d6b86","date":"2024-02-04T12:23:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23905046-3513-4c6e-aac2-400255705ee5_SNPRID","date":"2024-01-15T06:59:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-10T16:32:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-09T00:43:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-09T00:43:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2023-12-23T04:52:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fd59b891-34dd-4d4e-aedb-27e50d5f6792","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-22T19:55:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-10 10:24:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3794889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3794889","identity":"rs-3794889","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}