Background Ovarian cancer is associated with a poor prognosis, largely due to late-stage diagnosis and the development of therapy resistance. The green tea polyphenol epigallocatechin-3-gallate (EGCG) shows anti-tumor potential, but its targets and mechanisms in ovarian cancer remain unclear. This study aims to identify the key molecular targets and elucidate the underlying mechanisms of EGCG’s action in ovarian cancer. Methods Differentially expressed genes (DEGs) in the GSE18520 dataset were identified using the limma package (|log2 fold change| > 2, adjusted p < 0.05). Weighted gene co-expression network analysis (WGCNA) was performed with key parameters: soft thresholding power β = 12 (scale-free R2 = 0.801), unsigned network type, deepSplit = 2, minModuleSize = 15, and mergeCutHeight = 0.25. Hub genes were screened by intersecting DEGs, WGCNA brown module genes (|Module Membership| > 0.8, |Gene Significance| > 0.2), and EGCG targets from the Comparative Toxicogenomics. Three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) (5-fold cross-validation), support vector machines-recursive feature elimination (SVM-RFE), and random forest (RF) (Gini index), were applied to identify core targets. Cells were randomly assigned to Control, EGCG-treated, and EGCG + potassium voltage-gated channel subfamily H member 2 (KCNH2)-overexpression groups. In vitro experiments (Cell Counting Kit-8 (CCK-8), colony formation, flow cytometry) and molecular docking (AutoDock Vina) were used for validation. Results A total of 1204 DEGs were identified (|log2 fold change| > 2, adjusted p < 0.05). WGCNA identified 8 co-expression modules, among which the brown module (containing 386 genes) showed the strongest correlation with ovarian cancer. Intersection analysis yielded 19 common genes, and machine learning further narrowed this to KCNH2 as the sole overlapping target. KCNH2 was up-regulated in ovarian cancer tissues and cell lines (p < 0.01). Molecular docking predicted a potentially favorable interaction between EGCG and KCNH2, with a binding energy of −6.7 kcal/mol. Functionally, EGCG inhibited ovarian cancer cell viability (p < 0.01), and KCNH2 overexpression reversed EGCG-induced decrease in KCNH2 expression (p < 0.05). Additionally, EGCG reduced colony formation and induced apoptosis (p < 0.001), while KCNH2 overexpression reversed these effects (p < 0.01). Conclusion Based on bioinformatic and in vitro analyses, EGCG appears to suppress ovarian cancer cell growth and promote apoptosis partly by targeting and down-regulating KCNH2. These preliminary findings propose the EGCG-KCNH2 axis as a candidate mechanism warranting further preclinical investigation. However, given that the current evidence is limited to computational predictions and cell-based assays, additional studies in animal models and clinical specimens are necessary before any therapeutic implications can be considered. Nevertheless, this work provides a testable mechanistic hypothesis for EGCG’s anti-ovarian cancer activity, distinguishing it from non-specific anti-proliferative effects.
Background Coxsackie B3 virus (CVB3) poses a significant public health challenge due to the severity of its associated clinical disorders, driving an urgent need for novel therapeutic agents. Building upon previous research that identified potent isoxazole-based antiviral derivatives, two promising candidate compounds (P3 and P4) were previously identified through computational screening. Objective This study aims to propose a viable synthetic pathway for candidate compound P3 via retrosynthetic analysis, validate its binding kinetics through ligand-release channel dynamics, and elucidate the reaction mechanisms governing its synthesis. Methods Retrosynthetic analysis was conducted to establish a synthetic route for compound P3. The potential mechanism of the key 1,3-dipolar cycloaddition reaction was investigated theoretically. To examine the exit pathways and kinetic behavior of the ligands from the receptor active site to the surface, unbinding release simulations were performed using CaverDock software. Results Retrosynthetic design successfully established a plausible synthetic route toward compound P3, with mechanistic predictions confirming that its formation via 1,3-dipolar cycloaddition is thermodynamically controlled. CaverDock channel analysis identified the primary migration tunnels utilized by the ligands, demonstrating that compound P3 exhibits superior unbinding kinetics and release profiles compared to other derivatives. Conclusions These findings corroborate prior QSAR and molecular docking predictions regarding the high efficacy of compound P3. By providing both a synthetic roadmap and vital insights into ligand–receptor interaction dynamics, this work offers a reliable benchmark for the rational design and synthesis of novel CVB3 antiviral therapeutics. However, further experimental validation is required to confirm the in vitro and in vivo efficacy and safety profile of these proposed candidates.
Introduction Excess formation of AGEs (Advanced Glycation End Products) and increased AGE-RAGE (Receptor for Advanced Glycation End Products) interaction with resultant activation of NF-κB (Nuclear Factor Kappa B) signaling are implicated in the pathogenesis of diabetic nephropathy (DN). Interrupting RAGE-mediated NF-κB signaling could be a therapeutic option to treat DN and other AGEs mediated pathologies. Method In this study, the potential of phytochemicals identified from the methanolic leaf extract of Dillenia indica (DI) to interfere with the AGEs-RAGE axis and NF-κB activation using molecular docking, ADME-Tox and molecular dynamic simulation were investigated in silico approaches. Result Using molecular docking screening of 78 DI phytocompounds identified from GC-MS and LC-MS chromatographic techniques, it was found that some compounds such as Somniferine, Catechin, Butin, Cancentrine, Dipiperamide A and O-Methylsomniferine showed indeed better binding with RAGE and NF-κB while Secoisotetrandrine was comparable to the control drugs. ADME-Tox profiling revealed that Secoisotetrandrine had strong affinity to interact with both RAGE and NF-κB with predicted non-nephrotoxicity.Molecular dynamics simulations further revealed structural binding of Secoisotetrandrine with RAGE and NF-κB protein complexes using RMSD, RMSF & RG analysis throughout the trajectory. MMGBSA free binding energy analysis of the bound complexes demonstrated Secoisotetrandrine with superior stability due to stronger dispersion and hydrophobic contributions, although more stable H-bond interactions were observed in positive controlprotein complexes. Discussion AGEs accumulation and their interaction with RAGE contribute to increased intracellular ROS and activate self-perpetuating NF-κB loop. Virtual screening revealed Secoisotetrandrine with stable binding interaction and structural affinity with RAGE and NF-κB, indicating it could interrupt RAGE-mediated NF-κB interaction and might act as a valuable therapeutic way in providing renoprotection from elevated oxidative stress and inflammation in diabetic influenced renal cells. Conclusion The present study evaluates the antioxidant potential of the plant along with the bioactive metabolites from the methanolic extract, which identifies Secoisotetrandrine from the methanolic extract of Dillenia indica leaves by interrupting RAGE/NF-κB signaling. The study predicts Secoisotetrandrine as a potential candidate with favourable drug-likeness and pharmacokinetic properties and as a promising RAGE/NF-κB inhibitor and renoprotective agent. However, further evaluations are needed for its in vitro and in vivo studies as a therapeutic agent against Diabetic Nephropathy.
