GEO series

Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissues

GSE245351 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2024/10/21 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 18 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)
Samples: 18 samples: 18 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: Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissuesScience advances · 2024

Summary (written by the submitters)

Most cancer deaths are caused by metastasis but understanding how metastatic cancers adapt from their origin tissues to their target tissues remains a fundamental challenge. Here, we ask if the gene expression of primary and metastatic tumors is more similar to their tissue of origin or to their target tissue. We analyzed the gene expression profiles of both paired and unpaired primary and metastatic tumor patient cohorts across multiple cancer types, studying single-cell and bulk RNA-seq data.

Read the full summary ↓

To quantify the similarities between tumor samples and their normal, noncancerous origin and targeted tissues, we computed the Euclidean distance between their expression profiles, termed their transcriptomic distances, at the whole-genome and pathway levels. While the expression of most primary tumors is more similar to origin than to their target tissues, the expression patterns of metastases shift towards their target tissue. In specific cases, we chart the alterations occurring at the pathway level, pointing to key adaptive transcriptomic changes. Notably, our analysis indicates that primary cancers across several cancer types are primed for metastasis before the process begins. We further reproduce our findings using a new, unpublished RNA-seq data from colorectal cancer patients with liver metastases This first of its kind systematic analysis of the expression landscape of primary tumors and metastases with respect to their normal origin and target tissues provides a transcriptomic-wide view of cancer tumors adaptations to their metastatic niches

Published in

Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissues

Sanghvi N, Calvo-Alcañiz C, Rajagopal PS et al. · Science advances 2024 · PMID 39642223 · doi:10.1126/sciadv.adn0220

This dataset

Open GSE245351 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 GSE245351_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 PRJNA1027866 and SRA study SRP466271. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39642223) ↗

Study design

18 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 18 sample titles: 18 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

GSM7841233 CtL_1C
GSM7841234 CtL_4C
GSM7841235 CtL_5C
GSM7841236 CtL_6C
GSM7841237 CtL_7C
GSM7841238 CtL_8C
GSM7841239 CtL_10C
GSM7841240 CtL_12C
GSM7841241 CtL_20C
GSM7841242 CtL_1LM
GSM7841243 CtL_4LM
GSM7841244 CtL_5LM
GSM7841245 CtL_6LM
GSM7841246 CtL_7LM
GSM7841247 CtL_8LM
GSM7841248 CtL_10LM
GSM7841249 CtL_12LM
GSM7841250 CtL_20LM

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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.