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Deciphering functional intra-tumoral heterogeneity in BRAFV600E-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling

GSE311419 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2026/01/07 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 2 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
2 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Deciphering functional intra-tumoral heterogeneity in BRAF(V600E)-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodelingOncogene · 2026
Summary (written by the submitters)
A better understanding of functional heterogeneity of tumor cells may lead to the improvement of the diagnosis and development of effective therapies for patients with papillary thyroid cancer (PTC). Here, the transcriptional heterogeneity and hierarchical trajectory of malignant thyrocytes were investigated by single-cell RNA sequencing analysis in an adult-onset autochthonous mouse model of PTC driven by a BRAFV600E.
Read the full summary ↓
Within BRAFV600E thyrocytes, we identified distinct subpopulations exhibiting varying degrees of mesenchymal transformation and stability. Further analysis uncovered an epithelial-mesenchymal transition (EMT) trajectory, where malignant subpopulations progress from an intermediate to a more mesenchymal and malignant state. The EMT-related phenotype of each subpopulation was validated with an organoid culture system. Further analysis demonstrated that major malignant subpopulations maintain distinct EMT states through subpopulation-specific pathway usage, whereas transitioning cells undergo a dynamic shift in molecular strategy by using different pathway combinations or distinct gene sets within the same EMT-related pathways along the EMT trajectory. Moreover, metformin diminished the most malignant subpopulation in the PTC mouse model and inhibited EMT potential in both regular and organoid cultures. Importantly, the functional heterogeneity of tumor cells identified in PTC mouse model have clinical relevance. Furthermore, transcriptome dynamics analysis revealed an EMT trajectory-related gene module with substantial predictive value for human PTCs with BRAFV600E. Lastly, our analysis reveals that p53 deficiency lowers the tumorigenic threshold for BRAFV600E and promotes cell state transition towards a more malignant state, rather than altering cellular heterogeneity or the transition trajectory. Subsequent analysis in vitro demonstrated that p53 loss might confer more malignant potential to BRAFV600E thyrocytes through modulating metabolic plasticity. Collectively, our work not only deciphers the functional intra-tumoral heterogeneity of BRAFV600E-drieven PTC but also thereby provides a theoretical foundation for developing EMT-targeted treatment.
Published in
Deciphering functional intra-tumoral heterogeneity in BRAF(V600E)-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling
Yu J, Shen S, Luo R et al. · Oncogene 2026 · PMID 41935217 · doi:10.1038/s41388-026-03742-8
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE311419_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1369432 and SRA study SRP648879. Searching any of these in the dataset finder brings you back here.

Samples in this study

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