← BioTransfer GEO Dataset Finder
GEO series

PRECISE: A prognostic thyroid follicular-cell derived gene signature for papillary thyroid carcinoma

GSE310793 Homo sapiens (human) Single-cell RNA-seq 109 samples Submitted 2026/02/18 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 109 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
Homo sapiens (human)
Topic
Thyroid cancerThis series is part of our curated briefing on it.
Samples
109 samples: 109 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
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
An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulationsJCI insight · 2026
Summary (written by the submitters)
Papillary thyroid carcinoma (PTC) exhibits heterogeneous behavior. There is a need for effective biomarkers and improved risk stratification methods in PTC. Gene signatures derived from bulk RNA sequencing capture composite transcriptional profiles that are confounded by tumor microenvironment cells. We developed a thyroid follicular cell–enriched gene signature, Prognostic RNA Expression Cell-specific Integrated SignaturE (PRECISE), using single-cell RNA sequencing techniques and evaluated its prognostic utility across three independent cohorts with bulk RNA-seq data and survival outcomes.
Published in
An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations
Loberg MA, Xu GJ, Chen SC et al. · JCI insight 2026 · PMID 41480746 · doi:10.1172/jci.insight.191990
This dataset
Open GSE310793 in finder → Find more human RNA-seq datasets → Thyroid cancer landscape →
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.

Series matrix ↓
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 GSE310793_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1367083 and SRA study SRP648450. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41480746) ↗
Study design
109 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 109 sample titles: 109 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 69 more — browse all 109 samples with per-sample file links →

More Thyroid cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.