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c-Met inhibitors have demonstrated encouraging efficacy in the treatment of non-small cell lung cancer. However, some of these drugs are conditionally approved and still face certain limitations and challenges, primarily manifested as poor blood-brain barrier penetration, off-target toxicity, drug resistance, and low oral bioavailability. These factors restrict their clinical efficacy and widespread application. To discover novel c-Met inhibitors with high potency, minimal toxic side effects, and the ability to penetrate the blood-brain barrier, we designed and synthesized 33 new pyrimidine derivatives using Tepotinib as the lead, employing bioisosterism and conformational restriction strategies. Their anti-tumor activities were evaluated in vitro and in vivo. Among these derivatives, the optimal compound 11g exhibited IC50 values of 4.01 nM and 3.50 nM against MHCC97H and EBC-1 cells, respectively. In the EBC-1 xenograft mouse model, at a dose of 4 mg/kg, 11g achieved a tumor growth inhibition (TGI) rate of 64.9%, which was significantly higher than that of Tepotinib (33.5%) at the same dose. Pathological evaluation further confirmed that 11g possessed improved safety and reduced toxic side effects. In addition, 11g displayed superior blood-brain barrier permeability and metabolic stability compared with the lead compound. Mechanistic studies demonstrated that 11g effectively inhibits tumor cell proliferation and migration by binding to the c-Met protein and induces cell apoptosis. In summary, as a novel and highly potent c-Met inhibitor, 11g shows promising potential for the treatment of NSCLC, particularly in the prevention and treatment of tumor brain metastasis.

Targeting cancer stem cells (CSCs) has emerged as a promising strategy for cancer therapy and prevention. The human cytochrome P450 enzyme CYP4Z1 has been identified as a potential therapeutic target due to its role in promoting breast cancer stemness. Aiming to develop potent and selective CYP4Z1 inhibitors, our strategy involved systematic structure-activity relationship (SAR) studies of the lead compound XD-2 (1-benzyl-1H-imidazo [4,5-c] pyridine), which led to its structural optimization. A series of derivatives were designed and synthesized to enhance drug-like properties, inhibitory activity, and selectivity. Among all the synthesized compounds, the preferred analog C8, which features an imidazo[4,5-c]pyridine core connected to a terminal butyl group via an amide-containing linker, exhibited the most potent CYP4Z1 inhibitory activity, with an IC50 value of 55.3 nM against CYP4Z1. Molecular docking studies revealed that the introduced side chain extended into the hydrophobic subpocket and the phenyl group established additional aromatic stacking interactions with Trp120. Subsequent in vitro and in vivo biological assessments confirmed that compound C8 potently diminished stemness marker expression, impeded spheroid formation, and attenuated both metastatic potential and tumor-initiating capacity in breast cancer cells. Collectively, these results underscore the promise of C8 as a leading candidate for advancing clinically viable CYP4Z1-targeted therapies in breast cancer.

Depletion of the splicing factor RBM39 disrupts spliceosome function and induces widespread RNA splicing defects, leading to antiproliferative effects in susceptible cancer cells. Here, we report the discovery and characterization of a new series of biphenyl-containing RBM39 degraders. The lead compound 42 promotes RBM39 degradation through formation of a ternary complex with RBM39 and DCAF15/DDB1 in a Cullin-RING E3 ligase- and proteasome-dependent manner, consistent with a molecular glue mechanism. Transcriptomic analyses in HCT-116 and K562 cells revealed extensive alternative splicing alterations and suppression of cell-cycle-associated pathways, resulting in G2/M-phase arrest without apoptosis. Comparative cellular profiling identified 41 (YSA64) as a potent analog in acute myeloid leukemia MV4-11 cells and Ewing sarcoma A673 cells, disease contexts that have been minimally explored for RBM39 degraders. Notably, 41 exhibited favorable oral pharmacokinetics and significant antitumor efficacy in MV4-11 xenograft models. Collectively, this work expands the chemical space of RBM39 degraders and supports their continued development as RNA splicing-targeted anticancer agents.

Nur77, an orphan nuclear receptor, is involved in the development and progression of multiple tumors. In our previous study, we have shown that the protein level of Nur77 is elevated in colon tumors compared to adjacent normal tissues, highlighting its potential as a promising target for colorectal cancer therapy. Significantly, we have identified BI1071 as a Nur77-targeting compound that induces apoptosis in colorectal cancer cells. Based on the scaffold of BI1071, by substituting the indole group of BI1071 with a pyrrolyl group on one side, we rationally designed and synthesized a series of novel BI1071 analogues named SIM-C-PhCF3+Cl- targeting Nur77, and the structure-activity relationship of these BI1071 derivatives was summarized. From this series of compounds, A6 exhibited the strongest binding affinity to Nur77 (Kd = 0.40 ± 0.05 μM) and the most potent anti-proliferative activity against HCT116 and MC38 colorectal tumor cell lines, with IC50 values of 0.53 ± 0.06 μM and 0.16 ± 0.007 μM, respectively. Interestingly, unlike BI1071, which triggers Nur77-dependent apoptosis, compound A6 suppressed colon cancer cell proliferation predominantly by inducing Nur77-dependent mitotic arrest. Collectively, our findings provide a foundation for further investigation and development of Nur77-targeting antimitotic molecules toward colorectal cancer therapy.

Determination of estrogen receptor (ER) and progesterone receptor (PR) status is critical for breast cancer subtyping and guiding endocrine therapy. Although immunohistochemistry (IHC) remains the diagnostic gold standard, it is costly, labor-intensive, and prone to interobserver variability. These limitations are particularly restrictive in low-resource settings where access to standardized receptor testing is limited. This study presents a proof-of-concept evaluation of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy combined with artificial intelligence (AI) for label-free classification of ER and PR status in formalin-fixed paraffin-embedded (FFPE) breast cancer tissues. A total of 72 samples (33 ER-positive, 39 ER-negative) were analyzed for ER classification, and 74 samples for PR classification (20 PR-positive, 54 PR-negative), generating 2328 and 1804 spectra, respectively. Spectra were acquired from pathologist-annotated tumor regions exhibiting definitive nuclear staining (positive) or absence thereof (negative) using a grid-based mapping strategy. Preprocessing included baseline correction (rubber-band algorithm) and z-score normalization. Seven AI models - logistic regression, support vector machine (SVM), decision tree, XGBoost, feedforward neural network (FNN), recurrent neural network (RNN), and convolutional neural network (CNN) - were trained and optimized using a genetic algorithm. Model performance was assessed via repeated cross-validation using AUC-ROC, accuracy, sensitivity, specificity, PPV, NPV, and F1 score. CNN achieved the highest classification performance for both ER (AUC = 95.93% ± 6.64%, accuracy = 90.06% ± 4.85%) and PR (AUC = 97.46% ± 0.64%, accuracy = 91.51% ± 3.28%). FNN, RNN, and XGBoost also demonstrated strong performance, whereas SVM yielded the lowest accuracy and F1 scores. Statistically significant spectral differences between receptor-positive and -negative tumor regions were observed across biochemical bands corresponding to proteins, lipids, nucleic acids, and phosphorylated biomolecules. AI-enhanced ATR-FTIR spectroscopy demonstrates high diagnostic potential for hormone receptor subtyping in FFPE tissues. As a label-free, scalable platform, it offers a promising alternative to IHC, particularly in resource-constrained environments. These findings establish the technical feasibility of this approach and warrant further validation in multicenter clinical cohorts.

Chemokine receptor 4 (CXCR4) is a clinically significant G protein-coupled receptor implicated in HIV-1 entry, cancer progression, immune regulation, and metastatic dissemination, making it an attractive therapeutic target. This study employed an integrated computational and experimental framework to identify novel small-molecule CXCR4 inhibitors. A curated dataset of 608 compounds from peer-reviewed literature and patents was used to train machine-learning classification models. Decision Tree, Logistic Regression, and AdaBoost models showed balanced performance across key metrics, and external validation on 2146 in-house compounds identified 44 consensus CXCR4 inhibitors. Molecular docking analyses suggested favorable binding modes and key interactions comparable to those predicted for the reference inhibitor IT1t. One hundred-nanosecond molecular dynamics simulations indicated stable CXCR4-ligand complexes, with equilibration occurring within approximately 20 ns and backbone RMSD values maintained between 4 and 8 Å. MM/GBSA free-energy calculations demonstrated favorable energetics, with IS00622 exhibiting the strongest affinity (-70 kcal/mol), followed by IT1t, IS00998, and IS00179. In vitro assays identified IS00127 as a promising lead, showing strong antiproliferative activity against MDA-MB-231 cells and minimal toxicity toward HEK293 cells. ELISA assays confirmed dose-dependent CXCR4 downregulation with negligible effects on CXCR7, indicating high functional selectivity. Overall, this integrative strategy accelerates the discovery of potent, selective CXCR4 inhibitors for translational research.

The overexpression of the transcriptional enhanced associate domain (TEAD), which regulates gene transcription linked to cell growth, drives the proliferation in cases of hepatocellular carcinoma (HCC). In order to discover novel TEAD inhibitors that are more effective and have better efficacy and pharmacokinetic properties for treating HCC, this study employed a cyclization strategy to generate a novel indole-based scaffold of TEAD inhibitors. A comprehensive and systematic structure-activity relationship (SAR) analysis identified the most promising compound: LC-TD-05, a non-covalent, partial TEAD inhibitor with selective activity against TEAD1, TEAD2 and TEAD4, but reduced potency against TEAD3. LC-TD-05 exhibits good potency against TEAD1/2/4 (TEAD1 IC50 = 116.6 ± 21.7 nM, TEAD2 IC50 = 168.7 ± 17.1 nM, TEAD4 IC50 = 68.3 ± 18.2 nM), demonstrates favorable oral bioavailability (F = 53.7%), and exhibits significant anti-tumor activity in HCC LM3 models in vitro (LM3 cell IC50 = 248 ± 27.9 nM) and in vivo (TGI = 75%). Overall, this study provides a novel scaffold for TEAD inhibitors, enabling more effective interventions against HCC.

Cutaneous squamous cell carcinoma (cSCC) is a common and potentially aggressive skin cancer, with limited therapeutic options for advanced disease. The Hedgehog/GLI (HH/GLI) signaling pathway has emerged as a potential therapeutic target in cSCC. Itraconazole (ITZ), a repurposed antifungal agent, exhibited HH/GLI pathway inhibition but suffers from unfavorable physicochemical properties. Herein, we report the rational design and evaluation of truncated-ITZ analogues as novel HH/GLI pathway inhibitors for cSCC treatment. Starting from the metabolite-inspired lead compound A-26, two series of analogues were synthesized and optimized through structure-based drug design and ligand-lipophilicity efficiency-driven optimization. Among them, compound 16 demonstrated potent HH/GLI pathway inhibition and enhanced aqueous solubility. Compound 16 selectively inhibited proliferation of A431 SCC cells, suppressed colony formation, and induced cell apoptosis. In an A431 xenograft model, 16 significantly suppressed tumor growth with downregulation of GLI1, Ki67, and SOX2. Preliminary safety evaluation revealed no significant hematological or organ toxicity. These results established truncated-ITZ analogues as promising HH/GLI pathway inhibitors and support further development of compound 16 as a potential therapeutic agent for cSCC.

The therapeutic gain ratio (TGR) of radiotherapy for hepatocellular carcinoma (HCC) remains limited by two major barriers: insufficient precision in adaptive radiotherapy (ART) on the physical dimension and the lack of effective radiosensitization on the biological dimension. Although advances have been made separately in accurate dose delivery and tumor-sensitizing strategies, no approach has yet integrated both dimensions to achieve a coordinated improvement in TGR, representing a critical gap in current practice. In this study, we propose an integrated physical-biological strategy that combines nanomaterials with artificial intelligence (AI). We first constructed charge-engineered gold-platinum nanoparticles that respond to the acidic tumor microenvironment and enable prolonged, high-contrast computed tomography imaging of HCC. These enhanced images were then used to develop the first Transformer-convolutional neural network hybrid model (3D STS-Net) tailored for this scenario, enabling high-accuracy three-dimensional segmentation of small HCC for image-guided adaptive radiotherapy. In parallel, we systematically evaluated the nanoparticles' radiosensitizing effects in vitro and in vivo. The nanoparticles provided stable imaging enhancement for up to 120 h and markedly improved tumor-liver contrast. The 3D STS-Net achieved high segmentation accuracy, supporting more precise contouring for HCC ART. Moreover, the nanoparticles significantly increased radiation-induced reactive oxygen species and enhanced tumor control in animal models. Together, these findings demonstrate that the proposed strategy simultaneously strengthens radiotherapy performance in both physical and biological dimensions, leading to a coordinated improvement in TGR. This integrated "nanomaterial-AI" framework offers a systematic and generalizable approach for enhancing radiotherapy effectiveness in HCC.

Point-of-care testing (POCT) platforms frequently suffer from a fundamental bottleneck: while advances in molecular amplification improve signal intensity, the reliability of signal readout in complex clinical matrices remains poorly controlled. Here, we present an integrated biosensing framework that treats readout reliability as an explicit engineering objective rather than a post hoc correction problem. The platform integrates three complementary components: (i) a heptameric nanobody probe employed as a multivalent recognition element for target capture, (ii) a DNA-assisted clustering interface that spatially organizes gold nanoparticle reporters for robust signal amplification, and (iii) a few-shot learning module based on Prototypical Networks that enables robust classification with minimal training data while providing interpretable decision-making through metric-based reasoning. Alpha-fetoprotein was selected as the model analyte because it remains a clinically important biomarker for hepatocellular carcinoma screening and follow-up, while also representing a realistic POCT challenge in which clinically meaningful detection must be achieved with low instrumentation burden and reliable readout under matrix variability. In this setting, the system achieves a visual limit of detection of 2 ng/mL and demonstrates quantitative consistency across representative clinical serum samples. Importantly, the AI module functions as an integral system component, identifying diagnostically relevant regions and mitigating readout uncertainty arising from matrix effects and imaging variability. By jointly engineering the sensing interface and the interpretive layer, this work establishes a generalizable strategy for constructing trustworthy POCT systems in which chemical signal generation and digital interpretation are co-designed.