Find the papers that actually matter

Chemodynamic therapy (CDT) based on overproduced reactive oxygen species (ROS), activates cytoprotective autophagy, an inexorable phenomenon-that enables tumor cell survival, therefore attenuating ROS-induced therapeutic efficacy. Herein, we develop a tumor microenvironment (TME)-responsive nanoplatform (MnOx-GOx-PM@Ma) composed of manganese oxide nanoflowers (MnOx NFs) co-loaded with glucose oxidase (GOx) and an activatable melittin pro-peptide (PM), and coated with macrophage membranes (Ma) for targeted delivery. Combined MnOx NFs and GOx trigger O2/H2O2 cyclic generation, thereby amplifying CDT in the acidic and glutathione (GSH)-rich TME. Meanwhile, the PM is selectively cleaved by lysosomal legumain to activate melittin, which disrupts lysosomal membranes and converts cytoprotective autophagy into a pro-death process. Additionally, the releasing Mn2+ exhibits excellent magnetic resonance imaging (MRI) contrast properties. Both in vitro and in vivo studies demonstrate that MnOx-GOx-PM@Ma effectively suppresses tumor growth through synergistic starvation therapy, enhanced CDT, and autophagy inhibition. Collectively, this work presents a strategy to overcome autophagy-mediated therapeutic resistance and optimize synergistic CDT-based antitumor therapy.

Multi-modal imaging guided photothermal therapy (PTT) combined with photodynamic therapy (PDT) has shown significant advantages in breast cancer treatment. However, the development of superior phototheranostic nanoagents remains highly challenging. Herein, a "one-for-all" phototheranostic nanoagent was developed based on a fused-ring small molecule (L8-4F), which was functionalized with outer butyloctyl branched side chains for poor molecular planarity and loose intermolecular π-π stacking. The water-soluble L8-4F nanoparticles (NPs) prepared by the nano-precipitation method not only exhibited strong near-infrared I (NIR-I) absorption, a large Stokes shift (108 nm), and near-infrared II (NIR-II) emission in the range of 835-1200 nm, but also achieved a photothermal conversion efficiency (PCE) as high as 58 % under 808 nm laser irradiation, while simultaneously generating type-I superoxide radicals (•O₂-). In animal experiments, high-resolution vascular imaging and accurate tumor localization were achieved in 4T1 tumor bearing mice using L8-4F NPs via integrated fluorescence imaging (FLI) and photoacoustic imaging (PAI). Moreover, the synergistic combination of photothermal therapy (PTT) and type-I photodynamic therapy (PDT) provided effective strategy for breast cancer treatment. In general, this study presented valuable guidance for constructing efficient "one-for-all" phototheranostic nanoagent.

Aristolochic acids (AAs) are established human carcinogens strongly associated with upper tract urothelial carcinoma (UTUC). However, the multi-target oncogenic network beyond their genotoxic mechanism remains incompletely elucidated. This study employed an integrated computational approach combining network toxicology, machine learning, molecular docking, and molecular dynamics (MD) simulations to systematically explore the potential molecular mechanisms of AA-induced UTUC. We identified 97 shared potential targets of AAs and UTUC. Enrichment analyses revealed their significant involvement in lipid metabolism, xenobiotic detoxification, and cancer-related pathways such as PI3K-Akt signaling. Topological analysis of the protein-protein interaction network and a nested cross-validation machine learning model highlighted five core genes: CASP3, EGFR, PARP1, PTGS2, and HSP90AA1. Molecular docking predicted high binding affinities of AA with these core targets, particularly for PTGS2 (-9.3 kcal/mol) and EGFR (-8.2 kcal/mol). Subsequent 100-ns MD simulations and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations confirmed the structural stability and spontaneous binding (ΔG_bind = -55.68 kcal/mol) of the AA-EGFR complex. Our multi-omics analysis suggests that AAs may promote UTUC not only via canonical DNA adduct formation but also potentially through direct interactions with key signaling proteins, implicating a synergistic mechanism involving both genotoxic and non-genotoxic pathways. These findings provide a theoretical foundation for novel preventive and therapeutic strategies against AA-associated UTUC.

The precise identification of cancer driver mutations is essential for precision oncology; however, it remains a significant challenge because of the overwhelming presence of passenger mutations and the complexity of mutational processes. In this study, we introduce ResMLP-GL, a signature-aware residual multilayer perceptron designed for variant-level cancer driver prediction, which explicitly integrates COSMIC SBS context probability vectors with > 100 functional and sequence features. The network incorporates two projection residual blocks alongside a feature-wise gating module that multiplicatively modulates hidden activations, thereby enhancing the gradient flow and facilitating process-aware representation learning. An Optuna-guided search optimizes parameters such as width, dropout, learning rate, and L2 regularization, whereas ADASYN addresses class imbalance. Trained on harmonized TCGA GBM/COAD exomes and reserved for testing, ResMLP-GL achieved an AUC of 0.949 on held-out data and an AUC of 0.921 on independent ICGC cohorts, surpassing CHASMplus, OncodriveFML, and MutSigCV. SHAP analysis indicated that functional scores (REVEL, AlphaMissense, CADD) and specific SBS probabilities collectively drive predictions, offering interpretable connections between mutational processes and driver selection. The model elucidates tissue-specific, signature-aligned programs (e.g., SBS1 and DNA-repair-related signatures for TP53/PTEN/EGFR in GBM and SBS1/MMR/POLE for APC/KRAS/PIK3CA in COAD), and a model-derived driver burden stratifies survival. The code, trained weights, and processed feature tables were made available for reproducibility. In summary, ResMLP-GL demonstrates that residual-gated MLPs with a quantitative signature context provide state-of-the-art interpretable driver prediction across cancers. Our findings underscore the significance of explicitly incorporating the context of mutational processes, which not only complements but, in certain oncological contexts, surpasses methodologies that rely exclusively on recurrence frequency or functional impact scores. This approach offers a robust framework for addressing tissue-specific challenges in predicting driver mutations. All code, trained models, and processed data are available at https://github.com/Zubair11122/ResMLP-GL to promote transparent and reproducible precision oncology research.

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.

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.

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.

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.

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.

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.