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Proteogenomic Characterization of Primary Oral Cancer Unveils Extracellular Matrix Remodeling and Immunosuppressive Microenvironment Linked to Lymph Node Metastasis [RNA-seq]

GSE289930 Homo sapiens (human) Single-cell RNA-seq 33 samples Submitted 2025/03/12 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 33 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
Head and neck cancerThis series is part of our curated briefing on it.
Samples
33 samples: 33 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
Proteogenomic characterisation of primary oral cancer unveils extracellular matrix remodelling and immunosuppressive microenvironment linked to lymph node metastasisClinical and translational medicine · 2025
Summary (written by the submitters)
Our genomic analysis identified significant mutations in key genes within the MAPK, TGF-β, and WNT signaling pathways, which are essential for tumor development. The proteogenomic analysis highlighted pathways critical for lymph node dissemination and factors contributing to an immunosuppressive tumor microenvironment. Elevated levels of POSTN were found to reorganize the extracellular matrix (ECM), interact with TGF-β, disrupt cell cycle regulation, and suppress the immune response by reducing VCAM1 activity.
Read the full summary ↓
Integrated analyses of single-cell and spatial transcriptome data revealed that cancer-associated fibroblasts (CAFs) secrete TGF-β1/2, promoting cancer cell metastasis through epithelial-mesenchymal transition (EMT). Our integrated multi-omics analysis provides a detailed understanding of molecular mechanisms driving lymph node metastasis of OSCC. These insights could lead to more precise diagnostics and targeted treatments.
Published in
Proteogenomic characterisation of primary oral cancer unveils extracellular matrix remodelling and immunosuppressive microenvironment linked to lymph node metastasis
Liu Y, Yang Z, Pu JJ et al. · Clinical and translational medicine 2025 · PMID 40038875 · doi:10.1002/ctm2.70261
This dataset
Open GSE289930 in finder → Find more human RNA-seq datasets → Head and neck 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 GSE289930_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 PRJNA1225597 and SRA study SRP564908. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40038875) ↗
Study design
33 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 33 sample titles: 33 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
More Head and neck cancer datasets

Search all human RNA-seq datasets in GEO →

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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.