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A Transcriptome-based Molecular Classification Scheme for Thyroid Eye Disease

GSE285190 Homo sapiens (human) RNA-seq (gene expression) 195 samples Submitted 2025/12/09 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 195 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)
Samples
195 samples: 195 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
Integrative transcriptomic profiling and machine learning reveal hypoxia-associated molecular signatures for precision diagnosis in thyroid eye diseaseHuman genomics · 2025
Summary (written by the submitters)
Thyroid eye disease (TED) is an orbital autoimmune disease which severely affects vision, appearance, and quality of life. TED is the most common extrathyroidal manifestation of Graves' disease, which is mainly featured by hyperthyroidism. Current assessments of TED severity and activity, do not effectively guide regimen, especially the selection of target agents.
Read the full summary ↓
Peripheral blood samples from 161 TED patients and 20 Graves' hyperthyroidism (GH) patients without eye disease were collected for transcriptome sequencing, and corresponding clinical information was systematically compiled. We then comparatively derived a unique gene expression profile for TED compared to GH, and based on this, unsupervised clustering of TED patients using ConsensusClusterPlus was conducted. Next, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), hub gene screening, and drug sensitivity analysis were performed for different clusters. TED exhibited elevated of autoantigens, immune responses, cellular proliferation, and metabolic disorders than GH. A novel two-cluster classification scheme was established for TED, independent of classical assessment of severity and activity. Cluster 1 patients received more intravenous glucocorticoids and surgeries but experienced less pain and excessive tearing. They had higher peripheral blood lymphocytes, including CD4+ T cells, CD8+ T cells, T follicular helper cells, natural killer cells, and B cells, than cluster 2 patients. WGCNA identified cluster-related gene modules, and least absolute shrinkage and selection operator (LASSO) regression analysis yielded six hub genes (DDR2, LRP1B, NTRK2, PALB2, PTPRD, and SLC25A6) for cluster 1 and three hub genes (MON2, SNORA74B, and STX16) for cluster 2 as potential biomarkers. Considering current TED therapies, cluster 1 patients may prefer insulin-like growth factor 1 receptor (IGF-1R) antibodies, while cluster 2 patients may favor glucocorticoids. We described the specific gene profile of TED compared to GH and built a new molecular classification of TED patients into two clusters with different immune infiltration, hub genes, and drug sensitivity. It paves the way for improved application of precision medicine in TED.
Published in
Integrative transcriptomic profiling and machine learning reveal hypoxia-associated molecular signatures for precision diagnosis in thyroid eye disease
Qian W, Zhu T, Liu J et al. · Human genomics 2025 · PMID 41287009 · doi:10.1186/s40246-025-00860-4
This dataset
Open GSE285190 in finder → Find more human RNA-seq datasets →
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 GSE285190_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 PRJNA1201766 and SRA study SRP555016. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41287009) ↗
Study design
195 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 195 sample titles: 195 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

+ 155 more — browse all 195 samples with per-sample file links →

Datasets on a similar topic

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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.