Ying-Qiang Shen

This study develops a non-destructive analytical framework based on Raman spectroscopy for spatial chemical imaging and quantitative assessment of dentin demineralization. Human dentin samples treated with gradient EDTA and natural caries samples were examined. SEM−EDS was used to analyze surface morphology and elemental (Ca, P) contents. Single-point Raman spectra and Raman mapping datasets were then collected. Raman spectroscopy captured coordinated changes in mineral-related bands, collagen-associated bands, and mineral-to-matrix ratios, while MCR-ALS unmixed Raman mapping data into spatially resolved mineral-rich and organic/mixed components. Four PLSR modelling strategies were employed to build predictive models, including full-spectrum PLSR, full-spectrum FiPLS, manual 12-feature PLSR, and manual 12-feature RFE-PLSR. The manual-12-RFE-PLSR model achieved the highest predictive performance for Ca (R2 = 0.895, RMSE = 2.508) and P (R2 = 0.872, RMSE = 1.217), with comparable performance from the interval-selection FiPLS model. Importantly, manual-12-RFE-PLSR and full-spectrum FiPLS maintained acceptable accuracy (R2 > 0.75) when generalized to highly heterogeneous natural caries. This framework enables quantitative elemental tracking and spatial mapping of component distributions, demonstrating potential for providing real-time, objective decision support in minimally invasive dentistry.

Gene regulatory network (GRN) inference from single‑cell RNA‑seq conventionally relies on per‑dataset optimization. Existing tools must be refit for every new dataset, and the majority fail to infer causal regulatory directions. Here we present BoYueGRN, an amortized causal discovery framework trained exclusively on 10,000 synthetic structural causal models. For any unseen dataset, a single forward pass returns edge probabilities and regulatory directions, while TF-centric sliding windows with asymmetric fusion extend this fixed-size model to full-transcriptome coverage. BoYueGRN demonstrates strong zero‑shot performance across BEELINE benchmarks. On two independent genome-wide CRISPRi Perturb-seq screens, directional accuracy on retained edges reaches 0.86 and 0.95. Reconstructed cell-type- and stage-specific GRN dynamics across five diseases spanning more than 270,000 cells yield experimentally testable biological hypotheses. BoYueGRN reframes directed GRN inference as a train-once, reuse-across-datasets paradigm. By decoupling network reconstruction from per-dataset optimization, this paradigm opens the door to systematic, atlas-scale mapping of regulatory dynamics across human diseases.

Accurate pathological grading of oral tongue squamous cell carcinoma (OTSCC) is essential for individualized clinical management and prognostic assessment. Raman spectroscopy has emerged as a promising label-free analytical tool for biochemical characterization of tissues; however, most existing Raman-based classification studies predominantly employ single-point spectral acquisition, which inadequately reflects intratumoral spatial heterogeneity and limits analytical robustness and reproducibility in complex biological samples. Here, we introduce a Raman mapping-based spatial feature integration strategy to improve both classification accuracy and signal stability for three-grade differentiation of OTSCC. Two-dimensional Raman maps were acquired from tumor tissue sections, and regional average spectra were constructed by spatially integrating spectra within sliding windows of varying sizes to incorporate multiscale spatial-chemical variation and suppress spectral noise. Deep convolutional neural networks based on multiple ResNet architectures (ResNet-18, -34, -50, -101, and -152) were trained on these integrated spectral representations and systematically evaluated against conventional machine learning classifiers. Classification performance exhibited a strong dependence on spatial integration scale, with an optimal window size of 32×32 yielding a maximum test accuracy of 0.998. Among all evaluated models, ResNet-34 demonstrated the highest overall performance and significantly outperformed the best traditional classifier, Random Forest (accuracy: 0.965). The spatial averaging approach substantially improved model robustness and resistance to spectral noise, underscoring the analytical advantage of Raman mapping data integration over point-based measurements. This work advances a robust and clinically relevant Raman spectroscopic strategy for auxiliary pathological grading of OTSCC and highlights a pathway toward clinical translation of Raman-AI synergistic diagnostics.

Oral squamous cell carcinoma (OSCC) urgently requires innovative therapeutic strategies due to its severity and stagnant five-year survival rate. Targeting glutamine metabolism, a promising approach, is hampered by tumor cells’ profound metabolic plasticity. Through a series of experiments, we uncovered heterogeneous responses of OSCC cell lines to glutamine deprivation: While most cells maintained growth, HSC3 cells showed a marked reduction in proliferation. Subsequent experiments have shown that this slowdown was not due to programmed cell death. Metabolomics and biochemical assays revealed elevated cholesterol ester (ChE) levels in glutamine-tolerant cells, not due to enhanced endogenous synthesis (HMGCS1/SQLE expression decreased) but via upregulated low-density lipoprotein receptor (LDLR)-mediated LDL uptake. Moreover, we found that fatty acid oxidation during glutamine deprivation not only supplied substrates for the tricarboxylic acid (TCA) cycle but also accelerated energy metabolism and potentially increased lipid synthesis for membrane structure and signaling. RNA sequencing identified robust enrichment of the peroxisome proliferator-activated receptor α (PPARα) pathway in tolerant cells. Reactive oxygen species (ROS) accumulation and activating transcription factor 5 (ATF5) nuclear translocation suggested the activation of multiple stress response pathways to maintain survival and growth under glutamine deprivation. Our study reveals a survival mechanism in OSCC: cells enhance exogenous lipid utilization to bypass glutamine dependence. Combined inhibition of LDL uptake and PPARα signaling may overcome metabolic plasticity, providing a rationale for precision therapies targeting metabolic heterogeneity in OSCC. Graphical abstract

Head and neck squamous cell carcinomas (HNSCCs) are the most common malignant tumors in the head and neck region, characterized by a high recurrence rate and early metastasis. Despite advances in treatment, patient outcomes and prognosis remain poor, highlighting the urgent need for new therapeutic strategies. Recent research has increasingly focused on targeting glucose metabolism as a therapeutic strategy for cancer, revealing multiple promising targets and potential drugs. However, the metabolic heterogeneity among tumors leads to variable sensitivity to metabolic inhibitors in different patients, limiting their clinical utility. In this study, we employed bioinformatics analysis, cell experiments, animal models, and multi‐omics approaches to reveal differences in glucose metabolism phenotypes among HNSCC patients and elucidated the underlying molecular mechanisms driving these differences. Our findings showed that NSD1 mutation status affects the glucose metabolism phenotype in HNSCC, with NSD1 wild‐type HNSCC exhibiting higher mitochondrial respiration and NSD1 mutant HNSCC showing weaker mitochondrial respiration but enhanced glycolysis. We further demonstrated that NSD1 regulates mitochondrial respiration in HNSCC via epigenetic modulation of the TGFB2/PPARGC1A signaling axis. Additionally, we found that NSD1 wild‐type HNSCC is more sensitive to mitochondrial respiration inhibitors, whereas NSD1 mutant HNSCC shows increased sensitivity to glycolysis inhibitors. In summary, we found that NSD1 can epigenetically regulate the TGFB2/PPARGC1A axis to modulate mitochondrial respiration and sensitivity to metabolic inhibitors in HNSCC. These findings suggest a novel strategy for selecting metabolic inhibitors for HNSCC based on the NSD1 gene status of patients. © 2025 The Pathological Society of Great Britain and Ireland.

Surface-enhanced Raman spectroscopy (SERS) has emerged as a crucial analytical tool in the field of oncology, particularly presenting significant challenges for the diagnosis and treatment of head and neck cancer. This Review provides an overview of the current status and prospects of SERS applications, highlighting their profound impact on molecular biology-level diagnosis, tissue-level identification, HNC therapeutic monitoring, and integration with emerging technologies. The application of SERS for single-molecule assays such as epidermal growth factor receptors and PD-1/PD-L1, gene expression analysis, and tumor microenvironment characterization is also explored. This Review showcases the innovative applications of SERS in liquid biopsies such as high-throughput lateral flow analysis for ctDNA quantification and salivary diagnostics, which can offer rapid and highly sensitive assays suitable for immediate detection. At the tissue level, SERS enables cancer cell visualization and intraoperative tumor margin identification, enhancing surgical precision and decision-making. The role of SERS in radiotherapy, chemotherapy, and targeted therapy is examined along with its use in real-time pharmacokinetic studies to monitor treatment response. Furthermore, this Review delves into the synergistic relationship between SERS and artificial intelligence, encompassing machine learning and deep learning algorithms, marking the dawn of a new era in precision oncology. The integration of SERS with genomics, metabolomics, transcriptomics, proteomics, and single-cell omics at the multiomics level will revolutionize our comprehension and management of HNC. This Review offers an overview of the transformative impacts of SERS and examines future directions as well as challenges in this dynamic research field.

Cancer Stem Cells (CSCs) are cancer cells with self-renewal and tumorigenesis abilities. CSCs in tumor tissues are the leading cause of tumor progression, recurrence, and drug resistance. CSCs have distinct metabolic features that contribute to maintaining their self-renewal and stemness. Phospholipids are essential components of cell membranes and play fundamental roles in cellular activities. CSCs have abnormal phospholipid metabolism, which affects their self-renewal, differentiation, invasion, and drug resistance. Compared with non-CSCs, the phospholipid metabolism of CSCs mainly focused on significantly increased fatty acid (FAs) and phospholipids synthesis, phospholipid unsaturation, and lipolysis-fatty acid oxidation (FAO). In brief, FA and phospholipid metabolism in the anabolic and catabolic pathways are strictly regulated in CSCs to maintain self-renewal and stemness activity. In this review, we summarize the alterations in phospholipid metabolism in CSCs and their impacts on the stemness of CSCs, and we put forward the potential applications of targeting phospholipid metabolism for CSCs, to provide directions for the development of drugs targeting the phospholipid metabolism.