Glucose transporter 1 (GLUT1), a central orchestrator of tumor metabolic reprogramming, sustains malignant progression by enforcing glycolytic dependency and conferring therapeutic resistance. While conventional GLUT1-targeted small-molecule inhibitors demonstrate preclinical efficacy through glucose transport blockade and chemo-radiosensitization, their clinical translation is impeded by intrinsic limitations. Emerging nanomedicine paradigms have redefined GLUT1-targeted interventions through multifunctional platforms that synergistically unify precision therapeutics, imaging guidance, and immunometabolic modulation. Meanwhile, advanced formulations further exploit GLUT1-mediated endocytosis to achieve blood-brain barrier penetration, thus potentially addressing key challenges in the treatment of CNS malignancies. Notably, combinatorial nanoarchitectures simultaneously disrupt metabolic pathways and reprogram immunosuppressive niches via dual-targeting strategies, thereby counteracting tumor adaptation mechanisms. These innovations transcend conventional therapeutic boundaries by establishing metabolic-immune interplay regulation and barrier-defying delivery systems. This review systematically analyses the evolving landscape of GLUT1-targeted nanomedicine, evaluating both traditional molecular inhibitors and next-generation nanoplatforms that harness GLUT1 through diverse modalities. By dissecting molecular mechanisms and translational applications, we elucidate the diagnostic and therapeutic value of GLUT1-targeted nano-strategies in precision oncology while outlining future directions for clinical implementation.

The global annual incidence of brain tumors is approximately seven out of 100,000, accounting for 2% of all tumors. The mortality rate ranks first among children under 12 and 10th among adults. Therefore, the localization and segmentation of brain tumor images constitute an active field of medical research. The traditional manual segmentation method is time-consuming, laborious, and subjective. In addition, the information provided by a single-image modality is often limited and cannot meet the needs of clinical application. Therefore, in this study, we developed a multimodality feature fusion network, MM-UNet, for brain tumor segmentation by adopting a multi-encoder and single-decoder structure. In the proposed network, each encoder independently extracts low-level features from the corresponding imaging modality, and the hybrid attention block strengthens the features. After fusion with the high-level semantic of the decoder path through skip connection, the decoder restores the pixel-level segmentation results. We evaluated the performance of the proposed model on the BraTS 2020 dataset. MM-UNet achieved the mean Dice score of 79.2% and mean Hausdorff distance of 8.466, which is a consistent performance improvement over the U-Net, Attention U-Net, and ResUNet baseline models and demonstrates the effectiveness of the proposed model.

The design of prodrugs aims to address the issues of systemic toxicity and poor specificity associated with traditional chemotherapy drugs, thereby improving patient survival rates. However, effectively controlling the activation of prodrugs and further improve the efficacy remains a significant challenge that needs to be addressed. Photodynamic therapy (PDT) is a non-invasive cancer treatment that utilizes photosensitizers (PS) to generate reactive oxygen species (ROS) under light irradiation, selectively killing tumor cells, but PDT still faces challenges such as limited therapeutic efficacy. To address challenge in cancer treatment, light-activated prodrugs have emerged as a promising strategy to achieve precise drug release and activation through light control in terms of time and location. This review explores the classification and mechanisms of light-activated prodrugs, with a focus on covalent and non-covalent photosensitizer-drug conjugates. These approaches enhance targeting, precisely control drug release, and achieve synergistic effects between PDT and chemotherapy. By analyzing these strategies, we highlight their potential in improving PDT efficacy and advancing targeted cancer therapy. Finally, we discuss future directions for designing advanced light-activated prodrug systems, providing new insights for the development of more effective and targeted cancer treatments.

Esophageal squamous cell carcinoma (ESCC) is the predominant pathotype of esophageal carcinoma (EC) in China, especially in Henan province, with poor prognosis and limited 5-year survival rate. Cellular retinoic acid binding protein 2 (CRABP2) is a member of the retinoic acid (RA) and lipocalin/cytosolic fatty-acid binding protein family and plays a completely contrary role in tumorigenesis through the retinoid signaling pathway, depending on the nuclear RA receptors (RAR) and PPARbeta/delta receptors. Presently, the biological role of CRABP2 in the development of ESCC has never been reported. Here, we firstly evaluated the expression of CRABP2 at both mRNA and protein levels and showed that it was remarkably downregulated in clinical ESCC tissues and closely correlated with the occurrence position, pathology, TNM stage, size, infiltration depth and cell differentiation of the tumor. Additionally, the biological function assays demonstrated that CRABP2 acted as a tumor suppressor in esophageal squamous carcinogenesis by significantly inhibiting cell growth, inducing cell apoptosis and blocking cell metastasis both in vitro and in vivo. All in all, our finding simplicate that CRABP2 is possibly an efficient molecular marker for diagnosing and predicting the development of ESCC.