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Transcriptome-based classification of resected pancreatic ductal adenocarcinoma enhances prognostic modelling accuracy of overall survival following adjuvant treatment

GSE310252 Homo sapiens (human) RNA-seq (gene expression) 118 samples Submitted 2026/04/14 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 118 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
118 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)
In pancreatic ductal adenocarcinoma, patient outcomes after resection remain highly variable. Prognostic models are often inaccurate. Our study aimed to improve survival prediction by adding transcriptome-based classification to a validated prognostic model and applying it on a multicenter real-world cohort. Fresh-frozen tumor resection materials were obtained from the Dutch Pancreas Parel biobank.
Read the full summary ↓
RNA was sequenced if tumor cellularity was >30%. The samples were classified using transcriptome-based classification. Survival differences between transcriptome-based subtypes were studied in patients treated with and without adjuvant chemotherapy. Both patients receiving neoadjuvant treatment (NAT) and patients undergoing immediate surgery were included. Samples of 461 patients were collected, of which 118 samples underwent RNA sequencing. Of those, 39.0% were of the basal-like subtype and 61.0% were of the classical subtype. The basal-like subtype became dominant after NAT (63.3%, p = 0.004). Adjuvant gemcitabine-treated patients with a classical tumor survived longer than those with a basal-like tumor (median overall survival (OS): 22.8 vs. 11.4 months; p < 0.001). In multivariable Cox regression, the classical subtype significantly associated with increased survival (hazard ratio = 0.38; p = 0.002) and adding transcriptome-based subtyping significantly improved the prognostic model (p = 0.002). Subtype and adjuvant treatment independently statistically significantly associated with OS. Transcriptome-based subtyping significantly adds to clinical variables in survival prediction after surgery. The independent associations for subtype and adjuvant treatment with OS indicate that subtypes are prognostic, but not predictive for OS with adjuvant treatment. The provided prognostic information could potentially support treatment decisions and serve as stratification factor.
This dataset
Open GSE310252 in finder → Find more human RNA-seq datasets → Pancreatic 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 GSE310252_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1365527. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 118 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run fr23588343 … fr34227609 and carry no group information.

Samples in this study

+ 78 more — browse all 118 samples with per-sample file links →

More Pancreatic 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.