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Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [totalRNA-seq]

GSE273848 Homo sapiens (human) Single-cell RNA-seq 40 samples Submitted 2024/08/02 Platform GPL21697
At a glance

Single-cell RNA-seq · human · 40 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
Esophageal cancerThis series is part of our curated briefing on it.
Samples
40 samples: 40 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
Definition of a Multi-Omics Signature for Esophageal Adenocarcinoma Prognosis PredictionCancers · 2024
Summary (written by the submitters)
Esophageal cancer, a highly lethal tumor, contributes to 5% of all cancer deaths, with its primary subtypes being esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most studies focus on ESCC, this study investigates EAC using single-cell RNA sequencing (scRNA-seq) to analyze CD45+ immune cells from tumors and matched non-tumor tissues in therapy-naïve patients.
Read the full summary ↓
By examining the transcriptional profiles of these immune cells and the entire transcriptome in a cohort of 23 patients, the study identifies distinct transcriptional signatures. These signatures were used to stratify a large cohort of TCGA EAC patients, revealing strong associations with prognosis and clinical outcomes. The findings suggest that these transcriptional profiles can improve prognosis accuracy post-surgery and potentially guide effective therapies, including immunotherapy, for EAC patients.
Published in
Definition of a Multi-Omics Signature for Esophageal Adenocarcinoma Prognosis Prediction
Lambroia L, Conca Dioguardi CM, Puccio S et al. · Cancers 2024 · PMID 39123475 · doi:10.3390/cancers16152748
This dataset
Open GSE273848 in finder → Find more human RNA-seq datasets → Esophageal 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 GSE273848_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 PRJNA1141373 and SRA study SRP523894. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39123475) ↗
Study design
40 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 40 sample titles: 40 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 Esophageal 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.