Search Results (584)

Background/Objectives: Glioblastoma (GBM) is the most lethal primary brain malignancy in adults, with a median overall survival of approximately 15 months. Temozolomide (TMZ) resistance develops in virtually all patients, and no second-line regimen has improved outcomes over the past two decades. The DNA methyltransferase inhibitor decitabine (DAC) has attracted interest as a chemosensitizer, but whether it directly reverses the TMZ-resistance transcriptome or operates through distinct, complementary mechanisms has not been tested at multi-omics resolution. Methods: We performed an integrative six-layer multi-omics analysis across five public GEO datasets (bulk RNA-seq, EPIC 850K methylation, and 21,676 single cells) re-purposed from studies conducted for unrelated aims, formally tested DAC-mediated reversal of the TMZ-resistance transcriptome across 11,707 genes, mapped pharmacogenomic targets with DGIdb v5, and built an exploratory, hypothesis-generating 11-gene prognostic model internally validated in TCGA-GBM (n = 166) and externally tested in the independent CPTAC-GBM cohort (n = 96). Results: DAC reprogrammed transcription across 1114–1882 differentially expressed genes per cohort and reactivated 146 direct epigenetic targets, identifying INPP5D/SHIP1 as the top-ranked direct epigenetic-reactivation target. Genome-wide reversal analysis across 11,707 co-detected genes showed a negligible effect (Spearman ρ = 0.073), but single-cell analysis revealed significant per-cell attenuation of MES-like and stem-like programs (Δ = −0.071 and −0.135, respectively; both p < 0.001). The 11-gene risk model achieved a Harrell’s C-index of 0.706 (apparent); after correcting for the two-stage gene selection with a full-pipeline bootstrap, the optimism-corrected C-index was 0.63, and external validation in an independent cohort (CPTAC-GBM, n = 96) showed only near-chance discrimination (C-index 0.55), indicating that the signature does not generalize and is exploratory. Pharmacogenomic mapping yielded 734 unique therapeutic agents (230 FDA-approved) across 69 druggable targets after excluding AR. Most of these agents are not GBM-directed, so this catalog-level mapping is hypothesis-generating rather than a set of therapeutic recommendations. Conclusions: DAC does not broadly reverse the TMZ-resistant transcriptome but acts through three complementary mechanisms: epigenetic reactivation of INPP5D/SHIP1, cancer-testis-antigen and type I interferon induction, and per-cell attenuation of mesenchymal–stem-like transcriptional intensity, supporting hypotheses for rationally designed DAC-based combination therapy in TMZ-resistant GBM. Full article

Background: The rapid escalation of multidrug-resistant P. aeruginosa necessitates anti-virulence strategies targeting quorum sensing rather than bacterial survival; however, integrating scaffold repurposing with generative AI to inhibit LasR remains underexplored. Here, we address this gap by combining triazole scaffold mining with transformer-based de novo molecular generation to systematically identify putative LasR inhibitors. Methods: An integrated computational pipeline involving Structure-based inhibitor design using Generative Transformer Architecture, deep learning-assisted GNINA rescoring, density functional theory optimization, and molecular dynamics simulations was employed, followed by MM-GBSA binding free energy estimation. Results: Screening of 2666 triazole derivatives and 19,861 DrugGPT-generated compounds yielded top hits with superior binding affinities (−11.59 to −13.81 kcal/mol) compared to the reference ligand (−8.50 kcal/mol). MD simulations yielded stable protein–ligand complexes with RMSD values of 2.24–3.01 Å, while key interactions involving residues Tyr50, Asp67, and Ser123 were consistently maintained. Binding free energy calculations further confirmed strong thermodynamic stability, with MM-GBSA ΔG_bind values significantly favorable, supporting robust ligand–receptor affinity. Conclusions: Collectively, these findings establish a powerful AI-integrated framework for anti-virulence drug discovery and identify structurally diverse, high-affinity triazole-based and de novo compounds as promising lead candidates for disrupting LasR-mediated quorum sensing in P. aeruginosa. Full article

Drug resistance arising during cancer development and progression remains a major challenge in the treatment of epidermal growth factor receptor (EGFR)-driven tumors, particularly those harboring the clinically relevant T790M/L858R double mutation. In this study, we developed an integrated computational workflow combining explainable machine learning, virtual screening, molecular dynamics simulations, and binding free-energy calculations to identify novel inhibitors of this drug-resistant EGFR variant. An XGBoost regression model was trained using scaffold-aware cross-validation, Bayesian hyperparameter optimization, and sequential feature selection, resulting in a compact model based on 16 molecular descriptors. The model demonstrated robust predictive performance on external validation data, while SHAP analysis identified descriptors related to the local electronic environment, fragment distribution, and molecular topology as the primary contributors to activity prediction. The optimized model was subsequently applied to screen compounds from the Enamine REAL database. Top-ranked candidates were evaluated using explicit-solvent molecular dynamics simulations and MM/GBSA binding free-energy calculations. Several compounds formed stable protein–ligand complexes and maintained key interactions with residues known to be important for EGFR inhibition, including Lys745, Met790, and Leu718. These results demonstrate that the proposed workflow can efficiently prioritize computational candidates of drug-resistant EGFR mutants and may support the development of new therapeutic strategies for overcoming resistance in EGFR-driven cancers. Full article

Parkinson’s disease (PD) is characterised by the pathological aggregation of α-synuclein (α-syn) into Lewy body inclusions, yet no disease-modifying therapy exists. To address this, we developed an integrated computational pipeline combining quantitative structure–activity relationship (QSAR) modelling, structure-based virtual screening, molecular dynamics (MD) simulation, and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) binding free energy calculations to repurpose FDA-approved drugs as α-syn fibril inhibitors. Two complementary QSAR model families were trained on 501 α-syn binding affinity records from BindingDB: Morgan extended-connectivity fingerprint (ECFP4) classifiers and a frozen ChemBERTa-77M-MLM transformer encoder, each using Random Forest and Logistic Regression. The applicability domain (AD) was assessed using Morgan–Tanimoto similarity (Tc ≥ 0.40) and calibrated ChemBERTa cosine distance (θ ≤ 0.367). A three-stage funnel applying central nervous system (CNS) permeability filters, a consensus QSAR probability threshold (≥0.80), and AD gating reduced 2241 FDA-approved drugs to 205 candidates for AutoDock Vina 1.2.6 docking against two sites on the cryo-electron microscopy (cryo-EM) α-syn fibril structure, PDB 6SSX: the inter-protofilament cleft (Site 1) and the non-amyloid-beta component (NAC) groove (Site 2). The Morgan fingerprint models achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.940 and a balanced accuracy of 0.810; the ChemBERTa models achieved an AUROC of 0.785 and a balanced accuracy of 0.728. Notably, ChemBERTa AD covered 76.8% of the FDA drugs versus only 5.5% for Morgan–Tanimoto, enabling broad-spectrum screening. The top docking candidates were Olaparib (−7.91 kcal/mol), Paliperidone (−7.75 kcal/mol), Niraparib (−7.18 kcal/mol), Dordaviprone (−7.06 kcal/mol), and Parecoxib (−6.89 kcal/mol). The MD simulations over 200 ns across three independent replicates confirmed stable NAC groove binding, and replicate-averaged MM-PBSA calculations yielded ΔG = −20.6 ± 1.9 kcal/mol for Olaparib at Site 2, −17.1 ± 0.9 kcal/mol for Risperidone, and −16.9 ± 0.8 kcal/mol for Paliperidone, reported as the mean ± standard error of the mean (SEM) across replicates. Olaparib additionally formed five hydrogen bonds in the representative pose, while MD trajectories maintained approximately 2–5 hydrogen bonds, together with a halogen bond within the NAC groove, the largest contact count of any screened compound. These findings identify Olaparib as a novel high-affinity repurposing lead, while Paliperidone and Risperidone are reported as chemically informative secondary NAC–groove binders rather than proposed antiparkinsonian therapeutics, given that their dopamine D2-antagonist pharmacology is clinically associated with drug-induced parkinsonism. All of the candidates warrant experimental validation via thioflavin-T fluorescence or nuclear magnetic resonance (NMR) spectroscopy. Full article

Background: Skeletal muscle (SM) plays a critical role in movement, metabolism, and organ protection, with its maintenance and regeneration relying on muscle satellite (stem) cells (MSCs). Prostaglandin E2 (PGE2) regulates MSCs, but PGE2 levels decline with aging due to increased catabolism by 15-hydroxyprostaglandin dehydrogenase (15PGDH), a negative regulator of muscle repair. Methods: This study aimed to employ artificial intelligence and machine learning (ML)-assisted, structure-based screening approaches to identify novel 15PGDH inhibitors. Supervised models (support vector machine, random forest, and XGBoost were trained on curated bioactivity data (IC₅₀ values) from the ChEMBL database and used to virtually screen the Maybridge compound library (~51,000 compounds). Results: The area under the curve (AUC) values of the developed models SVM, RF, and XGBoost were 0.96, 0.99, and 1.00, respectively. Promising inhibitors were further validated using structure-based virtual screening (docking), molecular dynamics simulations (200 ns), and MM-PBSA/GBSA analyses. The top five inhibitors (PD00616, HTS11491, HTS02629, AW00889, and HTS11190) were identified as active (ML analysis) and potential 15PGDH inhibitors based on their subsequent binding affinities, involvement of catalytic residues (Ser138, Tyr151, and Lys155), and complex stability. Additionally, these inhibitors were found to follow the drug-likeness criteria. Conclusions: These findings offer valuable insights for the development of novel therapeutics targeting 15PGDH to combat muscle degeneration and related pathologies, including aging and sarcopenia. Full article

Background/Objectives: Prostate cancer (PC) exhibits marked disparities in incidence and mortality across ethnicities, with men of Sub-Saharan African (SSA) ancestry experiencing 1.7 and 2.0 times higher values, respectively, than European men (EUR). However, SSA preclinical models remain scarce (just one commercial cell line). In this study, we established and characterized a novel panel of PC cell lines derived from SSA patients using conditional reprogramming (CR), a method that enables efficient propagation of primary cells while maintaining their genotypic and phenotypic features. Methods: CR was applied to five SSA-PC samples, and successfully propagated samples were authenticated by STR and ~1 million SNP profiling, and extensively characterized for proliferative capacity, migratory behaviour, karyotyping and epithelial and prostate tumour lineage markers. To explore drug response profiles, a high-throughput screen (HTS) of 1280 clinically annotated compounds was conducted. Results: Three SSA-PC cell lines were successfully established and authenticated, and five potential drug hits were validated. A new finding was the reduced sensitivity of SSA-derived models (9.0 times difference compared to commercial EUR PC cell lines) to camptothecin, a TOP1 inhibitor, while being equally sensitive to epirubicin hydrochloride, a TOP2 inhibitor. The cardiac glycoside digoxin, anthelmintic pyrvinium pamoate and antirheumatic agent auranofin were also efficient drugs in the in vitro testing. Conclusions: These results support the relevance of SSA-derived PC models for preclinical drug screening and highlight the value of including ancestry-diverse models in oncobiology research. Full article

Background: HER2 is a key oncogenic gene in breast cancer, involved in tumor progression, metastasis, and therapeutic resistance. This study aimed to find new HER2 inhibitors using a hybrid of machine learning (ML) and structure-based virtual screening (VS), combined with molecular dynamics (MD) simulations on various scaffolds. Methods: Four supervised molecular fingerprint classification models were trained on a dataset of 10,000 validated compounds from ChEMBL. Random Forest was the top model for screening a large compound library. Selected compounds underwent molecular docking in the HER2 ATP binding site, ADMET, drug likeness, toxicity analysis, and 200 ns MD simulations. Methods like PCA, FEL, hydrogen-bond analysis, DCCM, RDF, salt-bridge analysis, and MM/PBSA were used to assess binding stability. Results: Virtual screening identified three compounds, CHMEBL193865 (Lead-1), CHMEBL46740 (Lead-2), and CHMEBL151318 (Lead-3)—with better binding affinity and interaction profiles than the reference inhibitor. MD simulations showed stable protein–ligand complexes with RMSD values of 2.32–2.76 Å. Among these, Lead-2 was the most structurally stable, and Lead-1 had the most favorable binding free energy. All three compounds showed good drug likeness, ADMET properties, and low predicted toxicity. Conclusions: These findings support further in vitro and in vivo testing for developing new therapeutics against HER2-overexpressing breast cancer, highlighting two scaffolds with promising lead optimization potential. Full article

Steroid sulfatase (STS) plays a crucial role in intratumoral estrogen biosynthesis and represents an attractive therapeutic target in estrogen receptor-positive breast cancer. In this study, a new series of potential STS inhibitors based on the quinone methide oxime scaffold, precisely 2-(4-hydroxyiminocyclohexa-2,5-dien-1-ylidene)-2-phenylacetonitrile framework, were designed and evaluated using an integrated in silico approach. A virtual library comprising 216 compounds (including syn/anti isomers) was screened by molecular docking against the human STS crystal structure (PDB ID: 8EG3). The binding affinities ranged from −7.077 to −9.726 kcal·mol⁻¹; however, only the best-performing compound 45-syn showed values comparable to those of the reference ligands. The top-ranked compound (45-syn) exhibited favorable interactions within the catalytic site, including polar contacts near the FGly–Ca²⁺ region and extensive hydrophobic and π–π interactions in the adjacent pocket. Structure–binding relationship analysis highlighted the importance of electron-withdrawing substituents at R¹ and aromatic moieties at R² for enhanced binding. Molecular dynamics simulations confirmed the stability of ligand–STS complexes and demonstrated reduced flexibility compared to the apo form. Additionally, in silico ADMET predictions indicated generally favorable drug-like profiles for selected candidates. Overall, the results highlight computationally prioritized scaffolds that merit further synthesis and biological evaluation as potential STS inhibitors. Full article

Cancer progression is driven by dysregulated kinase signaling and apoptotic evasion across multiple malignancies. Although targeted kinase inhibitors have improved outcomes, resistance and toxicity remain major challenges. Natural phytochemicals offer promising multi-target therapeutic potential. Persea americana contains diverse bioactive compounds; however, its role in multi-cancer kinase targeting remains underexplored. This study aimed to identify and validate anti-cancer kinase targets of Persea americana phytoconstituents across five cancers: lung, breast, cervical, colorectal, and prostate, using integrated network pharmacology and molecular simulation approaches. Cancer-associated genes were retrieved from the Open Targets Platform and prioritized through Gene Ontology analysis. Overlapping targets with 208 predicted human targets of Persea americana were identified. Protein–protein interaction networks revealed hub genes, followed by TCGA-based validation. Twenty-five phytoconstituents were docked against SRC and GSK3β, and top complexes underwent 100 ns molecular dynamics simulations. Enrichment highlighted kinase activity and apoptosis. SRC emerged as a pan-cancer hub, while GSK3β was prominent in breast cancer. Luteolin showed strongest binding to SRC (−11.9 kcal/mol), outperforming the co-crystal inhibitor, while valencene showed affinity toward GSK3β (−8.8 kcal/mol). Simulations confirmed stable interactions. Luteolin exhibits strong multi-target kinase inhibition, particularly against SRC, supporting its potential as a pan-cancer therapeutic candidate. Full article

Oral squamous cell carcinoma (OSCC) accounts for most head-and-neck cancers, and effective biological adjuvants remain limited. Gingival mesenchymal stem cells (GMSCs) exhibit anti-tumour paracrine activity, but the underlying molecular mechanisms and their relevance in patient cohorts remain incompletely understood. Consensus apoptosis–reactive oxygen species (ROS) effectors were identified through integrated transcriptomic analyses of TCGA-HNSC and three GEO cohorts. Candidate genes were evaluated in primary OSCC cells exposed to GMSC-conditioned medium or indirect Transwell co-culture. Findings were further examined using patient-cohort validation, single-cell ligand–receptor analysis, pathway and transcription-factor activity inference, and drug-repurposing approaches. Computational analyses identified an apoptosis–ROS network centred on BAX, BCL2, CASP3, CASP9, NOX1, and GPX1. Indirect GMSC co-culture reduced intracellular ROS, increased early apoptosis, and induced G2/M accumulation, whereas conditioned medium produced inconsistent effects, suggesting a requirement for live bidirectional paracrine signalling. BAX was the only consistently up-regulated effector. The axis demonstrated concordant differential expression across independent HPV-negative OSCC cohorts but was not independently prognostic under leakage-free cross-validation or external validation. Pathway analyses supported ROS suppression, apoptosis activation, and altered stromal–tumour communication. Drug-repurposing analyses identified HSP90 inhibitors and the FDA-approved TOP2 inhibitor mitoxantrone as candidate therapeutic agents. GMSC paracrine activity targets a biologically interpretable apoptosis–ROS axis in OSCC that is reproducibly expressed across patient cohorts but does not constitute an independent prognostic biomarker. The identified therapeutic candidates warrant further experimental investigation. Full article

Background: Acetylcholinesterase (AChE) is a key therapeutic target in neurological disorders, and the discovery of novel inhibitors with improved efficacy and pharmacokinetic properties remains a significant challenge. Methods: In this study, an integrated computational and experimental approach was employed to identify potential AChE inhibitors. A machine learning-based model was developed to predict bioactive compounds from large chemical libraries, followed by Blood–Brain Barrier (BBB) permeability screening to ensure Central Nervous System (CNS) suitability. The shortlisted compounds were further evaluated using molecular docking, molecular dynamics simulations, and MM-PBSA binding free-energy calculations to assess their interaction profiles and stability. Selected top-ranked compounds were subjected to in vitro biological evaluation for AChE inhibitory activity. Results: The results demonstrated that several screened compounds, including Z1498348710 and Z1824281875, exhibited notable inhibition with AChE activity reduced to approximately 75% and 72%, respectively, compared to the control. Other compounds such as Z29542160, Z105150208, Z94570687, and Z94570675 showed moderate inhibitory effects, maintaining AChE activity in the range of 82–86%. In comparison, the reference inhibitors Donepezil and Neostigmine bromide displayed significantly stronger inhibition, reducing AChE activity to approximately 20% and 18%, respectively. Conclusions: Overall, the identified compounds demonstrated moderate AChE inhibitory activity while exhibiting favorable predicted physicochemical and computational profiles. These findings suggest that they represent promising starting points for medicinal chemistry optimization and future development as CNS-active AChE inhibitors. Full article

Tyrosyl-DNA phosphodiesterase 1 (TDP1) represents a compelling pharmacological target for the development of agents designed to circumvent tumor resistance to topoisomerase 1 (TOP1) inhibitors, a major class of clinically relevant antineoplastic drugs. This paper describes the design and synthesis of novel hybrid TDP1 inhibitors combining coumarin and monoterpene moieties via rigid isoxazole and 1,2,3-triazole heterocyclic linkers. The synthesis was accomplished via [3 + 2] cycloaddition of nitrile oxides to alkynes and copper-catalyzed click chemistry. Biological tests have demonstrated the crucial role of linker nature in the activity of the compounds. Isoxazole-linked conjugates showed strong inhibitory effects on TDP1, with IC₅₀ values in the submicromolar to low micromolar range (0.8–3.2 μM). Overall, these values slightly surpassed those of the triazole-linked analogues, whose IC₅₀ values ranged from 1.1 to 23.3 μM. At noncytotoxic doses, compounds 26e and 16b enhanced the sensitivity of human cervical cancer (HeLa) cells to the antitumor agent topotecan, a TOP1 inhibitor, thereby supporting the promise of this structural class as components of combination chemotherapy. Full article

Alzheimer’s disease remains one of the hardest disorders to drug, and most computational pipelines still tackle one target at a time and ignore brain penetration until late. We bring all of that into a single optimization problem. Our framework, called CAGE-QMol(Constraint-Aware Quantum-inspired Molecular optimization), turns the search for a brain-penetrant, dual BACE1/AChE inhibitor into a constrained quadratic unconstrained binary optimization (QUBO) and solves it with an ensemble of classical, quantum-inspired, and Quantum Approximate Optimization Algorithm (QAOA) backends. The pipeline begins with three real public datasets—MoleculeNet BACE, ChEMBL CHEMBL4822 (BACE1) and CHEMBL220 (AChE), and the TDC BBB_Martins blood–brain-barrier set—which together yield 12,465 unique molecules with at least one measured endpoint. Multi-task ExtraTrees predictors trained on Morgan ECFP4 fingerprints and physicochemical descriptors deliver scaffold-split test-set metrics of $R^2=0.624$ ($\mathrm{MAE}=0.587$) for BACE1 and $R^2=0.383$ ($\mathrm{MAE}=0.793$) for AChE. The optimizer combines these predictions with a Lipinski-based feasibility cone, a TDC-derived BBB classifier, and similarity-driven diversity into a constrained QUBO. We adapt the classical exact-penalty rule, $\lambda >{\Delta }_S/{\delta }_g$, which guarantees every global minimizer of the penalized energy is feasible, and specialize it so that the multiplier is computed from the data rather than hand-tuned. A 10-qubit PennyLane QAOA circuit is benchmarked against exact enumeration, simulated annealing, genetic search, Bayesian TPE, and random search across ten seeds; the QUBO formulation lets a genetic solver match the exact ground state on every seed, while ablations show that removing the QUBO selection collapses the mean therapeutic score from 6.242 to 5.976 ($p<{10}^{-3}$, paired t-test). Top-50 candidates exhibit a mean BBB probability of 0.716, a mean QED of 0.768, and 100% Lipinski feasibility, with leading scaffolds (tetrahydroisoquinolinone, methoxy-tetrahydronaphthalene-urea, indanone-piperidine) reproducing motifs found in published dual BACE1/AChE inhibitor families. This paper contributes (i) a mathematically grounded penalty selection rule for constrained drug-discovery QUBOs, (ii) a single end-to-end pipeline from raw public data to ranked, 3D-embedded leads, and (iii) reproducible head-to-head benchmarks between classical, quantum-inspired, and QAOA-simulated optimizers on a real Alzheimer’s task. Full article

Objectives: Malaria remains a major global health burden, particularly in endemic regions such as Djibouti, where Plasmodium falciparum and Plasmodium vivax co-circulate, complicating disease control strategies. Increasing resistance to current antimalarial drugs reduces treatment effectiveness and highlights the urgent need for new, safe, and affordable therapeutic agents. This study aimed to identify potential inhibitors from Djiboutian medicinal plants using an integrated in silico approach targeting key proteins from both parasite species. Methods: A library of 222 phytoconstituents was screened against Plasmodium vivax FK506-binding protein 35 (PDB ID: 3IHZ) and Plasmodium vivax dihydrofolate reductase–thymidylate synthase (PDB ID: 1J3K) using molecular docking. Top-ranked compounds were further analyzed for binding interactions and evaluated for drug-likeness and pharmacokinetic properties using QikProp in Maestro v11.5. Selected protein–ligand complexes were subjected to 100 ns molecular dynamics simulations, and their stability was assessed using multiple descriptors, including structural deviation, flexibility, compactness, solvent exposure, and hydrogen bond persistence. Results: Several phytoconstituents exhibited strong binding affinities, with docking scores ranging from −6.09 to −7.54 kcal/mol, outperforming the reference drug artemisinin. Interaction analysis revealed key hydrogen bonds and hydrophobic contacts with essential active-site residues. ADMET predictions indicated favorable pharmacokinetic profiles, including high oral absorption, good membrane permeability, and low predicted toxicity. Molecular dynamics simulations demonstrated stable behavior for most complexes, with compound 121 showing enhanced stability in the 1J3K system and compound 123 exhibiting consistent dynamic stability in the 3IHZ system. In contrast, compound 82 displayed greater structural fluctuations despite maintaining stable hydrogen bond interactions. Conclusions: The integration of molecular docking, ADMET prediction, and molecular dynamics simulations identified compounds 121 and 123 as the most promising antimalarial candidates, exhibiting an optimal balance of binding affinity, favorable pharmacokinetic properties, and dynamic stability. These findings highlight the potential of Djiboutian medicinal plants as a valuable source of novel antimalarial agents and provide a strong computational foundation for future experimental validation. Full article

Background: Benzo[a]pyrene (BaP), a common food contaminant, is a recognized gastric carcinogen. This study aimed to identify therapeutic targets and repurposed drugs for gastric cancer (GC) using BaP as a network toxicology query. Methods: An integrated strategy combining network toxicology, multi-omics, machine learning (Random Forest, LASSO, SVM-RFE), and experimental validation was applied. Results: By intersecting GC-associated genes with BaP-related targets and machine learning, we identified three hub genes. The logistic regression model further revealed KCNE2 as a protective factor (OR = 0.515, 95% CI: 0.383–0.692), while SULF1 (OR = 2.940, 95% CI: 1.399–6.179) and TIMP1 (OR = 5.351, 95% CI: 2.020–16.743) were identified as potential risk factors. Survival analysis confirmed their prognostic significance. Single-cell transcriptomics descriptively showed TIMP1 and SULF1 enrichment in malignant/stromal cells and fibroblasts, respectively, whereas KCNE2 was restricted to normal epithelial cells and silenced in tumors. GSVA implicated epigenetic regulation, ECM remodeling, and TGF-β signaling. Molecular docking and dynamics simulations suggested that BaP can form stable complexes with DNMT1 and DNMT3A. Accordingly, drug enrichment analysis identified DNMT inhibitor 5-azacytidine as a top candidate. Cellular experiments confirmed that 5-azacytidine selectively inhibited GC cells and was associated with modulation of the DNMT3A–KCNE2 axis. Conclusions: Our findings provide a novel molecular target and a repurposed drug for GC from the perspective of a food contaminant. Full article