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Functional Genomics and Tumor Microenvironment Analysis Reveal Prognostic Biological Subtypes in Mantle Cell Lymphoma [RNA-seq]

GSE271664 Homo sapiens (human) RNA-seq (gene expression) 51 samples Submitted 2025/08/27 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 51 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
Mantle cell lymphomaThis series is part of our curated briefing on it.
Samples
51 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.
Paper
Functional genomics and tumor microenvironment analysis reveal prognostic biological subtypes in Mantle cell lymphomaNature communications · 2025
Summary (written by the submitters)
Mantle cell lymphoma (MCL) is a genetically and clinically heterogeneous B-cell malignancy. We study two MCL cohorts with differing treatment patterns: one enriched for immunochemotherapy, the other for chemotherapy alone. TP53 alterations are consistently associated with poor prognosis, whereas ATM mutations correlate with improved outcomes following rituximab-based chemotherapy.
Read the full summary ↓
Based on recurrent genetic events, six clusters are identified and refined into three prognostic groups: high-risk (TP53 mutations and deletions at 17p13.3, 13q14.2, and 19p13.3), intermediate-risk (ATM and epigenetic regulator mutations, or gains at 8q/17q/15q), and low-risk (lacking TP53 alterations, rare ATM mutations without 11q deletions, gains at 3q, deletions at 6q). Transcriptomic analysis reveals enrichment of proliferation, metabolism-promoting gene signatures in high-risk; angiogenesis and NOTCH signaling in intermediate-risk; and proinflammatory-related (i.e., IFNα, TNFα) in low-risk MCLs. Multi-proteomic spatial profiling using imaging mass cytometry (IMC) demonstrates enrichment of CD4⁺ T cells with high expression of exhaustion markers and dominant population of myeloid cells skewed toward an M2-like phenotype. Spatially, TP53-perturbed MCLs are immune infiltrated but exhausted, while ATM-perturbed cases remain immune-cold with dense tumor. Functional analysis shows that p53 represses BCR signaling through PTPN6 activation. Collectively, these findings highlight distinct molecular and immune landscapes and reveal therapeutic vulnerabilities in high-risk TP53-perturbed MCL.
Published in
Functional genomics and tumor microenvironment analysis reveal prognostic biological subtypes in Mantle cell lymphoma
Sharma S, Ali R, Bouska A et al. · Nature communications 2025 · PMID 41193463 · doi:10.1038/s41467-025-64666-7
This dataset
Open GSE271664 in finder → Find more human RNA-seq datasets → Mantle cell lymphoma 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 GSE271664_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 PRJNA1132725 and SRA study SRP518387. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41193463) ↗
Study design
A cohort of 51 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 COHP_50469 … COHP_50519 and carry no group information.

Samples in this study

+ 11 more — browse all 51 samples with per-sample file links →

More Mantle cell lymphoma 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.