ArticleBritish journal of cancer2026
Integrated multi-omics and single-cell analyses identify metabolic heterogeneity and therapeutic vulnerabilities in medullary thyroid cancer.
Article in British journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundMedullary 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.
methodsRNA 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.
resultsThree 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.
conclusionsOur 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.