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Single cell profiling of peripheral blood mononuclear cells in diverse men with prostate cancer reveals lethal disease-associated immune signatures

GSE270387 Homo sapiens (human) Single-cell RNA-seq 165 samples Submitted 2026/07/24 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 165 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
165 samples: 165 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)
Purpose: Prostate cancer disproportionately affects men of African ancestry (AA), who also exhibit a distinct immunobiological profile. We aimed to investigate the composition and gene expression patterns of circulating peripheral blood mononuclear cells (PBMCs) in AA and European American (EA) prostate cancer patients, and to evaluate their association with ancestry and lethal prostate cancer outcomes.
Read the full summary ↓
Experimental Design: We performed single-cell RNA sequencing (scRNA-seq) on 273,229 high-quality PBMCs isolated from 59 prostate cancer patients and healthy controls, along with single-cell TCR and BCR sequencing (scTCR-seq and scBCR-seq) in 53 individuals of African American (AA) and European American (EA) ancestry. Immune cell subpopulations were characterized, and differential gene expression and pathway analyses were conducted to identify ancestry-related differences and signatures associated with lethal disease. Results: Prostate cancer patients showed increased CD4⁺ cytotoxic and CD8⁺ effector memory T cells, with reduced CD4⁺ naïve and CD56 bright NK cells, indicating immune activation and dysregulation. AA patients had elevated CD4⁺ CTLs and cDC1s and fewer CD56 bright NK cells compared to EA. Lethal cases showed reduced cDC2s, suggestive of impaired immune priming. CD16⁺ monocytes shared 98 genes and 9 pathways across race and lethality contrasts, including enhanced interferon signaling in AA patients. Inflammatory and cytokine pathways—especially TNFα and NF-κB—were enriched in AA and in lethal disease, indicating shared immune dysregulation. TCR profiling revealed more large and hyperexpanded clonotypes in lethal cases, reflecting antigen-driven T cell responses and potential exhaustion. BCR sequencing showed reduced repertoire diversity and greater clonal dominance in lethal and EA patients, consistent with skewed humoral responses. Conclusions: Our findings reveal ancestry- and lethality-associated immune differences in prostate cancer. Elevated T cell exhaustion and inflammatory signaling in AA patients correlated with poor outcomes, highlighting potential ancestry-informed biomarkers and targets. Expanded T and B cell clones in lethal cases suggest antigen-driven immune activation with possible dysfunction.
This dataset
Open GSE270387 in finder → Find more human RNA-seq datasets → Prostate 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 GSE270387_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 PRJNA1126289 and SRA study SRP515095. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
165 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 165 sample titles: 165 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

+ 125 more — browse all 165 samples with per-sample file links →

More Prostate cancer datasets

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