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Mitochondrial DNA Mutations Drive Tumor Heterogeneity in Papillary Thyroid Carcinoma

GSE317520 Homo sapiens (human) RNA-seq (gene expression) 92 samples Submitted 2026/07/15 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 92 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Thyroid cancerThis series is part of our curated briefing on it.
Samples
92 samples: 92 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
Mitochondrial DNA mutations drive tumor heterogeneity in papillary thyroid carcinomaiScience · 2026
Summary (written by the submitters)
Tumor heterogeneity presents a major challenge to understanding cancer evolution and therapeutic resistance in thyroid cancer. While previous studies have focused on nuclear driver mutations, the role of mitochondrial DNA (mtDNA) alterations in this heterogeneity remains elusive. Through multi-regional whole-genome and transcriptome sequencing of aggressive papillary thyroid carcinoma (PTC), we reveal that mtDNA mutations exhibit significant spatial and evolutionary heterogeneity, contrasting with conserved nuclear drivers.
Read the full summary ↓
High mtDNA mutation burden correlated with increased mitochondrial gene expression, tumor dedifferentiation, and immune pathway activation via damage-associated molecular pattern signaling. This burden further remodeled the tumor microenvironment, favoring pro-inflammatory epithelial states over normal stromal populations. Clinically, these features were associated with reduced disease-free survival. Our findings identify mtDNA mutations as key drivers of heterogeneity and immune remodeling in PTC, suggesting mitochondrial mutational burden as a potential prognostic biomarker and therapeutic target.
Published in
Mitochondrial DNA mutations drive tumor heterogeneity in papillary thyroid carcinoma
Lee HS, Lim J, Heo JH et al. · iScience 2026 · PMID 42436980 · doi:10.1016/j.isci.2026.116361
This dataset
Open GSE317520 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 GSE317520_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1414416. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42436980) ↗
Study design
92 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 92 sample titles: 92 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

+ 52 more — browse all 92 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.