Related Experiment Video
Updated: Jun 17, 2026

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
Published on: May 31, 2020
Multi-Modal Metabolomics Deciphers Pan-Cancer Metabolic Landscapes and Spatial-Niche-Specific Alternations
Tingze Feng1,2, Hai-Long Piao1,2,3, Di Chen1,2
1State Key Laboratory of Phytochemistry and Natural Medicines, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 568 Lvshunzhong Road, Dalian 116051, China.
Background:
Metabolic reprogramming is a hallmark of cancer and supports tumor growth and adaptation within the tumor microenvironment (TME). The complexity of this reprogramming manifests as both distinct variations across cancer types and spatial heterogeneity within individual tumors. The specificity of these metabolic alterations, whether to cancer type, spatial niche, or as shared features, remains unclear, highlighting a critical gap in our systematic, pan-cancer understanding of metabolic reprogramming.
Methods:
We integrated bulk metabolomics and spatial metabolomics to investigate pan-cancer metabolic features and used blood-based metabolomics and spatial transcriptomics data to validate key findings. Metabolic differences were compared between tumor and normal tissues across multiple cancer types at the bulk level to identify metabolic modules shared across cancers or specific to individual cancer types. A two-step clustering framework was applied to identify both local and global TME-associated spatial metabolic modules of spatial metabolomics data from various tumor tissue slices.
Results:
We have identified a spectrum of metabolic features, including those specific to individual cancer types or spatial architectures and others shared across cancers, with some features emerging only at bulk-level and others uniquely discernible through spatial metabolomics. Integrative analyses also identified 19 metabolites consistently altered in both bulk and spatial data, especially carnitine species, which also showed concordant changes in blood samples and spatial associations with genes involved in fatty acid metabolism.
Conclusions:
This pan-cancer, multi-scale integrative analysis highlights substantial metabolic heterogeneity within the TME and across cancer types and identifies metabolites with consistent alterations across analytical layers, providing candidate features for future studies of tumor metabolism and potential metabolic biomarkers.
More Related Videos
07:47Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
Published on: September 15, 2023
06:51Dual-modality Molecular Cartography: Integrating Multiplex mRNA Detection with Protein Imaging Mass Cytometry
Published on: November 14, 2025
Related Concept Videos
Adaptive Mechanisms in Cancer Cells
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Mouse Models of Cancer Study
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Adaptive Mechanisms in Cancer Cells
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Mouse Models of Cancer Study
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...