Pancreatic ductal adenocarcinoma (PDAC) is characterized by profound therapy resistance, desmoplasia, and phenotypic plasticity. While sodium selenite is a redox-active compound with reported tumor-selective cytotoxicity, its impact on epithelial-mesenchymal transition (EMT) states in PDAC remains insufficiently defined. We investigated whether selenite modulates EMT-associated phenotypes in a dose- and context-dependent manner using complementary in vitro and patient-derived ex vivo models and linked marker shifts to histological tumor regression in PDAC tissue slices.

Three PDAC cell lines spanning distinct baseline EMT states (PANC-1, HPAF-II, Capan-2) were profiled by quantitative immunofluorescence for a predefined epithelial (EpCAM, cytokeratin, E-cadherin) and mesenchymal-associated (AHNAK2, ITGAV, vimentin) protein panel, with or without TGF-β pretreatment. Ex vivo tissue slices from treatment-naïve, non-metastatic PDAC resections (n = 10) were cultured in a within-patient paired design (0 h, 24 h, 48 h controls; 5 or 15 µM selenite during the second 24 h). Histological tumor regression was scored by a blinded pancreatic pathologist (Evans; CAP), compartment-resolved marker expression was quantified by multiplex immunofluorescence, and paired RNA sequencing was performed in a subset of donors (n = 4).

In vitro, responses were phenotype-contingent: the epithelial-biased HPAF-II and Capan-2 lines showed partial epithelial reinforcement and a consistent reduction of mesenchymal markers (particularly ITGAV, vimentin), whereas the mesenchymal-biased PANC-1 exhibited limited modulation. TGF-β pretreatment attenuated epithelial-promoting effects while suppression of mesenchymal markers was retained in a cell line-dependent manner. In ex vivo slices, selenite induced a dose-dependent improvement in histological tumor regression and increased the tumor-compartment epithelial-mesenchymal ratio at 15 µM, driven mainly by epithelial-marker upregulation, while mesenchymal markers showed no uniform suppression. At 15 µM, transcriptomics revealed a compact treatment response characterized by downregulation of basement-membrane/ECM modules without broad reversal across EMT gene sets; EMT scoring indicated heterogeneous shifts at 5 µM and a more consistent epithelial-leaning shift at 15 µM in most donors.

Selenite was associated with reinforcement of epithelial features mainly in malignant PDAC compartments in a baseline-state-, dose-, and context-dependent manner, with higher-dose phenotypic shifts occurring alongside stronger histological tumor response in clinically proximal models.

Not applicable.

Patient stratification is crucial for advancing personalized medicine yet is complicated due to the fragmented and variable nature of healthcare data. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) addresses the challenges regarding the data by offering a standardized framework for data integration across diverse sources and configurations. Recent advancements in machine learning, particularly transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT), have demonstrated significant potential in extracting deep patient representations from electronic health records.

This study assesses the efficacy of BERT-based patient representation learning using OMOP CDM data for patient stratification. We harmonize originally incompatible datasets, including the established MIMIC-IV-2.2 and lung cancer data from the German cancer registry Schleswig-Holstein, within the OMOP CDM framework. BERT is pre-trained on the MIMIC-IV-2.2 dataset, and the derived representations are utilized to generate patient embeddings from the cancer registry (test) data. We employ k-means clustering on the embeddings to stratify patient subgroups. To evaluate whether the embeddings are useful for clustering, we divide the original cancer registry data into the corresponding groups and conduct survival analyses on selected columns for each cluster. The clustering method's effectiveness is assessed by comparing survival models trained on these clusters with naïve clusters derived only from the original dataset. In addition, we included a clinical expert review, in which a physician assessed the resulting cluster assignments for clinical plausibility and interpretability.

Our approach effectively identifies patient similarities across datasets and allowed for efficient patient stratification. Survival analyses show varied performance depending on the model and cluster characteristics, with up to a 11% improvement over naïve k-means clusters, demonstrating the benefits of transfer learning. For some groups of patients, the corresponding accuracy of the survival analysis increased by up to 7%, emphasizing the value of stratifying homogeneous subgroups.

Utilizing standardized data and transformer-based foundation models to generate patient embeddings demonstrates effective knowledge transfer between two vastly different datasets and enables the identification of groups in which the accuracy of survival analysis can be significantly increased.

Cancer remains a major public health concern in China, with approximately 2.57 million cancer-related deaths reported in 2022. Real-world studies (RWS), which leverage high-quality real-world data (RWD), can generate robust real-world evidence (RWE) to enhance clinical decision-making and improve patient outcomes across diverse oncology settings.

A comprehensive literature search of four bibliographic databases (PubMed, Embase, China National Knowledge Infrastructure, and Wanfang) was conducted to identify oncology-related RWS articles and RWD sources covering Chinese populations from 2015 to 2025.

2606 RWS articles were identified. The findings indicate a growing trend in RWS publications, with a predominant focus on disease epidemiology. A total of 126 databases were extracted. Registries were observed as the main source of RWD. The top three investigated cancer types were digestive system, thoracic, and breast neoplasms. RWD sources are predominantly concentrated in coastal regions, reflecting disparities in the geographical distribution of oncology research.

Enhancing data quality and promoting the aggregation of RWD are essential for maximizing the utility of RWD in advancing oncology care in China. Moving forward, standardized regulatory procedures, improved data accessibility, and collaboration across the healthcare ecosystem are essential to unlock the value of RWD and improve patient outcomes.

Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide, despite significant advances in early detection and treatment. Conventional chemotherapeutic agents often face limitations such as drug resistance, off-target toxicity, and poor patient adherence. These challenges have highlighted the need for safer, more effective alternatives. Naturally derived compounds, especially flavonoids such as quercetin, have recently attracted attention for their ability to modulate key cancer pathways with minimal side effects. This review critically examines natural quercetin and its metabolites in the context of breast cancer prevention and treatment, focusing on their molecular targets and pharmacodynamic effects. To address issues such as poor bioavailability and rapid metabolism, we also discuss the design and synthesis of quercetin derivatives that exhibit improved stability, solubility, and targeted delivery. Additionally, the review highlights emerging quercetin-based nanomaterials designed to enhance therapeutic precision. This review offers a comprehensive overview of the development of quercetin-based therapies, outlining current progress, identifying translational hurdles, and proposing future directions for the development of optimized, targeted, and clinically viable quercetin formulations for breast cancer treatment.

Chimeric antigen receptor (CAR) T-cell therapy has achieved remarkable clinical success in hematologic malignancies but remains largely ineffective in solid tumors due to antigen heterogeneity, immune evasion, and dose-limiting toxicities. A central challenge is the identification of optimal target antigens that balance tumor specificity with therapeutic efficacy. In this review, we define the emerging antigenic landscape for CAR T-cell therapy in solid tumors through integrative curation and systems-level analysis. We reviewed 58 candidate targets spanning tumor-associated surface molecules, stromal and angiogenic components, immune checkpoints, and regulatory signaling nodes. Pathway enrichment reveals convergence on key oncogenic and immune regulatory circuits, including cell adhesion, receptor tyrosine kinase signaling, and PD-1/PD-L1 mediated immune suppression, underscoring their roles in tumor progression and immune escape. Notably, most prioritized targets localize to the plasma membrane and cell-cell interfaces, reinforcing their accessibility for CAR-based interventions. We further highlight advances in multi-antigen targeting, logic-gated CAR designs, and engineered resistance to immunosuppressive cues that collectively address tumor heterogeneity and functional exhaustion. By integrating antigen biology with emerging engineering strategies, this review provides a conceptual framework for rational target selection and combinatorial design. These insights advance the development of next-generation CAR T-cell therapies with improved precision and durability against solid tumors.

1-O-Acetylbritannilactone (ABL), a natural 1,10-seco-eudesmane sesquiterpenoid, exhibited moderate antihepatoma activity on HepG2, Huh7, and SK-Hep-1cells with IC₅₀ values of 50.7, 60.7, and 62.4 μM, respectively. Given that sesquiterpenoid dimers often exhibit stronger antitumor activity than their monomeric counterparts, a strategy of dimerization was employed to improve the activity of ABL. A total of 49 dimeric derivatives of ABL linked via ester bonds or carbamate bonds were synthesized and evaluated for their inhibitory activity against human hepatoma cell lines. As a result, all dimeric derivatives significantly enhanced the antihepatoma activity against the three cell line with IC₅₀ values ranging from 0.5 μM-20.2 μM. Among them, 43 derivatives were more active than sorafenib with IC₅₀ values below 7.0 μM. In particular, eight compounds (17, 21, 23, 24, 25, 30, 38 and 47) displayed IC₅₀ values at or below approximately 1.0 μM, indicating a 27.2- to 123.2-fold enhancement in potency over the parent ABL. These results highlighted their potential as promising novel antihepatoma candidates worthy of further investigation.

Objective: To develop S1-4 aptamer-conjugated citric acid-coated Fe₃O₄ nanoparticles and evaluate their targeting ability and magnetic hyperthermia effect against MCF-7 breast cancer cells.Methods: Citric acid-coated Fe₃O₄ nanoparticles were synthesized by chemical co-precipitation and subsequently conjugated with the S1-4 aptamer to fabricate S1-4 aptamer-conjugated citric acid-coated Fe₃O₄ nanoparticles. The nanoparticles were characterized by X-ray diffraction, transmission electron microscopy, dynamic light scattering, zeta potential analysis, Fourier transform infrared spectroscopy, and vibrating sample magnetometry. Biocompatibility was assessed in MCF-7 and MEF cells; cellular targeting was examined by Prussian blue staining; and the magnetic hyperthermia effect under an alternating magnetic field was evaluated by cell viability, live/dead staining, and apoptosis assays.Results: Compared with citric acid-coated Fe₃O₄ nanoparticles, S1-4 aptamer-conjugated citric acid-coated Fe₃O₄ nanoparticles exhibited a larger hydrodynamic diameter (17 ± 4 nm vs. 8 ± 3 nm) and a more negative surface charge (-26.7 ± 0.3 mV vs. -19.7 ± 0.5 mV). Fourier transform infrared spectroscopy confirmed successful aptamer conjugation. Both nanoparticles demonstrated good biocompatibility in MCF-7 and MEF cells. Prussian blue staining demonstrated stronger cellular uptake of S1-4 aptamer-conjugated citric acid-coated Fe₃O₄ nanoparticles in MCF-7 cells. Under alternating magnetic field exposure, S1-4 aptamer-conjugated citric acid-coated Fe₃O₄ nanoparticles produced a significantly greater hyperthermia effect than the controls, resulting in reduced cell proliferation and increased apoptosis in MCF-7 cells (P < 0.01).Conclusions: S1-4 aptamer-mediated Fe₃O₄ nanoparticles demonstrated active targeting ability and enhanced the antitumor effect of alternating magnetic field-mediated magnetic hyperthermia in breast cancer cells and maintained low cytotoxicity under the tested conditions, suggesting that they may be a feasible platform for targeted magnetic hyperthermia.

MicroRNAs are small non-coding RNA molecules of 20-24 nucleotides in length. They play a key role in regulation of gene expression by influencing stability and translation of mRNA in a wide range of biological processes. Since their discovery, a large number of microRNAs have been described, and significant progress has been made in elucidating their functions. Convincing evidence now exists that microRNAs are powerful genetic regulators. The discovery of a link between microRNAs and numerous human diseases, particularly various types of cancer, stimulated a significant interest in exploring their therapeutic potential. They represent a new class of therapeutics capable of restoring impaired cellular functions, especially in various malignancies. Despite significant progress in preclinical research, microRNA-based therapy is still in the beginning stages; only a few microRNAs have been taken into further clinical trials. This is due to their off-target effects, primarily toxicity and immunogenicity, which significantly limit the use of microRNAs as therapeutic agents. One way to overcome off-target effects is to develop new systems for targeted delivery of microRNAs to the target tissue (disease site). Managing the off-target effects of microRNAs is a serious problem that requires solutions.

IntroductionDeep learning has rapidly reshaped breast cancer imaging, but the evolution of segmentation, detection, and diagnostic research remains insufficiently characterized. This bibliometric review mapped global trends, collaboration patterns, thematic evolution, and emerging frontiers from 2006 to 2025.MethodsThis bibliometric study retrieved publications on deep learning in breast cancer imaging from the Web of Science Core Collection and Scopus. English-language articles and reviews published between 2006 and 2025 were included. After deduplication, Bibliometrix, VOSviewer, and CiteSpace analyzed publication trends, country contributions, collaboration patterns, keyword co-occurrence, temporal topic evolution, and citation bursts.ResultsA total of 3,568 publications were included. Annual output remained limited before 2016 but increased markedly thereafter, with especially rapid growth after 2020, indicating the transition of this field from an exploratory stage to accelerated development. China ranked first in corresponding-author publications, whereas the USA and the United Kingdom showed stronger citation impact, reflecting differences between publication scale and academic influence. Keyword analysis showed that the field was primarily structured around deep learning, breast cancer, mammography, segmentation, detection, and computer-aided diagnosis. Temporal analyses further indicated a shift from early computer-aided diagnosis frameworks and conventional neural-network approaches toward more advanced and clinically relevant themes, including explainable artificial intelligence, nomogram, self-attention, transformers, neoadjuvant therapy, and axillary lymph node metastasis.ConclusionDeep learning in breast cancer imaging has evolved into a rapidly expanding and increasingly sophisticated field centered on segmentation, detection, and clinically relevant diagnostic research. Future progress will depend on improved interpretability, robust validation, and stronger clinical integration.

Post-transplant relapse remains a major challenge in T-ALL, with limited effective salvage options. We report on eight T-ALL patients who received low-dose chidamide (5 mg once weekly) as maintenance therapy after allogeneic HSCT. With a median follow-up of 22.7 months, the estimated 2-year OS and EFS are both 87.5%. Seven patients (87.5%) remain in continuous complete remission with sustained MRD negativity. Grade 3-4 neutropenia occurred in 57.1% of evaluable patients but was clinically manageable. This case series demonstrates that a low-dose chidamide maintenance regimen is feasible, well tolerated, and promising in this setting.