Yuan Wu

Skin cutaneous melanoma is a highly aggressive malignancy with poor prognosis, particularly in advanced stages. Lactylation, a post-translational modification, plays a crucial role in shaping the tumor microenvironment and contributing to immune suppression, making it a potential therapeutic target. In this study, single-cell RNA sequencing data from the Tumor Immune Single-cell Hub database and bulk RNA sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. Lactylation-related genes were obtained from the Molecular Signatures Database, and lactylation scores for each cell type were computed using the AUCell R package. Cell–cell communication networks were constructed using CellChat, and a prognostic model was developed based on machine learning—including least absolute shrinkage and selection operator, random forest, and XGBoost. Drug candidates were identified via the L1000 Fireworks Display database and the Comparative Toxicogenomics Database, followed by molecular docking for drug–target interaction assessment. Results revealed that Tprolif cells exhibited the highest lactylation levels, indicating their immunosuppressive role in the tumor microenvironment, with the macrophage migration inhibitory factor receptor–ligand pathway identified as a key interaction hub. The eight lactylation-related genes–Tprolif prognostic model demonstrated moderate predictive ability for patient survival, and BG-FA-0953 emerged as a potential therapeutic candidate that may target lactylation-related pathways, warranting further investigation. This study provides novel insights into lactylation-driven immune modulation in skin cutaneous melanoma, with potential implications for risk stratification and therapeutic intervention in advanced-stage melanoma.

The pronounced spatial heterogeneity and dynamic evolution of the tumor microenvironment represent key factors limiting the sustained therapeutic efficacy of anti-tumor drugs in solid tumors. Traditional drug delivery strategies often rely on spatiotemporally uniform delivery profiles, making it difficult to match the complex structural organization and continuously evolving biological states within tumor tissues. This results in uneven drug distribution, limited therapeutic windows, and the development of drug resistance. In recent years, 3D-printing and bioprinting technologies, as layer-by-layer manufacturing methods based on digital design, have provided new solutions for constructing drug delivery systems with modifiable structures, partitioned spatial organization, and programmable time-release capabilities. By precisely controlling the arrangement of materials, bioactive molecules, and cells in three-dimensional space, these systems can not only achieve fine spatiotemporal regulation of the drug delivery process but also integrate cells, extracellular matrix, and mechanical cues during delivery, thereby partially reshaping the tumor microenvironment. This paper systematically reviews the design strategies and latest advancements of 3D-printing and bioprinting delivery systems in achieving spatiotemporally controllable drug delivery within the tumor microenvironment, focusing on their advantages in spatial regulation, temporal response, and tumor microenvironment reprogramming, while analyzing the current key challenges and clinical translation prospects, aiming to provide references for the rational design of next-generation tumor delivery systems.

Traditional Chinese medicine (TCM), with its multi-component and multi-target nature, offers rich potential for drug repurposing. Advances in systems biology, computational modeling, and high-throughput technologies have enabled systematic analysis of TCM mechanisms, facilitating in the identification of active ingredients, target interactions, and synergistic effects. However, most existing reviews focus on individual methods rather than providing an integrative translational strategy tailored to the complexity of TCM. This review addresses that gap by proposing a stage-based framework that combines mechanism analysis, preclinical validation, and clinical translation. We highlight key methodologies such as network pharmacology, multi-omics, molecular docking, and phenotypic screening, and discuss how they can be synergistically applied. Emerging technologies including AI, big data, 3D bioprinting, and organoid models are evaluated not only for their utility but also through critical analysis of their limitations in capturing TCM’s holistic principles. By bridging traditional knowledge with modern biomedical innovation, this review offers a novel roadmap for accelerating evidence-based TCM repurposing. The proposed integrative strategy supports more reproducible, mechanistically grounded, and globally relevant applications of TCM in modern drug development.