GEO series

Micro-RNA networks in T-cell prolymphocytic leukemia reflect T-cell activation and shape DNA damage response and survival pathways

GSE318878 Homo sapiens (human) RNA-seq (gene expression) 56 samples Submitted 2026/02/09 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 56 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: 56 samples: 56 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: Single-cell genomics highlight MYC-associated metabolic activation and altered cell interactions in T-prolymphocytic leukemia progressionNature communications · 2026

Summary (written by the submitters)

T-cell prolymphocytic leukemia (T-PLL) is a poor-prognostic mature T-cell malignancy. It typically presents with exponentially rising lymphocyte counts, splenomegaly, and bone marrow infiltration. Effective treatment options are scarce and a better understanding of TPLL's pathogenesis is desirable. Activation of the TCL1 proto-oncogene and loss-of-function perturbations of the tumor suppressor ATM are TPLL's genomic hallmarks.

Read the full summary ↓

The leukemic cell reveals a phenotype of active T-cell receptor (TCR) signaling and aberrant DNA damage responses. Regulatory networks based on the profile of microRNA (miR) have not been described for T-PLL. In a combined approach of small-RNA and transcriptome sequencing in 46 clinically and moleculary well-characterized T-PLL, we identified a global T-PLL-specific miR expression profile that involves 34 significantly deregulated miR species. This pattern strikingly resembled miR-ome signatures of TCR-activated T cells. By integrating these T-PLL miR profiles with transcriptome data, we uncovered regulatory networks associated with cell survival signaling and DNA damage response pathways. Despite a miR-ome that discerned leukemic from normal T cells, there were also robust subsets of T-PLL defined by a small set of specific miR. Most prominently, miR-141 and the miR- 200c-cluster separated cases into two major subgroups. Furthermore, increased expression of miR-223-3p as well as reduced expression of miR-21 and the miR-29 cluster were associated with more activated Tcell phenotypes and more aggressive disease presentations. Based on the implicated pathobiological role of these miR deregulations, targeting strategies around their effectors appear worth pursuing. We also established a combinatorial miR-based overall survival score for T-PLL (miROS-T-PLL), that might improve current clinical stratifications.

Published in

Single-cell genomics highlight MYC-associated metabolic activation and altered cell interactions in T-prolymphocytic leukemia progression

Wahnschaffe L, Jungherz D, Müller TA et al. · Nature communications 2026 · PMID 41803141 · doi:10.1038/s41467-026-70185-w

This dataset

Open GSE318878 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 GSE318878_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 PRJNA1420758 and SRA study SRP675808. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41803141) ↗

Study design

56 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 56 sample titles: 56 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

GSM9505364 p1367_T-PLL
GSM9505365 p1381_T-PLL
GSM9505366 p1395_T-PLL
GSM9505367 p1400_T-PLL
GSM9505368 p1402_T-PLL
GSM9505369 p1478_T-PLL
GSM9505370 p1483_T-PLL
GSM9505371 p1368_T-PLL
GSM9505372 p1548_T-PLL
GSM9505373 p1391_T-PLL
GSM9505374 p1359_T-PLL
GSM9505375 BC4_control
GSM9505376 BC7_control
GSM9505377 p1488_T-PLL
GSM9505378 p210_T-PLL
GSM9505379 p598_T-PLL
GSM9505380 p1077_T-PLL
GSM9505381 p1308_T-PLL
GSM9505382 p1326_T-PLL
GSM9505383 p1371_T-PLL
GSM9505384 p1499_T-PLL
GSM9505385 BC001_control
GSM9505386 BC002_control
GSM9505387 BC003_control
GSM9505388 BC005_control
GSM9505389 p1339_T-PLL
GSM9505390 p1375_T-PLL
GSM9505391 p1481_T-PLL
GSM9505392 p1485_T-PLL
GSM9505393 p1383_T-PLL
GSM9505394 p1561_T-PLL
GSM9505395 p1570_T-PLL
GSM9505396 p1581_T-PLL
GSM9505397 p1599_T-PLL
GSM9505398 p1432_T-PLL
GSM9505399 p1434_T-PLL
GSM9505400 p1557_T-PLL
GSM9505401 p1495_T-PLL
GSM9505402 p1332_T-PLL
GSM9505403 p1080_T-PLL

+ 16 more — browse all 56 samples with per-sample file links →

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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.