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Updated: May 9, 2026
Spontaneous Murine Model of Anaplastic Thyroid Cancer
Published on: February 3, 2023
Integrated multi-omics and single-cell analyses identify metabolic heterogeneity and therapeutic vulnerabilities in
Chuqiao Liu1, Cenkai Shen2, Yingtong Hou3
1Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Background:
Medullary thyroid cancer (MTC) is a heterogeneous and aggressive malignancy with limited therapeutic options. Metabolic reprogramming, a hallmark of cancer, may offer a promising avenue for understanding and managing MTC.
Methods:
RNA sequencing data of 101 MTC samples were obtained from a published dataset PRJCA008783, and untargeted metabolomic profiling was performed on 51 paired samples. Metabolic subtypes were identified using clustering analyses and validated using immunohistochemistry (47 cases), multiplex immunofluorescence (12 cases), and a previously published single-cell RNA sequencing dataset (7 cases derived from PRJCA021386). Deep learning-based approaches were applied to develop prognostic models.
Results:
Three metabolic subtypes were identified. The M3 subtype, associated with poor prognosis, was characterised by upregulated glycosaminoglycan (GAGs) biosynthesis, particularly chondroitin sulfate, and elevated expression of CHSY1, a key GAGs biosynthetic enzyme. M3 tumours displayed enhanced epithelial-mesenchymal transition (EMT) signatures. Multi-omic analyses implicated CHSY1 may promote EMT through interactions with myofibroblasts, which was supported by immunohistochemistry and immunofluorescence. Two prognostic classifiers, the 8 Metabolites Model and the 28 Metabolic Genes Model, effectively stratified patients by recurrence risk, with predictive power largely driven by GAGs-associated metabolism.
Conclusions:
Our study reveals substantial metabolic heterogeneity in MTC and proposes a novel metabolic classification system, offering mechanistic insights and supporting metabolite-driven prognostication for precision management of MTC.
Insights
Medullary thyroid cancer (MTC) exhibits metabolic heterogeneity. A new classification identifies a poor-prognosis subtype linked to glycosaminoglycan (GAGs) biosynthesis, enabling metabolite-driven prognostication for MTC.
Area of Science:
- Oncology
- Metabolomics
- Cancer Biology
Background:
- Medullary thyroid cancer (MTC) is an aggressive malignancy with few treatment options.
- Metabolic reprogramming is a key cancer hallmark offering potential therapeutic targets.
- Understanding MTC metabolism is crucial for developing novel management strategies.
Purpose of the Study:
- To investigate metabolic heterogeneity in MTC.
- To identify distinct metabolic subtypes of MTC.
- To develop prognostic models based on metabolic profiles for precision medicine.
Main Methods:
- RNA sequencing and untargeted metabolomic profiling of MTC samples.
- Clustering analyses to identify metabolic subtypes, validated by immunohistochemistry, multiplex immunofluorescence, and single-cell RNA sequencing.
- Deep learning approaches to create prognostic classifiers.
Main Results:
- Three metabolic subtypes of MTC were identified.
- The M3 subtype, associated with poor prognosis, showed increased glycosaminoglycan (GAGs) biosynthesis (chondroitin sulfate) and elevated CHSY1 expression.
- M3 tumors exhibited enhanced epithelial-mesenchymal transition (EMT) signatures, with CHSY1 potentially promoting EMT via myofibroblast interactions. Prognostic models based on metabolites and genes effectively stratified patients by recurrence risk, driven by GAGs metabolism.
Conclusions:
- MTC displays significant metabolic heterogeneity.
- A novel metabolic classification system for MTC was proposed.
- Metabolite-driven prognostication offers potential for precision management of MTC.
