GEO series

Single-cell transcriptomic analysis reveals tumor-immune determinants of lymph node colonization and progression in thyroid cancer

GSE313389 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2026/06/19 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 10 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Thyroid cancerThis series is part of our curated briefing on it.
Samples: 10 samples: 10 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: Single-cell transcriptomic analysis reveals tumor-immune determinants of lymph node colonization and progression in thyroid cancerScience advances · 2026

Summary (written by the submitters)

Lymph node (LN) metastases are a major driver of mortality across solid cancers, including thyroid carcinomas, which are known for high rates of nodal colonization. To elucidate the determinants of nodal spread, we isolated tumor-infiltrating leukocytes from primary thyroid tumors and matched metastatic LNs for single-cell RNA sequencing with validation by multiplex immunohistochemistry.

Read the full summary ↓

Comparing the microenvironmental alterations between primary tumors and their LNs, we found that thyrocytes and tumor-associated macrophages downregulate the expression of multiple inflammatory cytokine receptors, including TNFRSF12A and CX3CR1, upon LN colonization. LNs were associated with the induction of regulatory T cells to suppress T cell-mediated cytotoxicity compared to matched primary tumors. Notably, tumor-infiltrating lymphocytes within LNs demonstrated increased expression of activation markers, including interleukin-7 receptor (IL7R). High LN expression of IL7R was significantly correlated with improved outcomes and can serve as a biomarker in this heterogeneous disease. Our findings on the dynamic equilibrium within LN metastases may offer conserved mechanisms for nodal colonization across solid tumors.

Published in

Single-cell transcriptomic analysis reveals tumor-immune determinants of lymph node colonization and progression in thyroid cancer

Nguyen AT, Viramontes J, Vazquez I et al. · Science advances 2026 · PMID 42397917 · doi:10.1126/sciadv.aea4727

This dataset

Open GSE313389 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 GSE313389_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 PRJNA1379434 and SRA study SRP654346. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42397917) ↗

Study design

10 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 10 sample titles: 10 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

GSM9367324 Tumor, Diffuse Sclerosing Variant
GSM9367325 Lymph Node, Diffuse Sclerosing Variant
GSM9367326 Tumor, Classical PTC, 12A
GSM9367327 Lymph Node, Classical PTC, 12B
GSM9367328 Tumor, Anaplastic
GSM9367329 Lymph Node, Anaplastic
GSM9367330 Lymph Node, Tall Cell Variant
GSM9367331 Tumor, Tall Cell Variant
GSM9367332 Tumor, Classical PTC, 3A
GSM9367333 Lymph Node, Classical PTC, 3B

More Thyroid cancer datasets

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GSE272374 Maintenance of magnesium homeostasis by NUF2 promotes protein synthesis and anaplastic thyroid cancer progression 6 samples same disease briefing · shares progression, thyroid
GSE285569 CHI3L1 regulates papillary thyroid tumor progression via TP53 signaling 6 samples same disease briefing · shares progression, thyroid
GSE307253 GDF15 contributes to thyroid cancer progression and modulates thyroid cancer cell senescence in a p53-dependent manner. 6 samples same disease briefing · shares progression, thyroid
GSE274674 Single-cell RNA sequencing reveals a novel CD4+T cell subset in papillary thyroid cancer 4 samples same disease briefing · shares single-cell, thyroid
GSE158291 Single-cell transcriptomic landscape reveals the differences in cell differentiation and immune microenvironment of papillary thyroid carcinoma between male and female patients 3 samples same disease briefing · shares single-cell, thyroid
GSE315375 Increased MALAT1 Levels in Thyroid Cancer are Linked to Metabolic Reprogramming and Lymphatic Infiltration in Chilean population 2 samples same disease briefing · shares thyroid, lymph

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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.