GEO series

Single-cell and spatial transcriptomics identify COL6A3 as a prognostic biomarker in undifferentiated pleomorphic sarcoma

GSE281529 Homo sapiens (human) Spatial transcriptomics 1 samples Submitted 2024/11/10 Platform GPL34281

At a glance

Spatial transcriptomics · human · 1 samples

What it is: Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism: Homo sapiens (human)
Topic: Soft tissue sarcomaThis series is part of our curated briefing on it.
Samples: 1 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)

Undifferentiated pleomorphic sarcoma (UPS) and related tumors are the most common type of soft tissue sarcoma. However, this spectrum of tumors has different etiologies with varying rates of metastasis and survival. Two dermal-based neoplasms in this class of pleomorphic sarcomas, atypical fibroxanthoma (AFX) and pleomorphic dermal sarcoma (PDS), are challenging to differentiate at initial biopsy but vary significantly in prognosis.

Read the full summary ↓

We performed single-cell transcriptomics on five AFX and PDS biopsy specimens as well as both single-cell and spatial transcriptomics on one PDS excision specimen to better characterize these tumors. The top differential genes between AFX and PDS were predictive of overall survival in 17 other cancers included in the Human Protein Atlas. Of these genes, COL6A3 and BGN predicted overall survival and metastasis-free survival in independent cohorts of 46 and 38 UPS tumors, respectively. COL6A3 was most predictive of overall survival in UPS patients and outperformed an established sarcoma prognostic gene panel at predicting metastasis in UPS.

This dataset

Open GSE281529 in finder → Find more human RNA-seq datasets → Soft tissue sarcoma 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 GSE281529_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: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA1184336 and SRA study SRP544323. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 1 more — browse all 1 samples with per-sample file links →

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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.