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Updated: Jan 8, 2026
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
Integrated spatial metabolomics and transcriptomics reveal the molecular landscape of papillary thyroid cancer and
Kening Li1,2,3, Zongfu Pan1,2,3, Wenqiao Chang1
1Center for Clinical Pharmacy, Cancer Center, Department of Pharmacy, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Background:
Papillary thyroid cancer (PTC) exhibits highly variable clinical behavior, ranging from indolent growth to aggressive lymph node (LN) metastasis. However, the specific molecular mechanisms involved in PTC evolution, driven by metabolic reprogramming and tumor-microenvironmental interactions, remain poorly understood. This study aims to elucidate the spatial metabolic and transcriptional mechanisms underlying PTC tumorigenesis and LN metastasis at multiple molecular levels.
Methods:
An integrated spatial multi-omics strategy combining spatial metabolomics and spatial transcriptomics was employed to map the distribution of metabolites and gene expression in heterogeneous PTC tissues. Metabolite profiles and transcriptomic data were analyzed to identify dysregulated pathways and to uncover the key molecular features contributing to PTC tumorigenesis and LN metastasis. Genes related to metastasis-driving metabolites were validated using the TCGA dataset, and further substantiated by zebrafish xenograft models.
Results:
Spatial mapping revealed unique metabolic and transcriptional signatures across heterogeneous regions of PTC tissue. Key metabolic pathways, such as the arginine-polyamine axis, glycolysis and lipid metabolism, were prominently dysregulated in cancer regions. The surrounding stroma also exhibited metabolic adaptations, like polyamine recycling and glucose storage, which supported tumor growth via metabolic crosstalk. Most importantly, greater spatial heterogeneity, spatial evolutionary routes and 5 potential metastasis-driving metabolites, including FA (22:6), PC (36:4), PC (34:1), N-acetylaspartate and ascorbic acid, were discovered in the primary PTC tissue with LN metastasis. Knockdown of key genes NAT8L and SVCT-2 obviously decreased the metastatic ability of PTC cells. The TCGA database further confirmed that high expression of 10 pro-metastatic metabolite-related genes was significantly associated with poorer prognosis in PTC patients.
Conclusions:
This integrated spatial multi-omics approach provides novel insights into the molecular mechanisms underlying PTC evolution, potentially guiding the development of more effective diagnostic and therapeutic strategies for PTC patients.
