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Single-cell transcriptome profiling of the stepwise progression of head and neck cancer

GSE181919 Homo sapiens (human) Single-cell RNA-seq 37 samples Submitted 2022/08/31 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 37 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
Head and neck cancerThis series is part of our curated briefing on it.
Samples
37 samples
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.
Summary (written by the submitters)
Head and neck squamous cell carcinoma (HNSCC) undergoes stepwise progression from normal tissue to metastasized tumors. To comprehensively delineate the heterogeneity of tumor cells and their interactions during the progression of HNSCC, we employed single-cell RNA-seq (scRNA-seq) profiling for the tissues of normal, leukoplakia, HNSCC, and metastasized HNSCC.
Read the full summary ↓
We identified novel clones of malignant cells expressing LGALS7B and fibroblasts expressing CXCL8, and the abundance of these cells in the tumor tissue was associated with poorer prognostic outcomes. In addition, we demonstrated that fibroblasts in leukoplakia and tumor tissue express COL1A1, which interacts with CD44+ malignant cells, facilitating HNSCC progression. CD4+FOXP3+ regulatory T cells expand in leukoplakia and express LAIR2 with collagen stimulation, which may provide a favorable microenvironment for tumor progression. In conclusion, stromal and immune cell interactions play critical roles in the stepwise progression of HNSCC, and we have identified steps during HNSCC progression for possible therapeutic interventions
This dataset
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.

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 GSE181919_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA753861 and SRA study SRP332116. Searching any of these in the dataset finder brings you back here.

Samples in this study

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