Find the papers that actually matter

Nanozymes, engineered nanomaterials with enzyme-mimetic activities, have emerged as versatile platforms for ferroptosis-based cancer theranostics. Ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, has emerged as a promising strategy to overcome resistance to conventional cancer therapies. By catalyzing redox reactions, nanozymes can generate reactive oxygen species (ROS) and promote ferroptotic lipid peroxidation, thereby triggering cell death in tumor cells that evade apoptosis-based treatments. In parallel, non-redox activities of nanozymes, including hydrolase- and phosphatase-like functions, enable them to remodel the tumor microenvironment (TME), modulate biomolecular signaling, and support targeted therapy. This review provides a systematic and design-oriented overview of nanozymes that interface with ferroptosis. We summarize how redox and non-redox nanozyme activities converge on key ferroptosis-related processes, such as ROS production, glutathione depletion, iron metabolism disruption, and TME regulation. We then highlight rational engineering strategies, including single-atom and multimetallic catalytic centers, biodegradable coordination frameworks, stimuli-responsive architectures, and protein corona engineering, that enhance catalytic specificity, tumor targeting, and biosafety. Theranostic implementations are discussed with emphasis on multimodal imaging-guided platforms and combination regimens that integrate chemotherapy, radiotherapy, phototherapy, and immunotherapy. Finally, we outline major translational challenges and future opportunities, including AI and computation-guided nanozyme design and adaptive, corona-informed systems tailored for personalized cancer therapy. This review aims to serve as a roadmap for developing clinically translatable nanozymes that unify diagnosis and treatment through ferroptosis-oriented precision oncology.

Carotid plaque instability is a major determinant of ischemic stroke and is characterized by heightened inflammation and structural remodeling of the vessel wall. Although macrophages and vascular smooth muscle cells (VSMCs) are central to plaque vulnerability, the mechanisms coordinating immune activation with vascular remodeling remain incompletely understood. Bulk transcriptomic data from multiple Gene Expression Omnibus (GEO) datasets were integrated to compare unstable and stable carotid plaques. Differential gene expression analysis, weighted gene co-expression network analysis, immune-gene curation, and machine learning methods (least absolute shrinkage and selection operator [LASSO] and random forest) were used to identify key genes and construct a nomogram. The findings were validated using independent datasets, single-cell RNA sequencing, and human carotid plaque specimens. The mechanistic roles of CXCL16 were examined using macrophage functional assays, NF-κB inhibition, VSMC co-culture with macrophage-conditioned media, and the establishment of an ApoE-/- carotid atherosclerosis model with local CXCL16 suppression. Cell-cell communication and pseudotime analyses were performed to explore macrophage-VSMC interactions. CXCL16, CCL2, and MMP9 were consistently upregulated in unstable plaques and showed robust diagnostic performance across datasets. A three-gene nomogram generated from this study suggested potential clinical utility. Single-cell analyses indicated that CXCL16 was enriched in plaque-associated M1 macrophages and was associated with inflammatory activation states. Human plaque staining confirmed higher CXCL16/CCL2/MMP9 expression in unstable plaques with increased macrophage and leukocyte infiltration. In vitro, CXCL16 knockdown attenuated NF-κB activation and reduced downstream inflammatory mediators (including CCL2), accompanied by decreased macrophage migration; NF-κB inhibition phenocopied these effects. In vivo, CXCL16 suppression reduced carotid plaque formation and inflammatory cell infiltration. Cell-cell communication analysis revealed enhanced SPP1/osteopontin signaling from M1 macrophages toward VSMCs, with higher SPP1 expression in CXCL16-high M1 macrophages. Co-culture experiments showed that macrophage-derived CXCL16 promoted VSMC migration and phenotypic switching, which was reversed by CXCL16 knockdown. CXCL16 acts as a central inflammatory mediator in carotid plaque destabilization by promoting NF-κB-dependent macrophage activation and migration. It also enhances SPP1/osteopontin-associated macrophage-VSMC crosstalk that drives phenotypic remodeling in VSMCs. Our results collectively suggest CXCL16 as a diagnostic biomarker and a potential therapeutic target for carotid atherosclerosis.

Glioblastoma (GBM) remains lethal due to high molecular heterogeneity and treatment resistance. While previous studies have proposed various biomarkers, a critical research gap exists: the lack of robust algorithmic validation and systematic linkage to drug discovery. Existing research predominantly relies on single machine learning models or traditional statistics, which often fail to provide stable results across diverse clinical datasets. To address this, we developed a high-dimensional pipeline that compares and ensembles 175 machine learning algorithm combinations. Unlike conventional single-model workflows, this approach ensures superior target identification stability and utilizes SHAP-based explainability and molecular dynamics to bridge the gap between biomarker discovery and precision therapeutics. DEGs from GEO datasets were refined via PPI and functional analyses. The 175-algorithm ensemble identified core genes, with clinical utility validated via survival analysis. A drug discovery pipeline incorporating virtual screening, ADMET, and molecular dynamics (MD) was then implemented to evaluate compounds targeting the identified core genes. From 771 DEGs, 34 key genes were identified, with LOX validated as the core therapeutic target. The optimal predictive model achieved a robust AUC of 0.953, while survival analysis underscored the significant prognostic value of LOX. Following systematic screening, the most outstanding compound was prioritized via MD simulations, exhibiting exceptional binding stability, favorable pharmacokinetics, and minimal toxicity risk. This integrated pipeline provides a robust framework for identifying precision targets and potent candidate compounds, offering a novel strategy for overcoming GBM treatment barriers.

Triple-negative breast cancer (TNBC) is an aggressive malignancy often characterized by chemoresistance, partly due to the overexpression of drug efflux transporters like ABCG2. To address this challenge, this study developed and evaluated a cRGD-modified, pH-sensitive liposomal system for the targeted co-delivery of docetaxel (DTX) and siRNA against ABCG2 (si-ABCG2). The synthesized nanoparticles (DTX/siRNA/cRGD-PLPs) exhibited optimal physicochemical properties, including a mean particle size of approximately 241.7 nm, efficient co-loading of DTX and siRNA, and pH-responsive cargo release, while protecting the siRNA from degradation in serum. Also, DTX/siRNA/cRGD-PLPs maintained homogeneous size distributions over the storage period and induced minimal hemoglobin release, with hemolysis rates remaining below safety threshold. These liposomes demonstrated enhanced, time-dependent uptake into TNBC cell lines HCC1937 and MDA-MB-231. In vitro, the DTX/siRNA/cRGD-PLPs formulation was significantly more effective at inhibiting cell viability, proliferation, migration, and invasion, and at inducing apoptosis, compared to the free drug combinations and other controls. Moreover, the dual-payload co-delivery liposomes (DTX/siRNA/cRGD-PLPs) exerted superior anti-tumor effects relative to single-agent formulations. In an MDA-MB-231 xenograft mouse model, the liposomal treatment was well-tolerated and resulted in marked tumor growth inhibition, which was associated with reduced cell proliferation (Ki67) and increased apoptosis (Caspase-3) within the tumor tissue. This targeted co-delivery system shows significant potential for improving TNBC treatment by synergistically enhancing therapeutic efficacy and overcoming chemoresistance.

Histone deacetylase 2 (HDAC2) plays a critical role in the pathogenesis of diffuse large B-cell lymphoma (DLBCL), positioning it as an attractive therapeutic target. In this study, we applied an integrated computational strategy that combined machine learning-based quantitative structure-activity relationship (QSAR) modelling, molecular docking, ADMET filtering, and molecular dynamics (MD) simulations to identify and optimize potential HDAC2 inhibitors. A dataset of 1995 HDAC2-active molecules was assembled and reduced to 25 key molecular descriptors, enabling the development of a robust Random Forest QSAR model (R2 = 0.926, CCC = 0.956). Top-predicted compounds underwent docking and pharmacokinetic evaluation, highlighting compound 10 as a promising lead (binding energy ≈ -10.2 kcal·mol-1). Guided optimization produced analogue 10 g, which displayed enhanced affinity (-10.9 kcal·mol-1), stable protein-ligand interactions in MD simulations, and favourable MM-GBSA binding free energy (ΔG_bind ≈ -40.3 kcal·mol-1). In silico pharmacokinetic analysis further suggested acceptable safety and drug-likeness. This multi-stage pipeline prioritizes 10 g as a strong candidate for experimental validation and demonstrates the value of integrating predictive modelling with structure-based refinement in anti-lymphoma drug discovery.

The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced drug discovery. The current study employed a robust computational pipeline integrating Machine Learning (ML) algorithms, QSAR modeling, molecular docking, molecular dynamics (MD), density functional theory (DFT), and network pharmacology to identify novel Anaplastic Lymphoma Kinase (ALK) tyrosine kinase inhibitors. An initial library of 3546 compounds from the CHEMBL4247 database was systematically filtered to 578. This screening utilized Lipinski's rule of five, aided by QSAR and detailed PaDEL descriptor analysis. An ensemble ML model, specifically a Voting Classifier (VC) combining XGBoost, LightGBM, and ExtraTrees algorithms, attained high predictive accuracy (ROC-AUC = 0.99), facilitating a strong classification and prioritization of active leads. Molecular docking experiment identified five top hit ligands (60, 63, 124, 130, 204) having docking score ranging from -9.0 to -10.4 kcal/mol and also confirmed their strong binding affinities, which surpassed the native co-crystallized ligand used as a standard. Later on, ADMET studies were executed to explore their physicochemical properties. MD simulation trajectories and MM/PBSA analyses validated the notably conformational stability and favorable binding free energies of these hit complexes. Network pharmacology was incorporated to understand tentative mechanisms of action and potential off-targets, generating a protein-protein interaction (PPI) network. DFT-based frontier molecular orbital (FMO) analysis showed Ligand124 possessed the highest electrophilicity and optimal polarizability, consistent with its marked interaction stability in MD simulations. In addition, the molecular mechanisms of hit compounds against TC were elucidated using a network pharmacology approach, which revealed a compound-target network with crucial hub targets like AKT1 and TP53. Significant correlations with cancer-related pathways, such as PI3K-Akt and MAPK signaling, as well as key involvement in kinase activity, phosphorylation, and membrane signaling complexes, were observed by the enrichment analysis of the main targets. These comprehensive results imply that investigated hit compounds probably modulate the oncogenic signaling networks, especially those controlling cell survival, proliferation, and drug resistance, in order to achieve its anti-TC therapeutic actions. These findings highlight the fundamental ability of integrating ML and computational chemistry to accelerate therapeutic development for TC.

Lung adenocarcinoma (LUAD) is the predominant pathological subtype of non-small cell lung cancer. Its considerable tumor heterogeneity and drug resistance present major clinical obstacles, resulting in unfavorable patient outcomes. Protein palmitoylation is known to be a key factor in tumorigenesis; however, its cell-specific expression patterns and prognostic value in LUAD remain incompletely characterized. Using two independent datasets, TCGA-LUAD and GSE68465, and scRNA-seq data from 10 LUAD samples, we analyzed the expression of palmitoylation-related genes. Through single-cell clustering, CNV analysis, and palmitoylation activity scoring, malignant epithelial cells were identified. 10 machine learning algorithms were applied to construct prognostic models based on differentially expressed genes. RT-qPCR was used to detect mRNA expression of prognostic marker genes in clinical samples. In vitro experiments validated SEC61G's role in regulating drug sensitivity. A subset of malignant epithelial cells with high palmitoylation activity was identified. A 5-gene signature (UBE2S, SEC61G, CCT6A, GAPDH, HLA-DRA) was established by the integrated CoxBoost+SuperPC method, showing robust predictive efficacy in both GSE68465 and TCGA-LUAD. High-risk samples carried higher mutation burden, greater genomic heterogeneity, and a stronger tumor immunosuppressive microenvironment than the low-risk group. Clinical sample testing revealed upregulation of UBE2S, SEC61G, CCT6A, and GAPDH in LUAD patients and downregulation of HLA-DRA. SEC61G expression inversely correlated with AZD3759 sensitivity. In vitro, SEC61G knockdown or AZD3759 alone suppressed LUAD proliferation and induced apoptosis; no synergy was observed with combination therapy, indicating that SEC61G modulates AZD3759 sensitivity in LUAD cells. Our study comprehensively reveals the cellular heterogeneity of palmitoylation, establishes a robust palmitoylation-related prognostic model, and identifies SEC61G as a promising therapeutic target in LUAD, offering a novel perspective for LUAD precision stratification and treatment studies.

Breast cancer (BC) is one of the most common malignancies in women globally, characterized by significant genetic and clinical heterogeneity. This complexity emphasises the need for reliable biomarkers and novel therapeutic strategies to improve patient outcomes.To address this, the present study introduces an integrated computational pipeline using multiple datasets to identify robust biomarkers and potential repurposed drugs in BC. From transcriptomic profiles across 12 GEO datasets, 143 differentially expressed genes (DEGs) were identified. Gene Ontology and functional enrichment analyses were performed, followed by the construction of a protein-protein interaction (PPI) network to pinpoint hub genes. These hubs were prioritised using multiple topological centrality measures and validated with independent datasets (cBioPortal, GEPIA, KM plotter) and machine-learning classification using five algorithms: Random Forest, XGBoost, Support vector machine, K-nearest neighbour, and Logistic regression. Machine-learning models achieved test accuracies > 0.90 on TCGA-BRCA data (n = 1203), with K-nearest neighbour (class-weighted: accuracy 0.954) and XGBoost (SMOTE: accuracy 0.931) showing the strongest performance. Prioritised hub genes, TPX2 and BUB1B, were subjected to virtual-screening across five drug databases (DrugBank, DGIdb, OpenTargets, SwissTargetPrediction, and GSCALite), combined with molecular-docking, ADMET profiling, and drug-likeness evaluation, to identify promising repurposed candidates. Vorinostat exhibited the highest binding affinity to TPX2 (-29.32 kcal/mol) and BUB1B (-23.71 kcal/mol), followed by BRD-K90370028 (-18.40 kcal/mol on BUB1B), NSC19630 and Dasatinib (with consistent dual-target binding), CD-437, and four additional prioritised compounds that exhibited favourable interactions. In conclusion, this coherent transcriptomics-to-therapeutics workflow establishes TPX2 and BUB1B as strong prognostic biomarkers in BC, with promising repurposed drugs targeting these mitotic regulators.

Cancer metastasis accounts for about 90% of cancer-related mortality, but is difficult to predict. In particular, distant metastasis is more difficult to predict by a learning method than lymph node metastasis due to the limited amount of data available for training a model and the inherent complexity of distant metastasis. Predicting distant metastatic sites from a primary cancer is even more difficult than predicting whether or not distant metastasis will occur. We developed a deep learning model called a perturbed multilayer perceptron (PMLP) to predict distant metastatic sites using expression levels of competing endogenous RNAs and their correlations at the primary site of cancer samples. In independent testing of PMLP on datasets which were not used in training, it showed high predictive performance (average AUC of 0.99, accuracy above 96%, and F1 scores above 0.91) in all metastatic sites. In comparison of the model with other state-of-the-art methods, our model showed a better performance. This model along with the explanation functionality of its prediction results can be used as useful aids to predict potential distant metastatic sites from gene expressions at the primary sites of cancer. To the best of our knowledge, this is the first study to employ PMLP combined with ceRNA correlation changes (ΔSCCs) for predicting specific distant metastatic sites, showing superior predictive performance with model interpretability.

To develop and externally validate a multicenter MRI-based machine learning model integrating intra-tumoral heterogeneity (ITH) index, conventional radiomics (C-radiomics) and clinical variables for predicting one-year pulmonary metastasis (PM) in osteosarcoma. This retrospective study enrolled 320 patients with histologically confirmed osteosarcoma from four institutions, comprising internal (n = 254, Centers A-C) and external sets (n = 66, Center D). Pre-treatment contrast-enhanced T1-weighted fat-suppressed MRI was used for tumor segmentation and feature extraction. ITH features were obtained through supervoxel-based clustering, and C-radiomics features were derived conventionally. An XGBoost model integrating ITH index, C-radiomics, and clinical variables was developed. Model performance was evaluated using ROC, calibration, and decision curve analysis (DCA), with SHAP and subgroup analysis providing interpretability and robustness. Within one year after surgery, 39.4% of patients developed PM. The combined model achieved the highest predictive performance across sets, with an AUC of 0.843 (95% CI: 0.823-0.869), 73.8% accuracy, 78.2% sensitivity, and 81.1% specificity on the independent external test set, outperforming all single- and dual-modality models. Calibration and DCA confirmed strong model reliability and clinical utility across a broad threshold range. The ITH index (OR = 6.723, p = 0.008) and C-radiomics score (OR = 7.962, p = 0.001) were independent predictors of PM. Subgroup analysis demonstrated consistent performance across age, sex, stage, and tumor site (AUC range: 0.801-0.853). The MRI-based model integrating ITH, C-radiomics, and clinical variables enables accurate, noninvasive prediction of early PM in osteosarcoma, supporting personalized risk stratification and clinical decision-making.