Research on the molecular mechanism of celastrol targeting CTNNB1/STAT3 to inhibit uveal melanoma based on network pharmacology and multi-omics analysis

Research on the molecular mechanism of celastrol targeting CTNNB1/STAT3 to inhibit uveal melanoma based on network pharmacology and multi-omics analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on the molecular mechanism of celastrol targeting CTNNB1/STAT3 to inhibit uveal melanoma based on network pharmacology and multi-omics analysis zhanglong li, Ruofan Xi, Xudong Han, Ping Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7716555/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Uveal melanoma (UM) is among the most prevalent intraocular malignant tumors worldwide. Celastrol exhibits broad-spectrum anticancer properties; however, its underlying therapeutic mechanism in UM is yet to be elucidated. In this study, a network pharmacology approach was employed to identify potential common targets of celastrol and UM. These targets were further analyzed in conjunction with transcriptomic data and machine learning algorithms, which led to the identification of CTNNB1 and STAT3 as key molecular targets. The functional roles of these targets were investigated through immune infiltration analysis and single-cell RNA sequencing (scRNA-seq), while the binding stability between celastrol and CTNNB1/STAT3 was assessed using molecular docking (MD) and molecular dynamics simulation (MDS). Subsequently, celastrol was administered to B16-F10 and C918 cell lines, demonstrating that it significantly suppresses cell proliferation and migration by downregulating CTNNB1 and STAT3 expression, while simultaneously inducing apoptosis and cell cycle arrest. Moreover, real-time quantitative PCR (qPCR) and western blot (WB) analyses corroborated the modulation of target expression levels. Therefore, celastrol exerts potent anti-tumor effects in UM by inhibiting the CTNNB1 and STAT3 signaling pathways, thereby suppressing tumor cell proliferation and metastasis, as well as promoting cell cycle arrest and apoptosis. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Health sciences/Oncology Celastrol Uveal melanoma Network pharmacology Machine learning Molecular Dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Uveal melanoma (UM), also referred to as choroidal melanoma, is a highly aggressive and metastatic malignant tumor 1 that represents the most common primary intraocular malignancy in adults 2 . Approximately 50% of patients develop hematogenous metastasis, with the median survival time after metastasis ranging from 10 to 13 months, and the overall cure rate remains at zero 3 . The primary treatment methods for UM include surgery, plaque brachytherapy, phototherapy, and charged particle radiotherapy (CPT) 4 . However, chemotherapy regimens and molecularly targeted therapies exhibit limited therapeutic efficacy and are associated with notable adverse effects in the treatment of these cases 5 . The occurrence and progression of UM represent a complex, multistage process influenced by multiple regulatory factors. In addition to GNAQ/GNA11 2 genetic mutations being widely recognized as the initiating events in tumor formation and BAP1 6 mutations serving as key drivers of metastasis, the aberrant activation of several critical signaling pathways such as PI3K/AKT/mTOR, Wnt/β-catenin, and JAK/STAT3 7–11 also plays a central role in the development of the malignant UM phenotype. Celastrol is an active component in the traditional Chinese medicinal plant Tripterygium wilfordii Hook.f. It has been regarded as one of the most important traditional drug compounds in modern drug development 12 due to its significant therapeutic potential in various types of cancer 13 . Studies have shown that celastrol can inhibit key biological behaviors of tumor cells, including proliferation and migration, through multiple molecular pathways 14 , 15 . Celastrol exerts broad anti-cancer effects through the modulation of multiple signaling pathways, including NF-κB, PRDX, and JAK/STAT3 15–17 . Notably, its inhibitory impact on the PI3K/AKT/mTOR pathway has been validated as a promising therapeutic target in melanoma treatment 18 . However, limitations persist in the current research, and the exact molecular mechanism of celastrol's effect on UM needs to be further explored. Network pharmacology, integrated with machine learning and single-cell RNA sequencing (scRNA-seq) technology, enables the precise identification of disease-associated core targets 19 , 20 . Furthermore, when combined with molecular docking (MD) and molecular dynamics simulation (MDS), this approach can effectively validate the binding stability between drug candidates and target proteins 21 . Consequently, it can provide robust theoretical and technical support for elucidating the interaction mechanisms within the "drug-target-disease" network 22 , 23 . By integrating multi-omics approaches, this study identified CTNNB1 and STAT3 as critical regulatory targets in UM. Celastrol effectively inhibits the proliferation and metastasis of UM cells, promotes apoptosis, and arrests the cell cycle by targeting and inhibiting the CTNNB1 and STAT3 signaling pathways. These findings suggest that celastrol, an active compound derived from traditional Chinese medicine, holds significant potential for the treatment of UM. Methods Data source The potential targets of celastrol were derived from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database ( https://www.tcmsp-e.com/ ), the Swiss Target Prediction database ( http://www.swisstargetprediction.ch/ ), the GeneCards database ( https://www.genecards.org/ ), the HERB database ( http://herb.ac.cn/ ), and the Comparative Toxicogenomics Database (CTD) ( http://ctdbase.org/ ). The potential targets of UM were obtained from the Online Mendelian Inheritance in Man (OMIM) database ( http://www.omim.org/ ), the MalaCards database ( http://www.malacards.org/ ), the Open Targets database ( https://www.opentargets.org/ ), the DrugBank database ( https://go.drugbank.com/ ), and the GeneCards database. Additionally, the transcriptome data were obtained from 80 UM samples archived in The Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) network analysis The STRING database was utilized in this study to construct a PPI network ( https://string-db.org/ ), and the network was visualized and analyzed using Cytoscape software. Subsequently, three algorithms, namely cytoNCA, MCODE, and cytoHubba 24 , were employed to screen and identify the core subnetworks. The specific parameter settings were as follows: in the MCODE algorithm, the degree cutoff was set to 2, the node score cutoff to 0.2, k-core to 2, and the maximum depth to 100; in the cytoNCA algorithm, genes with both Degree and Betweenness greater than their median values were selected for the next round of screening; and in the cytoHubba algorithm, the top 10 genes ranked by the MCC method were selected as core genes. Stromal Score, Immune Score, and Survival Analysis This study utilized the ESTIMATE 25 algorithm to assess the infiltration levels of stromal and immune cells in tumor tissues. A higher abundance of stromal and immune cells corresponded to lower tumor purity, whereas higher tumor purity was associated with reduced infiltration of these cell types. Kaplan-Meier survival analysis 26 was subsequently performed to evaluate the prognostic value of stromal and immune scores. The R packages employed in this study were ESTIMATE (version 1.0.13) and KMsurv (version 0.1-5). All analyses were conducted in the R environment (version 4.4.1) Differential expression and enrichment analysis Differential expression analysis between groups was conducted using the limma R package (v3.62.2), with significant differentially expressed genes selected based on |Log2 Fold Change| >1 and p-value < 0.05. Subsequently, enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) 27 was performed using the clusterProfiler R package (v4.14.4). Finally, the ggplot2 R package (v3.5.1) was utilized to visualize the results, and the aPEAR R package (v 1.0) was used to cluster and visualize the results of the GO analysis. Machine learning The Least Absolute Shrinkage and Selection Operator (LASSO) analysis is widely applied in linear regression and logistic regression models to achieve feature selection and regularization. The Random Forest (RF) model was constructed and analyzed through the "randomForest" function in the randomForest R package (v 4.7–1.2), while the Support Vector Machine (SVM) model was analyzed using the ‘svm’ function in the e1071 R package (v 1.7–16). Gene Set Enrichment Analysis (GSEA) enrichment analysis GSEA systematically evaluated the feature-specific gene rankings from the KEGG database; single-sample gene set variation analysis (ssGSEA) was performed using the clusterProfiler R package 28 . Immune infiltration analysis The CIBERSORT algorithm 29 was used to conduct immune cell infiltration analysis on RNA transcriptome data to assess its correlation with infiltrating immune cells and to compare the differences in immune cell infiltration levels between the TumorScore-high group and the TumorScore-low group. Finally, the Spearman correlation coefficient was utilized to evaluate the correlations among different immune cell types. Analysis was conducted using the CIBERSORT R package (v 0.1.0) ScRNA-seq data analysis In this study, scRNA-seq data of UM were obtained from the GEO database (GSE139829), and the Seurat R package (v 5.2.1) was applied to further analyze the eight primary tumor samples. Quality control criteria were defined as follows: each cell must have at least 200 detected genes and the proportion of mitochondrial gene expression should be below 15%. Subsequently, the Harmony algorithm was utilized to correct for batch effects, followed by cell clustering analysis using the FindClusters function. The clustering results were visualized using UMAP. Finally, cell types were annotated based on the expression profiles of marker genes within each cluster 30 , in conjunction with findings from previously published studies. Molecular docking In this study, AutoDock Vina 4.2 was used for MD analysis and calculation of binding energy. The molecular structure of celastrol was obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ), while the protein structures were sourced from the Protein Data Bank (PDB, https://www.rcsb.org/ ). The specific proteins involved included CTNNB1 (PDB ID: 1JDH) and STAT3 (PDB ID: 6NJS). Finally, the MD results were visualized and subjected to in-depth structural analysis using PyMol Viewer 1.5 and Ligplus software. Molecular dynamics simulation In this study, MDS were performed using GROMACS 2018.8. The initial structural model was derived from the results of preliminary MD analyses. The protein topology was generated based on the AMBER99SB-ILDN force field. For the small molecule celastrol, partial atomic charges (RESP2) were calculated using Multiwfn 31 , and corresponding topological parameters were developed based on the General Amber Force Field (GAFF) with the aid of the sobtop tool. Prior to production simulation, the system underwent 1000 steps of steepest descent energy minimization, followed by 100ps of NVT and NPT equilibration. Subsequently, a 80 ns production simulation was carried out under constant temperature (300 K) and pressure (1.0 bar), with an integration time step of 2fs. Key metrics including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond formation were calculated to assess simulation stability. Additionally, the Gibbs free energy change of the system was estimated using the Boltzmann inversion multidimensional histogram method. Cell culture The human UM cell line C918 and the mouse melanoma cell line B16-F10 were purchased from the China General Microbiological Culture Collection Center and the Kunming Cell Bank of the Chinese Academy of Sciences, respectively. C918 and B16-F10 cells were cultured in Dulbecco's Modified Eagle Medium (DMEM, G4511-500ML, Servicebio, Wuhan, China) supplemented with 10% fetal bovine serum (FBS, 10099-141C, Gibco, California, USA) and 1% penicillin/streptomycin solution (CM0001-100ML, Sparkjade, Jinan, China). Cells were seeded in T25 culture flasks and incubated in a humidified incubator at 37°C with 5% CO 2 . The medium was changed every 2 days; cells were passaged at a ratio of 1:2 when the cell confluence reached approximately 90%. Cell viability assay C918 and B16-F10 cells were seeded into 96-well plates at a density of 6 × 10³ cells per well. After 24 h of culture, different concentrations of celastrol solutions (0, 0.03, 0.3, 1, 3, 10, and 30 µmol/L(µM)) were added to each well. The wells without celastrol were set as the control group, and those without cells and celastrol were set as the blank group. The cell viability was detected at 12 h, 24 h, 36 h, and 48 h after celastrol treatment using the CCK-8 Cell Viability Assay Kit (CT0001-B) produced by Jinan Spike Biotechnology Co., Ltd. During the detection, 10 µL of CCK-8 solution was added to each well and incubated at 37°C for 1 h. The absorbance value (OD value) was then measured at 450 nm using a microplate reader. The cell survival rate was calculated according to the formula: "Cell survival rate = [(Experimental group OD value - Blank group OD value) / (Control group OD value - Blank group OD value)] × 100%. Wound healing assay Approximately 5 × 10 5 B16-F10 cells were seeded into each well of a 6-well plate. When the cell confluence reached 80%-90%, a scratch was introduced by scraping the monolayer perpendicularly with a 200 µL pipette tip, followed by washing with PBS to remove detached cells. The control group received complete medium, while the treatment groups were exposed to varying concentrations of celastrol (0.03, 0.3, 1, 3, and 10 µM). All samples were incubated at 37°C in a 5% CO 2 atmosphere for 48 h. Images of the scratch areas were captured at 0 h, 12 h, 24 h, 36 h, and 48 h using an optical microscope set at 100x magnification. Quantitative analysis of the scratch areas was performed using ImageJ software. The migration rate was calculated using the formula: wound healing area (%) = [(scratch area at 0 h - scratch area at 48 h) / scratch area at 0 h] × 100. Cell cloning B16-F10 and C918 cells were seeded in 6-well plates at a density of 500 cells per well. After 24 h of incubation, various concentrations of celastrol (0, 0.03, 0.3, 1, 3, and 10 µM) were added to each well. The cells were then cultured for an additional 14 days. Cells were fixed using 4% paraformaldehyde (Servicebio, Wuhan, China) and subsequently stained with crystal violet solution (G1014-50ML, Servicebio, Wuhan, China). Finally, images were captured using a Zeiss Axio Vert. A1 system. Cell apoptosis Apoptosis detection was carried out using the Annexin V-FITC Apoptosis Detection Kit (Abs50001, Absin, Shanghai, China). B16-F10 and C918 cells were seeded in 6-well plates at a density of 1 × 10 5 cells/well and incubated until they became fully adherent. Subsequently, each well was treated with 500 µL of celastrol solution at various concentrations (0.03, 0.3, 1, 3, 10, and 30 µM), while the blank control group received an equal volume of complete medium. After 24 h of incubation, the cells were harvested and stained with appropriate fluorescent dyes. Thereafter, the cells were analyzed using a BD FACSVerse flow cytometer. Cell Cycle Cell cycle analysis was conducted using a cell cycle detection kit (C1052, Beyotime, Jiangsu, China) on a BD FACSVerse flow cytometer. The propidium iodide (PI) staining solution was prepared by mixing staining buffer, RNase A (50×), and the PI staining working solution (20×). B16-F10 and C918 cells were seeded in 6-well plates at a density of 1 × 10 6 cells per well and were exposed to varying concentrations of celastrol (0, 0.03, 0.3, 1, 3, and 10 µM). Following 24 h of treatment, the cells were harvested, fixed with ethanol, and subsequently stained with PI. Real-time fluorescence quantitative PCR (qPCR) In this study, total RNA was extracted from cells using the SPARK easy animal tissue/cell RNA extraction kit (AC0202, Sparkjade, Jinan, China). Complementary DNA (cDNA) was synthesized from total RNA using HiScript II Q Select RT SuperMix (Q711-02/03, Vazyme, Nanjing, China). qPCR was performed using the ChamQ Universal SYBR qPCR Master Mix (Q711-02/03, Vazyme, Nanjing, China) under the following thermal cycling conditions: initial pre-denaturation at 95°C for 3 min, followed by 40 cycles of denaturation at 95°C for 12 s, and annealing/extension at 62°C for 40 s. GAPDH mRNA was used as an internal reference gene for normalization of CTNNB1 and STAT3 expression, and the relative expression levels of the target genes were calculated using the 2 −ΔΔCt method. The corresponding primer sequences are listed in Table 1 . Table 1 Primer sequences for target genes Gene Primer sequences Homo-CTNNB1 Forward: 5’-GCTGCAACTAAACAGGAAGGG − 3’ Reverse: 5’-CCCACTTGGCAGACCATCAT − 3’ Homo-STAT3 Forward: 5’-CGAAGGGTACATCATGGGCT-3’ Reverse: 5’-GATCTGGGTCTTACCGCGA-3’ Homo-GAPDH Forward: 5’-GCACCGTCAAGGCTGAGAAC-3’ Reverse: 5’-TGGTGAAGACGCCAGTGGA-3’ Mus-Ctnnb1 Forward: 5’-TTGTAGAAGCTGGTGGGATGC-3’ Reverse: 5’-AGTCGCTGCATCTGAAAGGT-3’ Mus-Stat3 Forward: 5’-ACGAAAGTCAGGTTGCTGGT-3’ Reverse: 5’-GCTGCCGTTGTTAGACTCCT-3’ Mus-GAPDH Forward: 5’-TGTCTCCTGCGACTTCAACA-3’ Reverse: 5’-GGTGGTCCAGGGTTTCTTATC-3’ Western Blot (WB) Protein extraction was performed from collected B16-F10 and C918 cells using the radioimmunoprecipitation assay (RIPA) buffer supplemented with phenylmethylsulfonyl fluoride (PMSF) at a volume ratio of 100:1. The extracted proteins were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and subsequently transferred onto polyvinylidene fluoride (PVDF) membranes. The following primary antibodies were used: β-catenin (1:2000, Proteintech, Hubei, China, 51067-2-AP), STAT3 (1:2000, Proteintech, Hubei, China, 10253-2-AP), and β-actin (1:1000, Servicebio, Wuhan, China, GB12001). Following overnight incubation at 4°C, the PVDF membranes were treated with horseradish peroxidase-conjugated secondary antibodies (goat anti-rabbit IgG, 1:15000, ZSGB-BIO, Beijing, China, ZB-2301; goat anti-mouse IgG, 1:15000, ZSGB-BIO, Beijing, China, ZB-2305) for 1 h at 4°C. Immunoreactive bands were detected using the FUSION-FX7 imaging system (Vilber Lourmat, La Valette-du-Val-d'Oise, France), and densitometric analysis was carried out using ImageJ software (National Institutes of Health, Bethesda, MD, USA). Statistical analysis In this study, GraphPad Prism 9.5 (GraphPad Software, Inc., San Diego, USA) was used for statistical analysis of qPCR and WB experimental data. All experimental results were expressed as mean ± standard deviation (mean ± SD). The differences between groups were evaluated by independent sample t -test. The statistical significance was set at P < 0.05. Results This study encompasses the following aspects: predicting potential targets using network pharmacology, determining core targets through the integration of transcriptomics and machine learning analysis, validating the functions of core targets via immune infiltration analysis and scRNA-seq analysis, confirming the binding stability by combining MD with MDS, and conducting verification through cell-based experiments (Fig. 1 ). Screening potential targets by network pharmacology Through the integration of multiple databases, 368 potential therapeutic targets of triptolide and 477 disease-associated targets related to UM were identified, with 46 overlapping targets common to both datasets. Through Cytoscape software, the study constructed a system network of drug-disease-target interaction (Fig. 2 A) to visually display the potential target association between celastrol and UM. This finding suggests a potential link between celastrol and UM, thereby indicating that celastrol may exert its effects on UM pathophysiology through interactions with the aforementioned target molecules. The study imported 46 target genes into the clusterProfiler package for GO and KEGG enrichment analysis. The results showed that celastrol mainly exerts its effects by regulating biological processes such as programmed cell death. At the molecular function level, celastrol significantly affects RNA polymerase II transcription regulatory region sequence-specific DNA binding (Fig. 2 B). KEGG pathway enrichment analysis indicated that celastrol may affect UM by regulating the JAK-STAT signaling pathway, PI3K-Akt signaling pathway, and Th17 cell differentiation-related pathways (Fig. 2 C). These findings demonstrate that celastrol is closely associated with tumor proliferation and invasion, and exerts its effects on tumor cell survival and apoptosis through the regulation of programmed cell death. Subsequently, the 46 target genes were imported into the STRING database to construct a PPI network. The constructed network diagram contained 44 nodes and 533 edges. The network was analyzed topologically using the cytoNCA, MCODE, and cytoHubba plugins in Cytoscape (Fig. 2 D-F), and ultimately, the common targets in the three analysis results were selected as core targets—namely STAT3, CTNNB1, MYC, EGFR, TP53, BCL2, PTEN, AKT1, CCND1, and MTOR (Fig. 1 G). These targets may be the key regulatory nodes through which celastrol exerts its anti-tumor effects, thereby providing a theoretical basis and potential direction for the development of treatment strategies targeting UM based on these targets in the future. Transcriptome analysis identified significantly differentially expressed genes Using the ESTIMATE algorithm, the study calculated the stromal score, immune score, ESTIMATE score, and tumor score for each sample. Survival analysis revealed that stratifying samples into high- and low-score groups based on these metrics showed a statistically significant association with the overall survival time of clinical patients. Specifically, higher stromal score, immune score, and ESTIMATE score were correlated with poorer prognosis in terms of survival (Fig. 3 A). This result provides a basis for understanding the role of the tumor microenvironment (such as stromal components and immune infiltration) in disease progression, and also offers a reference for the subsequent development of treatment strategies from the perspective of the microenvironment. Based on this, the study used the tumorScore to further divide the samples into TumorScore-high and TumorScore-low groups for subsequent analysis. Differential expression analysis revealed significant differentially expressed genes between the two groups (Fig. 3 B), with the TumorScore-high group showing more high-expression genes compared to the TumorScore-low group. GO/KEGG functional enrichment analysis of these high-expression genes revealed that the GO analysis results indicated that these genes were mainly enriched in biological processes such as cell development, cell differentiation, and cell signaling (Fig. 3 C), while the KEGG pathway analysis revealed that they were mainly involved in cell adhesion molecules, chemokine signaling pathways, and phototransduction pathways (Fig. 3 D). These findings are highly consistent with the hallmark features of cancer cells, including enhanced proliferative capacity and invasive potential, thereby further supporting the validity of tumor score-based stratification. The study conducted machine learning analysis on the previously selected 46 genes. Lasso regression analysis selected 14 key genes (Fig. 3 E), SVM analysis identified 7 key genes (Fig. 3 F), and RF analysis chose 65 as the optimal number of features and selected the top 10 genes as key genes (Fig. 3 G). Ultimately, the study determined four core genes, CTNNB1, SMAD3, STAT3 and TLR7, by taking the intersection of the key genes from the three machine learning methods (Fig. 3 H). Among them, the CTNNB1 and STAT3 genes were identified again in the screening, which further verified their roles as key regulatory hubs affected by celastrol in UM. Single-gene GSEA analysis and its correlation with immune infiltration This study utilized the CIBERSORT algorithm to assess immune cell infiltration in UM and revealed high proportions of T cells, NK cells, and macrophages (Fig. 4 A). This observation was consistent with the cell types identified through subsequent single-cell clustering analysis. Further comparative analysis demonstrated significant differences in immune infiltration profiles between the TumorScore-high and TumorScore-low groups, with the TumorScore-low group exhibiting higher infiltration scores of CD8 T cells (Fig. 4 B). Correlation heatmap analysis uncovered complex interactions among various immune cell populations, subsequently revealing a strong positive correlation between T cells and macrophages (Fig. 4 C). The lollipop plot further illustrated the associations between key genes and specific immune cell types, showing a significant positive correlation between STAT3 and T cells. This suggested a potential role of STAT3 in promoting T cell infiltration into the tumor microenvironment. In contrast, CTNNB1 exhibited a negative correlation with CD8 T cells, which implied its involvement in immune evasion by suppressing CD8 T cell infiltration (Fig. 4 D). These findings support the hypothesis that CTNNB1 may facilitate tumor initiation and progression by establishing an immunosuppressive tumor microenvironment. Functional enrichment analysis revealed that the key genes CTNNB1 and STAT3 were significantly associated with multiple biological processes. Specifically, CTNNB1 was predominantly enriched in autophagy and mTOR signaling pathways, thereby highlighting its role in regulating cell metabolism and proliferation. Meanwhile, STAT3 was closely linked to NOD-like receptor signaling, endoplasmic reticulum protein processing, and ubiquitin-mediated proteolysis, thereby suggesting its involvement in cellular stress responses and inflammatory signaling during UM progression (Fig. 4 E). Collectively, these findings provide a solid theoretical and functional basis for targeting CTNNB1 and STAT3 in the development of precision therapeutic strategies for UM. ScRNA-seq analysis reveals expression heterogeneity and functional divergence of CTNNB1 and STAT3 in UM tumor cells This study employed scRNA-seq analysis profiling to further elucidate the expression patterns and functional divergence of CTNNB1 and STAT3 across distinct UM cell populations and to characterize cellular heterogeneity within UM tissues while assessing the expression dynamics of CTNNB1 and STAT3. Through clustering analysis at a resolution of 0.1, 11 distinct cell clusters were identified and further annotated into four major cell types based on canonical marker gene expression: tumor cells (marked by MLANA and CITED1), T cells (marked by CD8A and GZMA), monocytes/macrophages (marked by C1QA and C1QB), and fibroblasts (marked by COL1A1 and COL4A1) (Fig. 5 A and 5 B). Notably, CTNNB1 and STAT3 exhibited predominant expression in tumor cell clusters, yet substantial intercellular heterogeneity in their expression levels was observed (Fig. 5 C). Based on CTNNB1 and STAT3 expression profiles, tumor cells were stratified into a high-expression subgroup (Exp-high) and a low-expression subgroup (Exp-low) (Fig. 5 D), with the validity of subgroup classification confirmed through expression visualization (Fig. 5 E). Comparative transcriptomic analysis revealed a significantly higher number of upregulated genes in the Exp-high group compared to the Exp-low group (Fig. 5 F). Functional enrichment analysis using GO demonstrated that the Exp-high group was predominantly enriched in developmental processes, including multicellular organism development, cell differentiation, and cell development, whereas the Exp-low group showed enrichment in immune-related processes such as humoral immune response, granulocyte chemotaxis, and leukocyte chemotaxis (Fig. 5 G). KEGG pathway analysis further indicated that the Exp-high group was enriched in oncogenic signaling pathways, including PI3K-Akt, Wnt, and JAK-STAT, which align closely with the core regulatory network identified in this study. In contrast, the Exp-low group was enriched in inflammation-associated pathways, such as NF-κB and IL-17 signaling pathways (Fig. 5 G). Collectively, these findings highlight CTNNB1 and STAT3 as key regulatory nodes in UM tumor cells, where their expression heterogeneity contributes to functional divergence among tumor subpopulations. Specifically, high expression of CTNNB1 and STAT3 promotes tumor cell proliferation and malignant progression, whereas low expression is associated with immune microenvironment modulation via inflammatory signaling. These insights provide a mechanistic and theoretical foundation for targeting CTNNB1 and STAT3 in the development of precision therapeutic strategies for UM. MD results provide compelling evidence for structural stability of the complex The results indicate that celastrol binds to both CTNNB1 and STAT3 with high affinity, and exhibiting binding energies of -7.5 kcal/mol and − 8.4 kcal/mol, respectively. The planar and 3D interaction diagrams illustrate the key intermolecular forces involved. In the CTNNB1–celastrol complex, the amino acid residues His410 and Glu500 form three conventional hydrogen bonds with celastrol, thereby contributing to binding stabilization through hydrophobic effects (Fig. 6 A). In the STAT3–celastrol complex, Asp199 forms one conventional hydrogen bond with celastrol, with stability further maintained by hydrophobic interactions (Fig. 6 B). This result demonstrates that celastrol exhibits strong binding affinity toward CTNNB1 and STAT3, which further supports the underlying mechanism by which celastrol inhibits UM through interaction with these two proteins. MDS demonstrates that the complex exhibits a high degree of structural stability From the perspective of overall conformation, both complexes reached a stable state after 10 ns of simulation, as evidenced by minimal RMSD fluctuations (Fig. 6 C). At the residue level, CTNNB1 residues 300–400 (Fig. 6 D) and STAT3 residues 0–100 and 300–400 displayed notable flexibility (Fig. 6 E), thereby suggesting their involvement in key functional regions. In terms of structural compactness, the Rg for the CTNNB1–celastrol complex stabilized after 0–80 ns of simulation, whereas the STAT3–celastrol complex reached stability after 50 ns (Fig. 6 F). Regarding hydrogen bonding interactions, CTNNB1 consistently formed one hydrogen bond with celastrol (with a maximum of four observed) (Fig. 6 G), while STAT3 maintained a stable interaction of 2–3 hydrogen bonds with celastrol (Fig. 6 H), thereby contributing to the overall stability of the complexes. Additionally, the SASA decreased and remained stable in both systems, thus, indicating increased structural compactness (Fig. 6 I, J). Gibbs free energy analysis revealed that the CTNNB1–celastrol complex achieved optimal stability at Rg = 3.5 nm and RMSD = 0.54 nm, while the STAT3–celastrol complex reached its most stable state at Rg = 3.6 nm and RMSD = 0.65 nm (Fig. 6 K). These findings provide strong structural evidence that celastrol interacts specifically with CTNNB1 and STAT3 to exert its biological functions, and further supports its potential as a promising ligand targeting these two proteins. Celastrol inhibits proliferation and migration of B16-F10 and C918 cells To explore the effect of celastrol on the viability of UM cells, B16-F10 cells were treated with various concentrations of celastrol (0, 0.03, 0.3, 1, 3, and 10 µM) according to relevant literature 18 , 32 , while C918 cells were exposed to a broader range of concentrations (0, 0.3, 1, 3, 10, and 30 µM). Cell viability was evaluated using the CCK-8 assay at 12, 24, 36, and 48 h. The results demonstrated that celastrol significantly reduced the viability of both B16-F10 and C918 cells in a time- and dose-dependent manner. Notably, B16-F10 cells exhibited marked cell death at 10 µM (Fig. 7 A), whereas C918 cells showed significant cytotoxicity at a lower concentration of 3 µM (Fig. 8 A). The therapeutic effects on both cell lines were most pronounced following 24 h of treatment. Accordingly, in subsequent experiments, the drug treatment regimen for B16-F10 cells was established as 10 µM for 24 h, whereas a concentration of 3 µM for 24 h was selected for C918 cells. Colony formation assays were conducted to further validate the inhibitory effects of celastrol on both cell lines. The number of colonies formed was quantified following 14 days of treatment with varying concentrations of celastrol. The data revealed that celastrol significantly suppressed colony formation in a concentration-dependent manner. Specifically, 10 µM celastrol exerted a strong cytotoxic effect on B16-F10 cells (Fig. 7 B), while 3 µM celastrol induced substantial toxicity in C918 cells (Fig. 8 B). Scratch wound healing assays were performed to assess the impact of celastrol on cell migration. Microscopic images were captured at multiple time points to evaluate the healing process under different treatment conditions. The results showed that 10 µM celastrol had the most pronounced inhibitory effect on the migration of B16-F10 cells (Fig. 7 C, D), whereas 3 µM celastrol exhibited the strongest suppression of C918 cell migration (Fig. 8 C, D). Collectively, these findings indicate that celastrol effectively inhibits both the proliferation and migration of B16-F10 and C918 cells in a time- and dose-dependent manner. Celastrol induces apoptosis and cell cycle arrest in B16-F10 and C918 cells B16-F10 and C918 cells were exposed to varying concentrations of celastrol for 24 h to explore the effects of celastrol on apoptosis and cell cycle of UM cells. Apoptosis rates and cell cycle distribution were subsequently analyzed using flow cytometry. The results demonstrated that celastrol significantly increased the apoptosis rate in both cell lines in a dose-dependent manner (Figs. 7 E, F; 8 E, F). Furthermore, celastrol induced distinct cell cycle arrest patterns in each cell type: in B16-F10 cells, the proportion of cells in the G1 and S phases increased, while the G2/M phase population decreased (Figs. 9 A, B); in C918 cells, the G1 phase population decreased, the G2/M phase population increased, and the S phase population remained relatively unchanged (Figs. 9 C, D). Collectively, these findings indicate that celastrol not only promotes apoptosis in B16-F10 and C918 cells, but also exerts anti-tumor effects by modulating cell cycle progression. Celastrol suppresses mRNA expression and protein levels of core genes in B16-F10 and C918 cells B16-F10 and C918 cells were treated with 10 µM and 3 µM celastrol, respectively, for 24 h to investigate the impact of celastrol on key molecular targets. Total cellular RNA and protein were extracted, and their expression levels were analyzed by qPCR and WB. The results demonstrated that celastrol significantly downregulated the mRNA expression of STAT3 and CTNNB1 in both cell lines (Fig. 10 A, C), and markedly reduced the protein levels of STAT3 and β-catenin (Fig. 10 B, D). The experimental results fully demonstrate that celastrol significantly inhibits the proliferation and metastasis of UM cells by targeting CTNNB1 and STAT3, while promoting apoptosis and inducing cell cycle arrest. Discussion UM arises from uveal melanocytes and predominantly affects Caucasians, with 90% of cases involving the choroid 33 . Epidemiological data indicate that the median age at diagnosis for UM is 62 years 34 , 35 . Traditional Chinese medicine (TCM), as an integral part of traditional medical systems 36 , has garnered significant attention in oncology due to its comprehensive theoretical framework and relatively mild side effects 37 . Celastrol, an active compound derived from TCM, has demonstrated potent inhibitory effects against various malignant tumors 38 , 39 . This study systematically investigated the potential mechanisms of celastrol in UM using a multi-level approach that integrated network pharmacology, transcriptomic analysis, scRNA-seq, MD, and MDS, as well as cell-based experiments. This study was the first to screen the relevant targets of celastrol against UM, such as TP53, BCL2, and MYC, which are closely related to the occurrence and development of UM 40,41 . Furthermore, the core regulatory targets CTNNB1 and STAT3 were further screened out, thereby providing a novel theoretical basis and potential therapeutic strategies for precision treatment of UM. Previous studies have confirmed that celastrol can inhibit the proliferation of B16-F10 melanoma cells by regulating the PI3K/AKT/mTOR signaling pathway 18 . To our knowledge, this study is the first to link the anti- UM effect of celastrol to its key molecular targets, CTNNB1 and STAT3. CTNNB1 is a central regulatory component of the Wnt signaling pathway and its inhibition effectively blocks the aberrant proliferation signals in tumor cells 42 . Meanwhile, STAT3 functions as a core effector in the JAK-STAT pathway and its downregulation not only diminishes the survival advantage of tumor cells 43 , but may also enhance anti-tumor immunity by alleviating the suppression of CD8⁺T cells. This “dual regulatory” mechanism exemplifies the multi-target advantages of natural compounds in cancer therapy. Based on the ESTIMATE algorithm, this study analyzed the UM transcriptome and stratified tumor samples according to TumorScore, thereby identifying differentially expressed genes and revealing a potential link between the tumor microenvironment and clinical prognosis. Subsequently, three machine learning algorithms—Lasso regression, SVM, and RF—were applied to cross validate 46 potential targets, ultimately identifying CTNNB1 and STAT3 as core regulatory nodes. This quantitative modeling approach filtered out low-impact targets, thereby highlighting the central roles of CTNNB1 and STAT3 in the UM regulatory network. ScRNA-seq revealed heterogeneity in CTNNB1 and STAT3 expression, thereby indicating the presence of functionally distinct tumor subpopulations in UM. The high-expression subpopulation relies on classical oncogenic pathways to sustain its malignant phenotype, whereas the low-expression subpopulation may evade immune surveillance by remodeling the tumor immune microenvironment. These findings provide a solid theoretical foundation for the “stratified treatment” strategy in UM. Furthermore, this study used MD and MDS to confirm, for the first time, that celastrol specifically binds to and inhibits CTNNB1 and STAT3. In vitro experiments further validated its inhibitory and cytotoxic effects on B16-F10 and C918 cells. These findings not only identify novel molecular targets and therapeutic strategies for the precise treatment of UM, but also open new avenues for the clinical translation of natural compounds. However, this study also had several limitations. First, the absence of animal model experiments limits the ability to fully recapitulate the effects of celastrol within a complex in vivo microenvironment. Second, the reciprocal regulatory mechanisms between CTNNB1 and STAT3, as well as their synergistic interactions with other key molecular targets, remain to be further elucidated. Future studies should include the development of UM animal models to validate the in vivo antitumor efficacy of celastrol and its modulation of the immune microenvironment. Additionally, gene knockout or overexpression approaches should be employed to define the specific roles of CTNNB1 and STAT3 in the molecular mechanisms of celastrol action. Finally, combination strategies involving celastrol and immune checkpoint inhibitors should be explored to enhance antitumor immune responses. Conclusion This study demonstrates that celastrol exerts potent anti-tumor effects on UM cells through dual targeting of CTNNB1 and STAT3. It not only directly suppresses cell proliferation and migration while inducing apoptosis, but also modulates immune infiltration within the tumor microenvironment. As key regulatory molecules in UM, the expression heterogeneity of CTNNB1 and STAT3 offers a molecular basis for disease stratification and personalized therapeutic approaches. These findings establish a theoretical foundation for the development of celastrol as a promising therapeutic agent for UM, and provide novel insights into combination therapies that simultaneously target tumor cells and the surrounding microenvironment. Declarations Author Contributions: Conceptualization, P.Z.; methodology, ZL.L.; formal analysis, validation, software, ZL.L.; investigation, RF.X.; resources, P.Z.; data curation, ZL.L.; writing—original draft preparation, ZL.L.; writing—review and editing, P.Z., XD.H.; visualization, ZL.L., RF.X.; supervision, P.Z.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Natural Science Foundation of China Youth Science Foundation (No. 82205198), China Postdoctoral Science Foundation Project (No. 2022M711984), Shandong Young Science and Technology Talent Support Program (SDAST2025 QTB049). Shandong Provincial Traditional Chinese Medicine Science and Technology Project (NO. M20242003). Clinical Special Project of Shandong University of Traditional Chinese Medicine (LCKY202434) Informed Consent Statement: Not applicable. Data Availability Statement: The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Should any raw data files be needed in another format they are available from the first author Zhanglong Li ( [email protected] ) upon reasonable request. Source data are provided with this paper. Conflicts of Interest: The authors declare no conflicts of interest. References Rossi, E. et al. Uveal Melanoma Metastasis. 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1","display":"","copyAsset":false,"role":"figure","size":3865777,"visible":true,"origin":"","legend":"\u003cp\u003eThis study outlines the comprehensive technical framework employed to investigate the molecular mechanisms underlying the anti- UM effects of celastrol.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/dd89b2ba682563edbec0be9c.png"},{"id":97136406,"identity":"a7ee0b56-f417-458b-bc7d-6a62cba77542","added_by":"auto","created_at":"2025-12-01 09:56:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4919164,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the potential targets of celastrol in inhibiting UM as predicted by network pharmacology. A: Drug-target-disease interaction network, where purple nodes represent celastrol, green nodes represent UM, and orange nodes indicate the common target genes shared by the drug and the disease. B: GO enrichment analysis network of the common target genes. C: KEGG enrichment analysis\u003csup\u003e44–46\u003c/sup\u003e network of the common target genes. D: Network constructed using the cytoNCA algorithm to identify core target genes. E: Network constructed using the MCODE algorithm to identify core target genes. F: Network constructed using the cytoHubba algorithm to identify core target genes. G: Venn diagram showing the core target genes commonly identified by the three algorithms.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/803dce852fcfc521cf1d633f.png"},{"id":96939467,"identity":"033fda19-ef48-401d-8a73-d7e181e954d4","added_by":"auto","created_at":"2025-11-27 17:30:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2246306,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of core targets from transcriptomic data using machine learning. A. Transcriptional samples were stratified based on Estimate scores, and survival curves were generated to assess prognostic differences between groups. B. Volcano plot illustrating differential gene expression between groups; orange dots represent genes upregulated in the TumorScore-high group compared to the TumorScore-low group, while green dots represent downregulated genes. C. GO enrichment analysis was performed on genes highly expressed in the TumorScore-high group, with results visualized using a bar chart. D. KEGG pathway enrichment analysis\u003csup\u003e44–46\u003c/sup\u003e was performed on genes highly expressed in the TumorScore-high group, also displayed in a bar chart. E. The LASSO logistic regression algorithm, combined with ten-fold cross-validation, identified 14 potential core genes. F. The SVM algorithm was subsequently applied for further filtering, resulting in the selection of 7 core genes. G. Using the RF algorithm, 10 core genes with importance scores greater than 1.0 were selected. H. Venn diagram showing the intersection of core genes identified by all three machine learning approaches.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/706ccae77a242050bde8773e.png"},{"id":97136660,"identity":"951969b9-2fe2-40e0-8c70-a4e4c27117bd","added_by":"auto","created_at":"2025-12-01 09:56:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1575754,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of key genes and immune infiltration cell composition in the TCGA dataset. A. Distribution of immune infiltration cell proportions. B. Box plot comparison of immune cell proportions between TumorScore-high and TumorScore-low groups (*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). C. Heatmap of correlations among 22 types of immune cells. D. Lollipop plot of correlations between key genes and 22 types of immune infiltration cells. E. Results of GSEA enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/25d64d35499d49f4f706a856.png"},{"id":96939472,"identity":"3946d7de-69d4-42e3-a026-e51277d5c75d","added_by":"auto","created_at":"2025-11-27 17:30:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2913620,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression heterogeneity and functional differentiation of core targets in UM tumor cells. A. UMAP plot depicting the distribution of four identified cell types. B. Bubble plot illustrating the expression of characteristic marker genes for each cell type. C. UMAP plot showing CTNNB1 and STAT3 expression levels across different cell types. D. UMAP plot representing grouped tumor cell clusters. E. UMAP plot displaying CTNNB1 and STAT3 expression levels among distinct groups. F. Volcano plot comparing differentially expressed genes between Exp-high and Exp-low groups. G. Bar chart presenting GO enrichment analysis results of differentially expressed genes. H. Bar chart presenting KEGG enrichment analysis\u003csup\u003e44–46\u003c/sup\u003e results of differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/783632b0bbdd399d0037bfa5.png"},{"id":96939470,"identity":"c49cc1d2-d6ce-4803-9601-a46616af2ef4","added_by":"auto","created_at":"2025-11-27 17:30:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2306480,"visible":true,"origin":"","legend":"\u003cp\u003eMD and MDS results of celastrol with core proteins. A. Two-dimensional and three-dimensional schematic diagrams of celastrol MD with β-catenin. B. Two-dimensional and three-dimensional schematic diagrams of celastrol MD with STAT3. C. RMSD profiles of the β-catenin–celastrol and STAT3–celastrol complexes. D. RMSF distribution of the β-catenin–celastrol complex. E. RMSF distribution of the STAT3–celastrol complex. F. Rgprofiles of the β-catenin–celastrol and STAT3–celastrol complexes. G. Dynamic evolution of hydrogen bond numbers in the β-catenin–celastrol complex. H. Dynamic evolution of hydrogen bond numbers in the STAT3–celastrol complex. I. SASA profiles of the β-catenin–celastrol complex. J. SASA profiles of the STAT3–celastrol complex. K. Gibbs free energy landscape of the β-catenin–celastrol and STAT3–celastrol complexes. L. Post-MDS interaction analysis between β-catenin and celastrol. M. Post-MDS interaction analysis between STAT3 and celastrol.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/e8e39be71d83b90c8b9ba4a6.png"},{"id":96939477,"identity":"4762a6df-3192-4940-8f99-fdc2ab2629df","added_by":"auto","created_at":"2025-11-27 17:30:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3845214,"visible":true,"origin":"","legend":"\u003cp\u003eCelastrol inhibits proliferation and migration of murine B16-F10 cells and induces cell apoptosis. A. Cell viability of B16-F10 cells treated with varying concentrations of celastrol over different time points, as shown by line graphs (n = 4). B. Colony formation assay of B16-F10 cells following 14-day treatment with different concentrations of celastrol. C. Wound healing assay showing the migration capacity of B16-F10 cells treated with different concentrations of celastrol at various time intervals. D. Quantitative analysis of wound area (n = 3). E. Flow cytometry analysis of apoptosis in B16-F10 cells treated with celastrol. F. Statistical summary of dead cell percentage (n = 5). All data are presented as Mean ± SD, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001, compared with the control group.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/090ed783adda8db1b040392e.png"},{"id":97136553,"identity":"f8825ab6-3c15-4d0e-b32a-71a581372ee0","added_by":"auto","created_at":"2025-12-01 09:56:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4501200,"visible":true,"origin":"","legend":"\u003cp\u003eCelastrol inhibits proliferation and migration of human C918 cells and induces cell apoptosis. A. Cell viability of C918 cells treated with varying concentrations of celastrol over different time periods, presented as line graphs (n = 4). B. Colony formation assay of C918 cells following 14-day treatment with different concentrations of celastrol. C. Wound healing assay assessing the migration capacity of C918 cells treated with different concentrations of celastrol at various time intervals. D. Quantitative analysis of wound area (n = 3). E. Flow cytometry analysis of apoptosis in C918 cells treated with celastrol. F. Statistical summary of dead cell percentage (n = 5). All data are presented as Mean ± SD, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001, compared with the control group.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/b9547ca2c07df9061e1d2349.png"},{"id":96939476,"identity":"264425d1-7ad8-4fea-bbfe-052d3c9b0dd2","added_by":"auto","created_at":"2025-11-27 17:30:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":947457,"visible":true,"origin":"","legend":"\u003cp\u003eCelastrol induces cell cycle arrest in both B16-F10 and C918 cells. A. Flow cytometry analysis of the effect of varying concentrations of celastrol on the B16-F10 cell cycle. B. Quantitative distribution of B16-F10 cells in each phase of the cell cycle (n = 5). C. Flow cytometry analysis of the effect of varying concentrations of celastrol on the C918 cell cycle. D. Quantitative distribution of C918 cells in each phase of the cell cycle (n = 5). All data are presented as Mean ± SD, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001, compared with the control group.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/1b2536b5afed0a22c3f332af.png"},{"id":96939479,"identity":"5c0c484e-baf1-42dd-9847-91c75e90475b","added_by":"auto","created_at":"2025-11-27 17:30:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":512465,"visible":true,"origin":"","legend":"\u003cp\u003emRNA expression levels of CTNNB1 and STAT3, along with protein quantification, following 24-hour celastrol treatment in B16-F10 and C918 cells are presented below. A. mRNA expression levels of CTNNB1 and STAT3 in B16-F10 cells treated with 10 μmol/L celastrol (n = 3). B. Quantification of β-catenin and STAT3 proteins in B16-F10 cells treated with 10 μmol/L celastrol (n = 4). C. mRNA expression levels of CTNNB1 and STAT3 in C918 cells treated with 3 μmol/L celastrol (n = 3). D. Quantification of β-catenin and STAT3 proteins in C918 cells treated with 3 μmol/L celastrol (n = 5). All data are presented as Mean ± SD, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/a85505fe15d22f10fbfeef4f.png"},{"id":101151762,"identity":"2ea9dca5-b877-4f33-b612-0bfb9e266285","added_by":"auto","created_at":"2026-01-26 16:04:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27745118,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/bb26ab04-237a-4c0c-af92-bc668b17026d.pdf"},{"id":96939473,"identity":"217b7878-f902-4349-a37f-f88bb7f4c5d8","added_by":"auto","created_at":"2025-11-27 17:30:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1321135,"visible":true,"origin":"","legend":"","description":"","filename":"OriginalWesternBlotimage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7716555/v1/b5110b786220ed18a7797801.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the molecular mechanism of celastrol targeting CTNNB1/STAT3 to inhibit uveal melanoma based on network pharmacology and multi-omics analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUveal melanoma (UM), also referred to as choroidal melanoma, is a highly aggressive and metastatic malignant tumor \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e that represents the most common primary intraocular malignancy in adults \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Approximately 50% of patients develop hematogenous metastasis, with the median survival time after metastasis ranging from 10 to 13 months, and the overall cure rate remains at zero \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The primary treatment methods for UM include surgery, plaque brachytherapy, phototherapy, and charged particle radiotherapy (CPT) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, chemotherapy regimens and molecularly targeted therapies exhibit limited therapeutic efficacy and are associated with notable adverse effects in the treatment of these cases \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The occurrence and progression of UM represent a complex, multistage process influenced by multiple regulatory factors. In addition to GNAQ/GNA11 \u003csup\u003e2\u003c/sup\u003e genetic mutations being widely recognized as the initiating events in tumor formation and BAP1 \u003csup\u003e6\u003c/sup\u003e mutations serving as key drivers of metastasis, the aberrant activation of several critical signaling pathways such as PI3K/AKT/mTOR, Wnt/β-catenin, and JAK/STAT3 \u003csup\u003e7\u0026ndash;11\u003c/sup\u003e also plays a central role in the development of the malignant UM phenotype.\u003c/p\u003e\u003cp\u003eCelastrol is an active component in the traditional Chinese medicinal plant \u003cem\u003eTripterygium wilfordii\u003c/em\u003e Hook.f. It has been regarded as one of the most important traditional drug compounds in modern drug development \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e due to its significant therapeutic potential in various types of cancer \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Studies have shown that celastrol can inhibit key biological behaviors of tumor cells, including proliferation and migration, through multiple molecular pathways \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Celastrol exerts broad anti-cancer effects through the modulation of multiple signaling pathways, including NF-κB, PRDX, and JAK/STAT3 \u003csup\u003e15\u0026ndash;17\u003c/sup\u003e. Notably, its inhibitory impact on the PI3K/AKT/mTOR pathway has been validated as a promising therapeutic target in melanoma treatment \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, limitations persist in the current research, and the exact molecular mechanism of celastrol's effect on UM needs to be further explored.\u003c/p\u003e\u003cp\u003eNetwork pharmacology, integrated with machine learning and single-cell RNA sequencing (scRNA-seq) technology, enables the precise identification of disease-associated core targets \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, when combined with molecular docking (MD) and molecular dynamics simulation (MDS), this approach can effectively validate the binding stability between drug candidates and target proteins \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Consequently, it can provide robust theoretical and technical support for elucidating the interaction mechanisms within the \"drug-target-disease\" network \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. By integrating multi-omics approaches, this study identified CTNNB1 and STAT3 as critical regulatory targets in UM. Celastrol effectively inhibits the proliferation and metastasis of UM cells, promotes apoptosis, and arrests the cell cycle by targeting and inhibiting the CTNNB1 and STAT3 signaling pathways. These findings suggest that celastrol, an active compound derived from traditional Chinese medicine, holds significant potential for the treatment of UM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source\u003c/h2\u003e\u003cp\u003eThe potential targets of celastrol were derived from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tcmsp-e.com/\u003c/span\u003e\u003cspan address=\"https://www.tcmsp-e.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Swiss Target Prediction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the HERB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://herb.ac.cn/\u003c/span\u003e\u003cspan address=\"http://herb.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Comparative Toxicogenomics Database (CTD) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003cspan address=\"http://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The potential targets of UM were obtained from the Online Mendelian Inheritance in Man (OMIM) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.omim.org/\u003c/span\u003e\u003cspan address=\"http://www.omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the MalaCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.malacards.org/\u003c/span\u003e\u003cspan address=\"http://www.malacards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Open Targets database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.opentargets.org/\u003c/span\u003e\u003cspan address=\"https://www.opentargets.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the GeneCards database. Additionally, the transcriptome data were obtained from 80 UM samples archived in The Cancer Genome Atlas (TCGA) database.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProtein-protein interaction (PPI) network analysis\u003c/h3\u003e\n\u003cp\u003eThe STRING database was utilized in this study to construct a PPI network (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the network was visualized and analyzed using Cytoscape software. Subsequently, three algorithms, namely cytoNCA, MCODE, and cytoHubba \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, were employed to screen and identify the core subnetworks. The specific parameter settings were as follows: in the MCODE algorithm, the degree cutoff was set to 2, the node score cutoff to 0.2, k-core to 2, and the maximum depth to 100; in the cytoNCA algorithm, genes with both Degree and Betweenness greater than their median values were selected for the next round of screening; and in the cytoHubba algorithm, the top 10 genes ranked by the MCC method were selected as core genes.\u003c/p\u003e\n\u003ch3\u003eStromal Score, Immune Score, and Survival Analysis\u003c/h3\u003e\n\u003cp\u003eThis study utilized the ESTIMATE \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e algorithm to assess the infiltration levels of stromal and immune cells in tumor tissues. A higher abundance of stromal and immune cells corresponded to lower tumor purity, whereas higher tumor purity was associated with reduced infiltration of these cell types. Kaplan-Meier survival analysis \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e was subsequently performed to evaluate the prognostic value of stromal and immune scores. The R packages employed in this study were ESTIMATE (version 1.0.13) and KMsurv (version 0.1-5). All analyses were conducted in the R environment (version 4.4.1)\u003c/p\u003e\n\u003ch3\u003eDifferential expression and enrichment analysis\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis between groups was conducted using the limma R package (v3.62.2), with significant differentially expressed genes selected based on |Log2 Fold Change| \u0026gt;1 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Subsequently, enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e was performed using the clusterProfiler R package (v4.14.4). Finally, the ggplot2 R package (v3.5.1) was utilized to visualize the results, and the aPEAR R package (v 1.0) was used to cluster and visualize the results of the GO analysis.\u003c/p\u003e\n\u003ch3\u003eMachine learning\u003c/h3\u003e\n\u003cp\u003eThe Least Absolute Shrinkage and Selection Operator (LASSO) analysis is widely applied in linear regression and logistic regression models to achieve feature selection and regularization. The Random Forest (RF) model was constructed and analyzed through the \"randomForest\" function in the randomForest R package (v 4.7\u0026ndash;1.2), while the Support Vector Machine (SVM) model was analyzed using the \u0026lsquo;svm\u0026rsquo; function in the e1071 R package (v 1.7\u0026ndash;16).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGene Set Enrichment Analysis (GSEA) enrichment analysis\u003c/h2\u003e\u003cp\u003eGSEA systematically evaluated the feature-specific gene rankings from the KEGG database; single-sample gene set variation analysis (ssGSEA) was performed using the clusterProfiler R package \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImmune infiltration analysis\u003c/h3\u003e\n\u003cp\u003eThe CIBERSORT algorithm \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e was used to conduct immune cell infiltration analysis on RNA transcriptome data to assess its correlation with infiltrating immune cells and to compare the differences in immune cell infiltration levels between the TumorScore-high group and the TumorScore-low group. Finally, the Spearman correlation coefficient was utilized to evaluate the correlations among different immune cell types. Analysis was conducted using the CIBERSORT R package (v 0.1.0)\u003c/p\u003e\n\u003ch3\u003eScRNA-seq data analysis\u003c/h3\u003e\n\u003cp\u003eIn this study, scRNA-seq data of UM were obtained from the GEO database (GSE139829), and the Seurat R package (v 5.2.1) was applied to further analyze the eight primary tumor samples. Quality control criteria were defined as follows: each cell must have at least 200 detected genes and the proportion of mitochondrial gene expression should be below 15%. Subsequently, the Harmony algorithm was utilized to correct for batch effects, followed by cell clustering analysis using the FindClusters function. The clustering results were visualized using UMAP. Finally, cell types were annotated based on the expression profiles of marker genes within each cluster \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, in conjunction with findings from previously published studies.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMolecular docking\u003c/h2\u003e\u003cp\u003eIn this study, AutoDock Vina 4.2 was used for MD analysis and calculation of binding energy. The molecular structure of celastrol was obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the protein structures were sourced from the Protein Data Bank (PDB, \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). The specific proteins involved included CTNNB1 (PDB ID: 1JDH) and STAT3 (PDB ID: 6NJS). Finally, the MD results were visualized and subjected to in-depth structural analysis using PyMol Viewer 1.5 and Ligplus software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMolecular dynamics simulation\u003c/h2\u003e\u003cp\u003eIn this study, MDS were performed using GROMACS 2018.8. The initial structural model was derived from the results of preliminary MD analyses. The protein topology was generated based on the AMBER99SB-ILDN force field. For the small molecule celastrol, partial atomic charges (RESP2) were calculated using Multiwfn \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and corresponding topological parameters were developed based on the General Amber Force Field (GAFF) with the aid of the sobtop tool. Prior to production simulation, the system underwent 1000 steps of steepest descent energy minimization, followed by 100ps of NVT and NPT equilibration. Subsequently, a 80 ns production simulation was carried out under constant temperature (300 K) and pressure (1.0 bar), with an integration time step of 2fs. Key metrics including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond formation were calculated to assess simulation stability. Additionally, the Gibbs free energy change of the system was estimated using the Boltzmann inversion multidimensional histogram method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCell culture\u003c/h2\u003e\u003cp\u003eThe human UM cell line C918 and the mouse melanoma cell line B16-F10 were purchased from the China General Microbiological Culture Collection Center and the Kunming Cell Bank of the Chinese Academy of Sciences, respectively. C918 and B16-F10 cells were cultured in Dulbecco's Modified Eagle Medium (DMEM, G4511-500ML, Servicebio, Wuhan, China) supplemented with 10% fetal bovine serum (FBS, 10099-141C, Gibco, California, USA) and 1% penicillin/streptomycin solution (CM0001-100ML, Sparkjade, Jinan, China). Cells were seeded in T25 culture flasks and incubated in a humidified incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. The medium was changed every 2 days; cells were passaged at a ratio of 1:2 when the cell confluence reached approximately 90%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCell viability assay\u003c/h2\u003e\u003cp\u003eC918 and B16-F10 cells were seeded into 96-well plates at a density of 6 \u0026times; 10\u0026sup3; cells per well. After 24 h of culture, different concentrations of celastrol solutions (0, 0.03, 0.3, 1, 3, 10, and 30 \u0026micro;mol/L(\u0026micro;M)) were added to each well. The wells without celastrol were set as the control group, and those without cells and celastrol were set as the blank group. The cell viability was detected at 12 h, 24 h, 36 h, and 48 h after celastrol treatment using the CCK-8 Cell Viability Assay Kit (CT0001-B) produced by Jinan Spike Biotechnology Co., Ltd. During the detection, 10 \u0026micro;L of CCK-8 solution was added to each well and incubated at 37\u0026deg;C for 1 h. The absorbance value (OD value) was then measured at 450 nm using a microplate reader. The cell survival rate was calculated according to the formula: \"Cell survival rate = [(Experimental group OD value - Blank group OD value) / (Control group OD value - Blank group OD value)] \u0026times; 100%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eWound healing assay\u003c/h2\u003e\u003cp\u003eApproximately 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e B16-F10 cells were seeded into each well of a 6-well plate. When the cell confluence reached 80%-90%, a scratch was introduced by scraping the monolayer perpendicularly with a 200 \u0026micro;L pipette tip, followed by washing with PBS to remove detached cells. The control group received complete medium, while the treatment groups were exposed to varying concentrations of celastrol (0.03, 0.3, 1, 3, and 10 \u0026micro;M). All samples were incubated at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere for 48 h. Images of the scratch areas were captured at 0 h, 12 h, 24 h, 36 h, and 48 h using an optical microscope set at 100x magnification. Quantitative analysis of the scratch areas was performed using ImageJ software. The migration rate was calculated using the formula: wound healing area (%) = [(scratch area at 0 h - scratch area at 48 h) / scratch area at 0 h] \u0026times; 100.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCell cloning\u003c/h2\u003e\u003cp\u003eB16-F10 and C918 cells were seeded in 6-well plates at a density of 500 cells per well. After 24 h of incubation, various concentrations of celastrol (0, 0.03, 0.3, 1, 3, and 10 \u0026micro;M) were added to each well. The cells were then cultured for an additional 14 days. Cells were fixed using 4% paraformaldehyde (Servicebio, Wuhan, China) and subsequently stained with crystal violet solution (G1014-50ML, Servicebio, Wuhan, China). Finally, images were captured using a Zeiss Axio Vert. A1 system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCell apoptosis\u003c/h2\u003e\u003cp\u003eApoptosis detection was carried out using the Annexin V-FITC Apoptosis Detection Kit (Abs50001, Absin, Shanghai, China). B16-F10 and C918 cells were seeded in 6-well plates at a density of 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/well and incubated until they became fully adherent. Subsequently, each well was treated with 500 \u0026micro;L of celastrol solution at various concentrations (0.03, 0.3, 1, 3, 10, and 30 \u0026micro;M), while the blank control group received an equal volume of complete medium. After 24 h of incubation, the cells were harvested and stained with appropriate fluorescent dyes. Thereafter, the cells were analyzed using a BD FACSVerse flow cytometer.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eCell Cycle\u003c/h2\u003e\u003cp\u003eCell cycle analysis was conducted using a cell cycle detection kit (C1052, Beyotime, Jiangsu, China) on a BD FACSVerse flow cytometer. The propidium iodide (PI) staining solution was prepared by mixing staining buffer, RNase A (50\u0026times;), and the PI staining working solution (20\u0026times;). B16-F10 and C918 cells were seeded in 6-well plates at a density of 1 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells per well and were exposed to varying concentrations of celastrol (0, 0.03, 0.3, 1, 3, and 10 \u0026micro;M). Following 24 h of treatment, the cells were harvested, fixed with ethanol, and subsequently stained with PI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eReal-time fluorescence quantitative PCR (qPCR)\u003c/h2\u003e\u003cp\u003eIn this study, total RNA was extracted from cells using the SPARK easy animal tissue/cell RNA extraction kit (AC0202, Sparkjade, Jinan, China). Complementary DNA (cDNA) was synthesized from total RNA using HiScript II Q Select RT SuperMix (Q711-02/03, Vazyme, Nanjing, China). qPCR was performed using the ChamQ Universal SYBR qPCR Master Mix (Q711-02/03, Vazyme, Nanjing, China) under the following thermal cycling conditions: initial pre-denaturation at 95\u0026deg;C for 3 min, followed by 40 cycles of denaturation at 95\u0026deg;C for 12 s, and annealing/extension at 62\u0026deg;C for 40 s. GAPDH mRNA was used as an internal reference gene for normalization of CTNNB1 and STAT3 expression, and the relative expression levels of the target genes were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The corresponding primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequences for target genes\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\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer sequences\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHomo-CTNNB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: 5\u0026rsquo;-GCTGCAACTAAACAGGAAGGG \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-CCCACTTGGCAGACCATCAT \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHomo-STAT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: 5\u0026rsquo;-CGAAGGGTACATCATGGGCT-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-GATCTGGGTCTTACCGCGA-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHomo-GAPDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: 5\u0026rsquo;-GCACCGTCAAGGCTGAGAAC-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-TGGTGAAGACGCCAGTGGA-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMus-Ctnnb1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: 5\u0026rsquo;-TTGTAGAAGCTGGTGGGATGC-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-AGTCGCTGCATCTGAAAGGT-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMus-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: 5\u0026rsquo;-ACGAAAGTCAGGTTGCTGGT-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-GCTGCCGTTGTTAGACTCCT-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMus-GAPDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward: 5\u0026rsquo;-TGTCTCCTGCGACTTCAACA-3\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse: 5\u0026rsquo;-GGTGGTCCAGGGTTTCTTATC-3\u0026rsquo;\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=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eWestern Blot (WB)\u003c/h2\u003e\u003cp\u003eProtein extraction was performed from collected B16-F10 and C918 cells using the radioimmunoprecipitation assay (RIPA) buffer supplemented with phenylmethylsulfonyl fluoride (PMSF) at a volume ratio of 100:1. The extracted proteins were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and subsequently transferred onto polyvinylidene fluoride (PVDF) membranes. The following primary antibodies were used: β-catenin (1:2000, Proteintech, Hubei, China, 51067-2-AP), STAT3 (1:2000, Proteintech, Hubei, China, 10253-2-AP), and β-actin (1:1000, Servicebio, Wuhan, China, GB12001). Following overnight incubation at 4\u0026deg;C, the PVDF membranes were treated with horseradish peroxidase-conjugated secondary antibodies (goat anti-rabbit IgG, 1:15000, ZSGB-BIO, Beijing, China, ZB-2301; goat anti-mouse IgG, 1:15000, ZSGB-BIO, Beijing, China, ZB-2305) for 1 h at 4\u0026deg;C. Immunoreactive bands were detected using the FUSION-FX7 imaging system (Vilber Lourmat, La Valette-du-Val-d'Oise, France), and densitometric analysis was carried out using ImageJ software (National Institutes of Health, Bethesda, MD, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eIn this study, GraphPad Prism 9.5 (GraphPad Software, Inc., San Diego, USA) was used for statistical analysis of qPCR and WB experimental data. All experimental results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). The differences between groups were evaluated by independent sample \u003cem\u003et\u003c/em\u003e-test. The statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study encompasses the following aspects: predicting potential targets using network pharmacology, determining core targets through the integration of transcriptomics and machine learning analysis, validating the functions of core targets via immune infiltration analysis and scRNA-seq analysis, confirming the binding stability by combining MD with MDS, and conducting verification through cell-based experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eScreening potential targets by network pharmacology\u003c/h2\u003e\u003cp\u003eThrough the integration of multiple databases, 368 potential therapeutic targets of triptolide and 477 disease-associated targets related to UM were identified, with 46 overlapping targets common to both datasets. Through Cytoscape software, the study constructed a system network of drug-disease-target interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) to visually display the potential target association between celastrol and UM. This finding suggests a potential link between celastrol and UM, thereby indicating that celastrol may exert its effects on UM pathophysiology through interactions with the aforementioned target molecules.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe study imported 46 target genes into the clusterProfiler package for GO and KEGG enrichment analysis. The results showed that celastrol mainly exerts its effects by regulating biological processes such as programmed cell death. At the molecular function level, celastrol significantly affects RNA polymerase II transcription regulatory region sequence-specific DNA binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). KEGG pathway enrichment analysis indicated that celastrol may affect UM by regulating the JAK-STAT signaling pathway, PI3K-Akt signaling pathway, and Th17 cell differentiation-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). These findings demonstrate that celastrol is closely associated with tumor proliferation and invasion, and exerts its effects on tumor cell survival and apoptosis through the regulation of programmed cell death.\u003c/p\u003e\u003cp\u003eSubsequently, the 46 target genes were imported into the STRING database to construct a PPI network. The constructed network diagram contained 44 nodes and 533 edges. The network was analyzed topologically using the cytoNCA, MCODE, and cytoHubba plugins in Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F), and ultimately, the common targets in the three analysis results were selected as core targets\u0026mdash;namely STAT3, CTNNB1, MYC, EGFR, TP53, BCL2, PTEN, AKT1, CCND1, and MTOR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). These targets may be the key regulatory nodes through which celastrol exerts its anti-tumor effects, thereby providing a theoretical basis and potential direction for the development of treatment strategies targeting UM based on these targets in the future.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptome analysis identified significantly differentially expressed genes\u003c/h2\u003e\u003cp\u003eUsing the ESTIMATE algorithm, the study calculated the stromal score, immune score, ESTIMATE score, and tumor score for each sample. Survival analysis revealed that stratifying samples into high- and low-score groups based on these metrics showed a statistically significant association with the overall survival time of clinical patients. Specifically, higher stromal score, immune score, and ESTIMATE score were correlated with poorer prognosis in terms of survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This result provides a basis for understanding the role of the tumor microenvironment (such as stromal components and immune infiltration) in disease progression, and also offers a reference for the subsequent development of treatment strategies from the perspective of the microenvironment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on this, the study used the tumorScore to further divide the samples into TumorScore-high and TumorScore-low groups for subsequent analysis. Differential expression analysis revealed significant differentially expressed genes between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), with the TumorScore-high group showing more high-expression genes compared to the TumorScore-low group. GO/KEGG functional enrichment analysis of these high-expression genes revealed that the GO analysis results indicated that these genes were mainly enriched in biological processes such as cell development, cell differentiation, and cell signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), while the KEGG pathway analysis revealed that they were mainly involved in cell adhesion molecules, chemokine signaling pathways, and phototransduction pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These findings are highly consistent with the hallmark features of cancer cells, including enhanced proliferative capacity and invasive potential, thereby further supporting the validity of tumor score-based stratification.\u003c/p\u003e\u003cp\u003eThe study conducted machine learning analysis on the previously selected 46 genes. Lasso regression analysis selected 14 key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), SVM analysis identified 7 key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), and RF analysis chose 65 as the optimal number of features and selected the top 10 genes as key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Ultimately, the study determined four core genes, CTNNB1, SMAD3, STAT3 and TLR7, by taking the intersection of the key genes from the three machine learning methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Among them, the CTNNB1 and STAT3 genes were identified again in the screening, which further verified their roles as key regulatory hubs affected by celastrol in UM.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eSingle-gene GSEA analysis and its correlation with immune infiltration\u003c/h2\u003e\u003cp\u003eThis study utilized the CIBERSORT algorithm to assess immune cell infiltration in UM and revealed high proportions of T cells, NK cells, and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This observation was consistent with the cell types identified through subsequent single-cell clustering analysis. Further comparative analysis demonstrated significant differences in immune infiltration profiles between the TumorScore-high and TumorScore-low groups, with the TumorScore-low group exhibiting higher infiltration scores of CD8 T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Correlation heatmap analysis uncovered complex interactions among various immune cell populations, subsequently revealing a strong positive correlation between T cells and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The lollipop plot further illustrated the associations between key genes and specific immune cell types, showing a significant positive correlation between STAT3 and T cells. This suggested a potential role of STAT3 in promoting T cell infiltration into the tumor microenvironment. In contrast, CTNNB1 exhibited a negative correlation with CD8 T cells, which implied its involvement in immune evasion by suppressing CD8 T cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These findings support the hypothesis that CTNNB1 may facilitate tumor initiation and progression by establishing an immunosuppressive tumor microenvironment. Functional enrichment analysis revealed that the key genes CTNNB1 and STAT3 were significantly associated with multiple biological processes. Specifically, CTNNB1 was predominantly enriched in autophagy and mTOR signaling pathways, thereby highlighting its role in regulating cell metabolism and proliferation. Meanwhile, STAT3 was closely linked to NOD-like receptor signaling, endoplasmic reticulum protein processing, and ubiquitin-mediated proteolysis, thereby suggesting its involvement in cellular stress responses and inflammatory signaling during UM progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Collectively, these findings provide a solid theoretical and functional basis for targeting CTNNB1 and STAT3 in the development of precision therapeutic strategies for UM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eScRNA-seq analysis reveals expression heterogeneity and functional divergence of CTNNB1 and STAT3 in UM tumor cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study employed scRNA-seq analysis profiling to further elucidate the expression patterns and functional divergence of CTNNB1 and STAT3 across distinct UM cell populations and to characterize cellular heterogeneity within UM tissues while assessing the expression dynamics of CTNNB1 and STAT3. Through clustering analysis at a resolution of 0.1, 11 distinct cell clusters were identified and further annotated into four major cell types based on canonical marker gene expression: tumor cells (marked by MLANA and CITED1), T cells (marked by CD8A and GZMA), monocytes/macrophages (marked by C1QA and C1QB), and fibroblasts (marked by COL1A1 and COL4A1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Notably, CTNNB1 and STAT3 exhibited predominant expression in tumor cell clusters, yet substantial intercellular heterogeneity in their expression levels was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on CTNNB1 and STAT3 expression profiles, tumor cells were stratified into a high-expression subgroup (Exp-high) and a low-expression subgroup (Exp-low) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), with the validity of subgroup classification confirmed through expression visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Comparative transcriptomic analysis revealed a significantly higher number of upregulated genes in the Exp-high group compared to the Exp-low group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Functional enrichment analysis using GO demonstrated that the Exp-high group was predominantly enriched in developmental processes, including multicellular organism development, cell differentiation, and cell development, whereas the Exp-low group showed enrichment in immune-related processes such as humoral immune response, granulocyte chemotaxis, and leukocyte chemotaxis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). KEGG pathway analysis further indicated that the Exp-high group was enriched in oncogenic signaling pathways, including PI3K-Akt, Wnt, and JAK-STAT, which align closely with the core regulatory network identified in this study. In contrast, the Exp-low group was enriched in inflammation-associated pathways, such as NF-κB and IL-17 signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eCollectively, these findings highlight CTNNB1 and STAT3 as key regulatory nodes in UM tumor cells, where their expression heterogeneity contributes to functional divergence among tumor subpopulations. Specifically, high expression of CTNNB1 and STAT3 promotes tumor cell proliferation and malignant progression, whereas low expression is associated with immune microenvironment modulation via inflammatory signaling. These insights provide a mechanistic and theoretical foundation for targeting CTNNB1 and STAT3 in the development of precision therapeutic strategies for UM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eMD results provide compelling evidence for structural stability of the complex\u003c/h2\u003e\u003cp\u003eThe results indicate that celastrol binds to both CTNNB1 and STAT3 with high affinity, and exhibiting binding energies of -7.5 kcal/mol and \u0026minus;\u0026thinsp;8.4 kcal/mol, respectively. The planar and 3D interaction diagrams illustrate the key intermolecular forces involved. In the CTNNB1\u0026ndash;celastrol complex, the amino acid residues His410 and Glu500 form three conventional hydrogen bonds with celastrol, thereby contributing to binding stabilization through hydrophobic effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In the STAT3\u0026ndash;celastrol complex, Asp199 forms one conventional hydrogen bond with celastrol, with stability further maintained by hydrophobic interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). This result demonstrates that celastrol exhibits strong binding affinity toward CTNNB1 and STAT3, which further supports the underlying mechanism by which celastrol inhibits UM through interaction with these two proteins.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eMDS demonstrates that the complex exhibits a high degree of structural stability\u003c/h2\u003e\u003cp\u003eFrom the perspective of overall conformation, both complexes reached a stable state after 10 ns of simulation, as evidenced by minimal RMSD fluctuations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). At the residue level, CTNNB1 residues 300\u0026ndash;400 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) and STAT3 residues 0\u0026ndash;100 and 300\u0026ndash;400 displayed notable flexibility (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), thereby suggesting their involvement in key functional regions. In terms of structural compactness, the Rg for the CTNNB1\u0026ndash;celastrol complex stabilized after 0\u0026ndash;80 ns of simulation, whereas the STAT3\u0026ndash;celastrol complex reached stability after 50 ns (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Regarding hydrogen bonding interactions, CTNNB1 consistently formed one hydrogen bond with celastrol (with a maximum of four observed) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), while STAT3 maintained a stable interaction of 2\u0026ndash;3 hydrogen bonds with celastrol (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH), thereby contributing to the overall stability of the complexes. Additionally, the SASA decreased and remained stable in both systems, thus, indicating increased structural compactness (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI, J). Gibbs free energy analysis revealed that the CTNNB1\u0026ndash;celastrol complex achieved optimal stability at Rg\u0026thinsp;=\u0026thinsp;3.5 nm and RMSD\u0026thinsp;=\u0026thinsp;0.54 nm, while the STAT3\u0026ndash;celastrol complex reached its most stable state at Rg\u0026thinsp;=\u0026thinsp;3.6 nm and RMSD\u0026thinsp;=\u0026thinsp;0.65 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK). These findings provide strong structural evidence that celastrol interacts specifically with CTNNB1 and STAT3 to exert its biological functions, and further supports its potential as a promising ligand targeting these two proteins.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eCelastrol inhibits proliferation and migration of B16-F10 and C918 cells\u003c/h2\u003e\u003cp\u003eTo explore the effect of celastrol on the viability of UM cells, B16-F10 cells were treated with various concentrations of celastrol (0, 0.03, 0.3, 1, 3, and 10 \u0026micro;M) according to relevant literature \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, while C918 cells were exposed to a broader range of concentrations (0, 0.3, 1, 3, 10, and 30 \u0026micro;M). Cell viability was evaluated using the CCK-8 assay at 12, 24, 36, and 48 h. The results demonstrated that celastrol significantly reduced the viability of both B16-F10 and C918 cells in a time- and dose-dependent manner. Notably, B16-F10 cells exhibited marked cell death at 10 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), whereas C918 cells showed significant cytotoxicity at a lower concentration of 3 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The therapeutic effects on both cell lines were most pronounced following 24 h of treatment. Accordingly, in subsequent experiments, the drug treatment regimen for B16-F10 cells was established as 10 \u0026micro;M for 24 h, whereas a concentration of 3 \u0026micro;M for 24 h was selected for C918 cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eColony formation assays were conducted to further validate the inhibitory effects of celastrol on both cell lines. The number of colonies formed was quantified following 14 days of treatment with varying concentrations of celastrol. The data revealed that celastrol significantly suppressed colony formation in a concentration-dependent manner. Specifically, 10 \u0026micro;M celastrol exerted a strong cytotoxic effect on B16-F10 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), while 3 \u0026micro;M celastrol induced substantial toxicity in C918 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eScratch wound healing assays were performed to assess the impact of celastrol on cell migration. Microscopic images were captured at multiple time points to evaluate the healing process under different treatment conditions. The results showed that 10 \u0026micro;M celastrol had the most pronounced inhibitory effect on the migration of B16-F10 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D), whereas 3 \u0026micro;M celastrol exhibited the strongest suppression of C918 cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D).\u003c/p\u003e\u003cp\u003eCollectively, these findings indicate that celastrol effectively inhibits both the proliferation and migration of B16-F10 and C918 cells in a time- and dose-dependent manner.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eCelastrol induces apoptosis and cell cycle arrest in B16-F10 and C918 cells\u003c/h2\u003e\u003cp\u003eB16-F10 and C918 cells were exposed to varying concentrations of celastrol for 24 h to explore the effects of celastrol on apoptosis and cell cycle of UM cells. Apoptosis rates and cell cycle distribution were subsequently analyzed using flow cytometry. The results demonstrated that celastrol significantly increased the apoptosis rate in both cell lines in a dose-dependent manner (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F; \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE, F). Furthermore, celastrol induced distinct cell cycle arrest patterns in each cell type: in B16-F10 cells, the proportion of cells in the G1 and S phases increased, while the G2/M phase population decreased (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B); in C918 cells, the G1 phase population decreased, the G2/M phase population increased, and the S phase population remained relatively unchanged (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC, D). Collectively, these findings indicate that celastrol not only promotes apoptosis in B16-F10 and C918 cells, but also exerts anti-tumor effects by modulating cell cycle progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCelastrol suppresses mRNA expression and protein levels of core genes in B16-F10 and C918 cells\u003c/h3\u003e\n\u003cp\u003eB16-F10 and C918 cells were treated with 10 \u0026micro;M and 3 \u0026micro;M celastrol, respectively, for 24 h to investigate the impact of celastrol on key molecular targets. Total cellular RNA and protein were extracted, and their expression levels were analyzed by qPCR and WB. The results demonstrated that celastrol significantly downregulated the mRNA expression of STAT3 and CTNNB1 in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, C), and markedly reduced the protein levels of STAT3 and β-catenin (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, D). The experimental results fully demonstrate that celastrol significantly inhibits the proliferation and metastasis of UM cells by targeting CTNNB1 and STAT3, while promoting apoptosis and inducing cell cycle arrest.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUM arises from uveal melanocytes and predominantly affects Caucasians, with 90% of cases involving the choroid \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Epidemiological data indicate that the median age at diagnosis for UM is 62 years \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Traditional Chinese medicine (TCM), as an integral part of traditional medical systems \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, has garnered significant attention in oncology due to its comprehensive theoretical framework and relatively mild side effects \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Celastrol, an active compound derived from TCM, has demonstrated potent inhibitory effects against various malignant tumors \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This study systematically investigated the potential mechanisms of celastrol in UM using a multi-level approach that integrated network pharmacology, transcriptomic analysis, scRNA-seq, MD, and MDS, as well as cell-based experiments. This study was the first to screen the relevant targets of celastrol against UM, such as TP53, BCL2, and MYC, which are closely related to the occurrence and development of UM \u003csup\u003e40,41\u003c/sup\u003e. Furthermore, the core regulatory targets CTNNB1 and STAT3 were further screened out, thereby providing a novel theoretical basis and potential therapeutic strategies for precision treatment of UM.\u003c/p\u003e\u003cp\u003ePrevious studies have confirmed that celastrol can inhibit the proliferation of B16-F10 melanoma cells by regulating the PI3K/AKT/mTOR signaling pathway \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. To our knowledge, this study is the first to link the anti- UM effect of celastrol to its key molecular targets, CTNNB1 and STAT3. CTNNB1 is a central regulatory component of the Wnt signaling pathway and its inhibition effectively blocks the aberrant proliferation signals in tumor cells \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Meanwhile, STAT3 functions as a core effector in the JAK-STAT pathway and its downregulation not only diminishes the survival advantage of tumor cells \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, but may also enhance anti-tumor immunity by alleviating the suppression of CD8⁺T cells. This \u0026ldquo;dual regulatory\u0026rdquo; mechanism exemplifies the multi-target advantages of natural compounds in cancer therapy.\u003c/p\u003e\u003cp\u003eBased on the ESTIMATE algorithm, this study analyzed the UM transcriptome and stratified tumor samples according to TumorScore, thereby identifying differentially expressed genes and revealing a potential link between the tumor microenvironment and clinical prognosis. Subsequently, three machine learning algorithms\u0026mdash;Lasso regression, SVM, and RF\u0026mdash;were applied to cross validate 46 potential targets, ultimately identifying CTNNB1 and STAT3 as core regulatory nodes. This quantitative modeling approach filtered out low-impact targets, thereby highlighting the central roles of CTNNB1 and STAT3 in the UM regulatory network.\u003c/p\u003e\u003cp\u003eScRNA-seq revealed heterogeneity in CTNNB1 and STAT3 expression, thereby indicating the presence of functionally distinct tumor subpopulations in UM. The high-expression subpopulation relies on classical oncogenic pathways to sustain its malignant phenotype, whereas the low-expression subpopulation may evade immune surveillance by remodeling the tumor immune microenvironment. These findings provide a solid theoretical foundation for the \u0026ldquo;stratified treatment\u0026rdquo; strategy in UM.\u003c/p\u003e\u003cp\u003eFurthermore, this study used MD and MDS to confirm, for the first time, that celastrol specifically binds to and inhibits CTNNB1 and STAT3. In vitro experiments further validated its inhibitory and cytotoxic effects on B16-F10 and C918 cells. These findings not only identify novel molecular targets and therapeutic strategies for the precise treatment of UM, but also open new avenues for the clinical translation of natural compounds.\u003c/p\u003e\u003cp\u003eHowever, this study also had several limitations. First, the absence of animal model experiments limits the ability to fully recapitulate the effects of celastrol within a complex in vivo microenvironment. Second, the reciprocal regulatory mechanisms between CTNNB1 and STAT3, as well as their synergistic interactions with other key molecular targets, remain to be further elucidated. Future studies should include the development of UM animal models to validate the in vivo antitumor efficacy of celastrol and its modulation of the immune microenvironment. Additionally, gene knockout or overexpression approaches should be employed to define the specific roles of CTNNB1 and STAT3 in the molecular mechanisms of celastrol action. Finally, combination strategies involving celastrol and immune checkpoint inhibitors should be explored to enhance antitumor immune responses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that celastrol exerts potent anti-tumor effects on UM cells through dual targeting of CTNNB1 and STAT3. It not only directly suppresses cell proliferation and migration while inducing apoptosis, but also modulates immune infiltration within the tumor microenvironment. As key regulatory molecules in UM, the expression heterogeneity of CTNNB1 and STAT3 offers a molecular basis for disease stratification and personalized therapeutic approaches. These findings establish a theoretical foundation for the development of celastrol as a promising therapeutic agent for UM, and provide novel insights into combination therapies that simultaneously target tumor cells and the surrounding microenvironment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, P.Z.; methodology, ZL.L.; formal analysis, validation, software, ZL.L.; investigation, RF.X.; resources, P.Z.; data curation, ZL.L.; writing—original draft preparation, ZL.L.; writing—review and editing, P.Z., XD.H.; visualization, ZL.L., RF.X.; supervision, P.Z.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China Youth Science Foundation (No. 82205198), China Postdoctoral Science Foundation Project (No. 2022M711984), Shandong Young Science and Technology Talent Support Program (SDAST2025 QTB049). Shandong Provincial Traditional Chinese Medicine Science and Technology Project (NO. M20242003). Clinical Special Project of Shandong University of Traditional Chinese Medicine (LCKY202434)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Should any raw data files be needed in another format they are available from the first author Zhanglong Li ([email protected]) upon reasonable request. Source data are provided with this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRossi, E. et al. Uveal Melanoma Metastasis. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJager, M. J. et al. Uveal melanoma. \u003cem\u003eNat. Rev. Dis. Primers\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, 24 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRantala, E. S., Hernberg, M. M., Piperno-Neumann, S., Grossniklaus, H. E. \u0026amp; Kivel\u0026auml;, T. T. 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Toward understanding the origin and evolution of cellular organisms. \u003cem\u003eProtein Sci.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 1947\u0026ndash;1951 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Celastrol, Uveal melanoma, Network pharmacology, Machine learning, Molecular Dynamics","lastPublishedDoi":"10.21203/rs.3.rs-7716555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7716555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUveal melanoma (UM) is among the most prevalent intraocular malignant tumors worldwide. Celastrol exhibits broad-spectrum anticancer properties; however, its underlying therapeutic mechanism in UM is yet to be elucidated. In this study, a network pharmacology approach was employed to identify potential common targets of celastrol and UM. These targets were further analyzed in conjunction with transcriptomic data and machine learning algorithms, which led to the identification of CTNNB1 and STAT3 as key molecular targets. The functional roles of these targets were investigated through immune infiltration analysis and single-cell RNA sequencing (scRNA-seq), while the binding stability between celastrol and CTNNB1/STAT3 was assessed using molecular docking (MD) and molecular dynamics simulation (MDS). Subsequently, celastrol was administered to B16-F10 and C918 cell lines, demonstrating that it significantly suppresses cell proliferation and migration by downregulating CTNNB1 and STAT3 expression, while simultaneously inducing apoptosis and cell cycle arrest. Moreover, real-time quantitative PCR (qPCR) and western blot (WB) analyses corroborated the modulation of target expression levels. Therefore, celastrol exerts potent anti-tumor effects in UM by inhibiting the CTNNB1 and STAT3 signaling pathways, thereby suppressing tumor cell proliferation and metastasis, as well as promoting cell cycle arrest and apoptosis.\u003c/p\u003e","manuscriptTitle":"Research on the molecular mechanism of celastrol targeting CTNNB1/STAT3 to inhibit uveal melanoma based on network pharmacology and multi-omics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 17:30:54","doi":"10.21203/rs.3.rs-7716555/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T05:01:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T17:24:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T11:46:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219117081668598389905377455974262224085","date":"2025-11-26T06:01:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198448033070278818179322551950537661628","date":"2025-11-26T03:24:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213430362703258202175024557003402019303","date":"2025-11-24T15:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256132143025704064680146655568430946248","date":"2025-11-22T12:57:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309260074829110016140751428646549622486","date":"2025-11-21T08:49:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-21T07:17:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T07:08:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-18T09:28:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-15T13:07:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-15T13:03:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ec51c3d-4be5-4a6b-8078-00ededff3cb5","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58657891,"name":"Biological sciences/Cancer"},{"id":58657892,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":58657893,"name":"Biological sciences/Drug discovery"},{"id":58657894,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-01-26T16:01:15+00:00","versionOfRecord":{"articleIdentity":"rs-7716555","link":"https://doi.org/10.1038/s41598-026-37061-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-24 15:58:07","publishedOnDateReadable":"January 24th, 2026"},"versionCreatedAt":"2025-11-27 17:30:54","video":"","vorDoi":"10.1038/s41598-026-37061-5","vorDoiUrl":"https://doi.org/10.1038/s41598-026-37061-5","workflowStages":[]},"version":"v1","identity":"rs-7716555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7716555","identity":"rs-7716555","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}