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.

Accurate subtyping of lung cancer is essential for improving patient prognosis and enabling personalized treatment. However, current clinical techniques are often time-consuming and heavily dependent on the operator's subjective judgment and experience, which limits the accuracy and timeliness of intraoperative subtype diagnosis and margin assessment. In this study, we developed an intelligent diagnostic model by integrating Fourier transform infrared (FTIR) spectroscopy with a Random Forest (RF) classifier. A total of 210 tumor and adjacent tissue samples from 105 patients, including adenocarcinoma, squamous cell carcinoma, and benign lung tumors were analyzed. The constructed RF model achieved an accuracy of 97.95% with an Area Under the Curve (AUC) of 0.99 in binary classification (lung cancer vs. adjacent tissues), and an accuracy of 94.91% in multiclass classification of lung cancer subtypes, significantly outperforming conventional algorithms such as Support Vector Machine, Naive Bayes, and Logistic Regression. In addition, spectral analysis methods, including peak area comparison, peak fitting, and second derivative analysis, revealed distinct differences in nucleic acids, proteins, and lipids, highlighting the characteristic bands responsible for subtype discrimination and providing spectroscopic insights into the pathological features of different lung cancer subtypes. Collectively, our findings demonstrate that the diagnostic model is a powerful approach for distinguishing lung cancer tissues from normal tissues and for subtype classification, offering a promising tool for lung cancer diagnosis.

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.

Developing effective cancer treatments requires identifying novel therapeutic agents with high biological activity. Fucoidan, a sulfated polysaccharide from brown algae, is a promising natural scaffold for anticancer drug development. In this study, oversulfated fucoidans (SFU and SFU-1) were derived from natural fucoidans (FU and FU-1), and the effects of this structural modification on anticancer efficacy were investigated comprehensively. Chemical characterizations of FU/FU-1 and SFU/SFU-1 were performed, and a systematically generated experimental dataset across multiple cancer cell lines was compiled. Using these data, several machine learning (ML) algorithms were applied to predict the anticancer efficacy of fucoidan-based molecules. Specifically, k-Nearest Neighbors Regression (kNNR), Least-Squares Boosting (LSBoost), Support Vector Regression (SVR), Decision Tree Regression (DT), Random Forest Regression (RFR), Linear Regression (LR), and Gaussian Process Regression (GPR) were evaluated with five-fold cross-validation. Model performance was assessed utilizing the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). The results show that FT-IR analysis of the oversulfated derivatives, SFU and SFU-1, confirmed successful modification. For SFU, the appearance of a shoulder peak at 820 cm-1 (equatorial C-2), alongside the characteristic 840 cm-1 peak, verified site-specific sulfonation. In SFU-1, new peaks at 1243 cm-1 (SO stretching) and 583 cm-1 (OSO deformations) were identified. These spectral changes demonstrate the effective integration of sulfate groups into the molecular frameworks. Cytotoxicity assays against five human cancer cell lines revealed dose-dependent inhibition, with the SFU derivative exhibiting the most potent activity, particularly reducing HeLa and MDA-MB-231 cell viability to 23.93% and 25.13% at 2 mg/mL. GPR model achieves superior predictive performance compared to other methods, with the lowest MAE and RMSE (8.4627 and 11.5692, respectively) and the highest R2 (0.7039) values. The findings reveal that models that capture the nonlinear relationship between sulfation degree and anticancer efficacy, especially GPR, are powerful tools for the preliminary evaluation of natural product-based drug candidates. This study demonstrates that integrating chemical modification, experimental validation, and classical ML can accelerate the rational assessment of naturally derived therapeutics in oncology.

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.

Analyzing gene expression data is essential for predicting and detecting diseases, including cancer. The data is very repetitive and noisy, which makes it hard to find important information about illnesses. In the past decade, several traditional machine learning and feature selection models have been developed for cancer type classification from gene expression data. Rather than introducing new deep learning primitives, this work presents a principled integration framework that combines sparsity-aware representation learning, structure-inducing spatial embedding, and hierarchical multi-scale attention. This paper presents a method for cancer gene classification based on Hierarchical Attention Assisted Feature Pyramid Network (HA-FPN). The work involved two publicly available datasets. The proposed methodology starts with dimensionality reduction through a Variational Sparse Autoencoder (VSAE), followed by an updated DeepInsight algorithm for image conversion of the input. Next, the classification technique is constructed using the proposed HA-FPN model. Moreover, the improved gradient descent optimization (IGDO) is utilized to change the hyperparameter of the classification model. In addition, the results demonstrate that the model combined with an IGDO outperforms the currently existing methods in terms of accuracy, precision, recall, and F1-score. The method that is presented efficiently brings out different aspects of the data through t-SNE computations. Moreover, the proposed approach is very robust, and hence, it can reach high performance levels on two different datasets.

Computed tomography (CT) is essential for finding and diagnosing kidney tumors and cysts because good lesion segmentation enables accurate diagnosis, appropriate therapy planning, and disease monitoring. Renal mass (Tumor and cyst) shapes and sizes are diverse and complex, particularly around diffuse borders, and low-intensity contrast and heterogeneous morphology make effective segmentation difficult. To overcome these challenges, introduced internal dilated guided self-attention model based on dilated residual inception U-Net (IDGSA-DRIU-Net), a deep learning model for segmenting renal tumors and cysts. The architecture features a newly built Internal Dilated Guided Self-Attention (IDGSA) module, which combines Dilated Multiscale Position Attention and Channel Attention introducing an attention mechanism that leverages internal guidance to capture spatial dependencies across multiple dilation scales and emphasizes key feature channels, allowing for more effective utilization of local and global contextual information for better multi-scale feature aggregation. A Dilated Residual Inception (DRI) module improves multiscale contextual feature recovery while preserving structural features. In post-processing, a Conditional Random Field (CRF) is utilized to eliminate false positives and refine bounds. The proposed model was evaluated using the KiTS19, KiTS21, and KiTS23 datasets and obtained a Dice Similarity Coefficient (DSC) of 98.27% for the tumor on KiTS19, 96.31% and 94.82% for the tumor and cyst on KiTS21, and 94.98% and 93.64% for the tumor and cyst on KiTS23. The results suggest that IDGSA-DRIU-Net with CRF outperforms the popular state-of-the-art models, indicating that successful in kidney tumor and cyst segmentation.

Hypoxia is a distinctive characteristic of solid tumors, limiting the efficiency of photodynamic therapy (PDT). In this study, transcriptomics was applied to analyze the changes in mRNA levels in mouse tumors treated with multiple photodynamic therapy sessions, revealing that 12 genes in the PI3K/mTOR pathway were significantly upregulated. In order to improve delivery efficiency and enhance the efficacy of PDT photosensitizers, a nitroreductase-responsive BODIPY prodrug (BNP) was designed and employed to encapsulate a PI3K/ mTOR dual inhibitor (BEZ235) to self-assemble into nanoparticles (BNP@BEZ NPs). The fluorescence level of BNP@BEZ NPs in hypoxic cells was 12.20-fold that in normoxic cells because of the responsiveness of nitrobenzyl groups to hypoxia. Such minimized off-target effects also resulted in preferential intratumoral activation of prodrugs. Meanwhile, BEZ co-loading alleviated tumor hypoxia and increased intracellular ROS levels to 155.26% compared to blank prodrug nanoparticles. BNP@BEZ NPs-mediated PI3K/mTOR inhibition and apoptosis of tumor cells exhibited sufficient antitumor efficacy with a tumor inhibition rate of 93.31%. In conclusion, this study confirms that the PI3K/mTOR pathway promotes tumor PDT resistance by enhancing mitochondrial respiration. The hypoxic -responsive photodynamic properties of BNP, combined with BEZ's oxygen consumption-inhibiting capacity, can enhance tumor suppression rates. This strategy of nanomedicine development by capitalizing on the synergistic effect between active ingredients will avoid the low efficacy and adverse effects of monotherapies.

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.

To investigate the potential risk of nicotine exposure on glioblastoma multiforme (GBM). A two-sample Mendelian randomization (MR) approach was employed to assess the causal association between serum cotinine levels, a metabolite of nicotine, and GBM. GBM datasets were obtained from the GEO database, and differentially expressed genes were analyzed. Nicotine-related targets were screened using databases such as BATMAN and CTD, followed by gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and protein interaction analyses for core targets. Machine learning, GSEA analysis, GEPIA2, HPA database, and CNGB single-cell sequencing database were employed to screen and validate core targets. CB-Dock2 and iMODS were used for molecular docking and structural dynamics analysis to validate screening results. MR analysis revealed a causal relationship between serum cotinine and GBM. Network toxicology analysis identified 194 potential target genes, with KEGG analysis indicating pathways related to viral infection, immunity, cancer, and metabolism. Machine learning identified core targets including NFκB1, HIF1α, CD4, FN1, MMP2, and GSK3β, whose mechanisms were validated through GSEA, GEPIA2, HPA and CNGB databases, molecular docking, and structural dynamics analysis. This study employed multiple methodologies to investigate the association between genetically predicted serum cotinine levels and GBM risk, identifying core targets including NFκB1, HIF1α, CD4, FN1, MMP2, and GSK3β. These findings provide novel insights for future research on the association between nicotine-related exposure and GBM risk.