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Follicular thyroid neoplasms represent a common subtype of thyroid tumor that has become the most prevalent endocrine neoplasms in recent decades. Accurate diagnosis and grading of these tumors are critical for clinical management of follicular thyroid neoplasms, yet remain challenging due to the limitations of conventional imaging modalities without histopathological molecular information. There is growing interest in analytical techniques that can provide metabolic and molecular insights without the need for exogenous reagents. In such a context, Fourier transform infrared (FTIR) microspectroscopy has emerged as a promising label-free approach for detecting intrinsic disease biomarkers. In this work, we proposed a label-free framework for screening and grading follicular thyroid neoplasm tissues using FTIR microspectroscopy and machine learning. A total of 32 clinical samples in the form of tissue sections were collected from a real-world cohort and measured for the spectral data, from patients diagnosed with follicular thyroid adenoma, follicular tumor with uncertain malignant potential, and follicular thyroid carcinoma. For tumor screening, disease-specific features were extracted from the FTIR mapping data and further imaged through integration, principal component analysis, and clustering, enabling visual and quantitative assessment of tissue abnormalities. For neoplasms grading, a deep neural network trained with an adversarial learning strategy achieved a grading accuracy of 94.0% on an independent test set. These findings collectively demonstrate the potential of FTIR microspectroscopy as a powerful, reagent-free tool for the diagnosis, pathological evaluation, and clinical management of follicular thyroid neoplasms.

The AAA + ATPase p97/VCP is a central regulator of protein homeostasis and has emerged as an attractive anticancer target. However, first-generation ATP-competitive inhibitors have faced clinical setbacks due to off-target toxicity and sensitivity to ATP concentrations. Herein, we report the design, synthesis, and systematic structure-activity relationship (SAR) study of a novel series of diphenylmethyl-based non-competitive p97/VCP inhibitors derived from the allosteric hit MSC1094308. SAR optimization revealed that conversion of the amide linker to a secondary amine, coupled with the introduction of a tetrahydropyrido[3,4-b]indole scaffold and fluorine substitution on the biphenylmethyl group, dramatically enhanced p97/VCP inhibitory activity. The lead compounds, 10a and 10b, exhibited potent non-competitive inhibition (IC50 = 1.04 μM and 17 nM, respectively) and maintained efficacy independent of ATP concentration. Microscale thermophoresis (MST) confirmed strong binding affinity of 10a to p97/VCP (Kd = 14.99 μM), and limited proteolysis-mass spectrometry (LiP-MS) identified p97/VCP as a direct cellular targ et of 10a. Mechanistically, 10a induced mitochondrial membrane depolarization, leading to concurrent regulation of both apoptotic (caspase-3, PARP cleavage) and autophagic (LC3-II, p62) pathways. In vitro, 10a demonstrated broad-spectrum antiproliferative activity across multiple cancer cell lines and completely suppressed the growth of patient-derived colorectal cancer organoids. In an MC38 colorectal cancer xenograft mouse model, 10a achieved 55% tumor growth inhibition with manageable toxicity. Collectively, this study identifies 10a as a promising lead compound for colorectal cancer therapy and establishes allosteric p97/VCP inhibition via mitochondrial stress as a viable therapeutic strategy.

The PRMT5·MTA complex has been recognized as a potential drug target for the treatment of MTAP-deleted cancers, especially for the brain tumors. Although several MTA-cooperative PRMT5 synthetic lethal inhibitors have been advanced into clinical trials, only one of them (TNG908) showed brain permeability in the preclinical evaluation but failed to achieve the anticipated therapeutic exposure levels in glioblastoma in clinical trials. In this study, we reported the discovery of compound 21, which showed much higher brain permeability than TNG908. More importantly, compound 21 achieved significant tumor growth inhibition in an orthotopic U87MG brain tumor model, supported by its enhanced distribution and penetration within brain tissue. These results indicate the potential clinical advantages of compound 21 for treating MTAP- deleted tumors and support its potential utility against intracranial malignancies.

Raman spectroscopy has been proved to have the potential to accurately diagnose a variety of diseases, and novel Raman probes or instruments for clinical applications have been constantly developed. However, biological tissues are usually structurally complex. The Raman signals collected in vivo may be a mixture of various chemical components, even different tissues, which poses challenges for disease analysis and diagnosis. This work proposed a Raman spectral unmixing approach to separate the signals of different tissues from their mixed spectra. Specifically, continuous wavelet transform was performed to extract the multi-scale time-frequency domain features of Raman spectra. TransUnet model was introduced to analyze the multi-scale features from high-frequency to low-frequency through the convolution and transformer modules, and predict the Raman signals of target components. Breast cancer tissues were selected as the research subject, the Raman signals of stroma and adipocyte were successfully separated from their mixed tissues, and multiple biochemical changes in breast cancer tissues were revealed through further analysis of the unmixing signals. This work will contribute to biological in vivo detection of Raman probes or instruments, enabling them to separate signals from different tissues, structures, and even biochemical molecular components for more detailed and accurate analysis of diseases.

Early identification of breast cancer is essential for improving survival rates, yet current screening approaches often exhibit inadequate specificity or excessive invasiveness. Serum Raman spectroscopy (RS) provides a quick, non-destructive option, but its clinical use is impeded by the complexity of spectral data interpretation. Herein, we presented an interpretable deep learning framework, a one-dimensional convolutional neural network with a patient-voting strategy(PV-CNN), to evaluate serum RS from 732 individuals (366 patients and 366 healthy controls). Our model achieved a diagnostic accuracy of 95.21%, a sensitivity of 92.38%, and a specificity of 97.00%, significantly surpassing the performance of conventional ML algorithms. Furthermore, we employed Grad-CAM and SHAP analyses to elucidate the decision-making processes of deep learning, representing a significant advancement in addressing the "black-box" issue. This interpretability analysis found that tryptophan (1017 cm-1) and phenylalanine (1002 cm-1) were key Raman spectral indicators for breast cancer diagnosis. This study demonstrates that interpretable deep learning-enhanced RS can serve as a reliable, label-free, and physiologically explainable method for breast cancer detection.

The P2X7 receptor (P2X7R) is critically involved in tumor progression by promoting cancer cell proliferation, invasion, metastasis, and immune evasion. Through structure-based virtual screening of the TOPSCIENCE L1000 library, we identified pixantrone - an FDA-approved agent - as a promising lead compound targeting P2X7R. Leveraging its well-established safety profile and anthraquinone scaffold, we designed and synthesized a novel series of P2X7R antagonists. Among them, the optimized compound 17d displayed potent antagonistic activity (IC50 = 3.57 μM) and specific binding to P2X7R. In addition, 17d exhibited significant antiproliferative activity against MCF-7 cells (IC50 = 0.42 μM) and effectively inhibited their invasive and metastatic capabilities. Compound 17d also demonstrated favorable oral bioavailability and pharmacokinetic properties. Moveover, in a murine breast cancer model, 17d significantly suppressed tumor growth and metastasis while promoting the activation of CD4 and CD8 T cells to enhance antitumor immunity. Notably, 17d acted synergistically with anti-PD-1 monoclonal antibody, overcoming resistance to anti-PD-1 therapy. These findings highlight 17d as a promising candidate for P2X7R-targeted cancer therapy and underscore the value of repurposing FDA-approved drugs using structure-based approaches to accelerate the development of safer and more effective anticancer agents.

Cyclin-dependent kinase 4/6 (CDK4/6) plays a pivotal role in cell cycle regulation, and its abnormal activation is closely associated with the initiation and progression of breast cancer.1 However, the limited number of clinically available CDK4/6 inhibitors restricts treatment options. This study focuses on the design, synthesis, and antitumor activity investigation of novel CDK4/6 inhibitors.Employing computer-aided drug design (CADD) strategies and utilizing drug-like and bioisostere principles, we innovatively introduced pyridine-2-aminopyrimidine, thieno[3,2-d]pyrimidine, and pyrazolo[1,5-a]pyrimidine as core skeletal structures to design and screen 250 compound molecules. Based on computational results, including molecular docking and MM/GBSA binding free energy, 40 potential novel CDK4/6 inhibitors were selected for synthesis.Cellular assays validated that compound 3c exhibited the strongest inhibitory activity against CDK4/6 in breast cancer cell lines, with IC50 values of 0.11 ± 0.01 μM (MCF-7), 0.21 ± 0.01 μM (4T1), and 0.12 ± 0.01 μM (MDA-MB-231). And compound 3c demonstrated favorable CDK4/6 inhibition rates and showed a certain degree of selectivity among 23 kinases. In vitro mechanistic studies revealed that 3c consistently inhibited cell colony formation and migration. Furthermore, 3c effectively arrested the MCF-7 cell cycle at the G1 phase and induced apoptosis. In the MCF-7 xenograft model, compound 3c achieved a tumor inhibition rate of 45.20%, highlighting its significant potential as a therapeutic CDK4/6 inhibitor.

PROteolysis TArgeting Chimeras (PROTACs) represent a novel therapeutic strategy that leverages the ubiquitin-proteasome system for targeted protein degradation. Aptamers, with their high specificity and binding affinity, have recently been explored as alternative recognition elements in PROTAC design. Here, we developed an aptamer-based PROTAC targeting SET domain bifurcated histone lysine methyltransferase 1 (SETDB1), an epigenetic regulator implicated in breast cancer progression. The SETDB1-specific aptamer identified in our previous work was conjugated to a CRBN E3 ligase ligand via click chemistry, generating a serum-stable PROTAC, designated as P-SETDB1-4. P-SETDB1-4 effectively recruits CRBN to SETDB1, inducing proteasome-dependent degradation of SETDB1 in breast cancer cells. Consequently, P-SETDB1-4 significantly inhibits the proliferation and migration of breast cancer cells. Moreover, P-SETDB1-4 enhances the CD8+ T cells cytotoxicity against breast cancer cells and suppresses tumor growth in vivo. RNA sequencing analysis elucidates the molecular mechanism underlying P-SETDB1-4-mediated tumor suppression and promotion of CD8+ T cell-mediated killing. This study provides a promising therapeutic strategy for breast cancer and highlights the potential of aptamer-CRBNL PROTACs for targeting other challenging oncogenic proteins.

The inhibition of PI3K has only a limited therapeutic effect and often leads to drug resistance. Combining PI3K and BRD4 inhibition results in a beneficial antitumor effect in MYC-driven hematological malignancies and solid tumors, underlying the significance of developing dual kinase/epigenetic inhibitors. In this study, we report the design, synthesis, and biological evaluation of novel dual PI3K/BRD4 inhibitors. A series of benzopyranone (BP) based analogs were synthesized and subjected to structure activity relationship analysis (SAR). LCI40 displayed significant enzymatic inhibitory activity against PI3K, BRD4 proteins and demonstrated a remarkable selectivity profile against a 468 panel of kinases and 31 bromodomains. It exhibited favorable in vitro and in vivo pharmacokinetics in a mouse model. In a mantle cell lymphoma cell line (Mino), LCI40 inhibited the phosphorylation of pAKT (S473) and suppressed c-MYC levels. Further, LCI40 displayed immunomodulatory capacity with minimal toxicity to normal mouse immune cells. LCI40 is a promising lead candidate for development as a dual PI3K/BRD4 inhibitor for cancer therapy.

Neurotensin Receptor 1 (NTSR1) is a Class A G protein-coupled receptor(GPCRs) that exhibits high affinity for neurotensin. Overexpression of NTSR1 in various cancers makes it a promising target for tumor-specific imaging and therapy. Currently, radioligands such as the small-molecule antagonist 3BP-227 and the peptide agonist [68Ga]DOTA-NT-20.3 have entered clinical trials. However, NTSR1-targeted radiopharmaceuticals still face challenges including suboptimal tumor-to-background ratios, high renal accumulation, and the lack of validated theranostic pairs for patient stratification. Studies have shown that pharmacokinetic modifiers such as hydrophilic linkers and albumin-binding moieties can significantly modulate ligand bioavailability as well as imaging and therapeutic efficacy. This review systematically elaborates recent advances in the development of compounds targeting NTSR1, focusing on the molecular structures and biological activities of radiolabeled peptide and non-peptide derivatives. It emphasizes their applications in tumor imaging diagnosis and targeted radiotherapy. The article aims to provide a reference for drug development based on NTSR1 ligands.