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Individualized transcriptomic changes in malignant and benign prostate identify markers of disease recurrence

GSE316750 Homo sapiens (human) RNA-seq (gene expression) 82 samples Submitted 2026/05/26 Platform GPL21290
At a glance

RNA-seq (gene expression) · human · 82 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)
Samples
82 samples: 82 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.
Summary (written by the submitters)
More accurate methods for early prediction of cancer aggressiveness are essential for enhanced prostate cancer (PC) treatment. In this study, the transcriptome profiles from malignant and adjacent benign prostate tissue samples were compared in 41 patients who underwent radical prostatectomy. Hierarchical clustering clearly separated benign and malignant tissues and included expected transcriptional changes associated with carcinogenesis.
Read the full summary ↓
Using reproducibility-optimized statistical testing (ROTS), we identified 45 genes whose expression differed between paired malignant and benign prostate tissue from the same patient (FDR < 0.05) in patients with and without biochemical recurrence (BCR). Within this individualized approach, two interconnected regulatory pathways underlying the pathogenesis were identified: one centered on FOS, NR4A, and EGR family transcription factors, with strong involvement of TNFα signaling, and another, a less dense network, centered on IL6. Expression of the 45 identified transcripts was also higher in the more aggressive Luminal B-type PC than in Luminal A- and Basal-type PC, further underscoring their association with PC aggressiveness. A machine learning method (SIVS) was then used to further define five transcripts, BPIFB2, C11orf96, DUSP5, NR4A2, and NR4A3, whose individualized malignant to benign expression differences were most strongly associated with an increased risk of BCR, and thus, providing novel information on their potential involvement in processes increasing the PC aggressiveness.
This dataset
Open GSE316750 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 GSE316750_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 PRJNA1404129 and SRA study SRP663580. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
82 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 82 sample titles: 82 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

+ 42 more — browse all 82 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.