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Single-cell transcriptomic profiles of metastatic lesion biopsies and PBMCs from M1-stage prostate cancer patients

GSE297652 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2025/11/19 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 12 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)
Samples
12 samples: 7 × PBMC_M1 vs 5 × Single-cell RNA-seq of metastatic prostate cancer sample MGroups 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
CD45⁺ hybrid circulating cells may reflect tumor-immune interactions and serve as transcriptomic indicators of metastatic potential in prostate cancerTheranostics · 2026
Summary (written by the submitters)
This dataset contains single-cell RNA sequencing (scRNA-seq) data generated from patients with M1-stage metastatic prostate cancer, comprising two complementary sample types: metastatic lesion biopsies from five patients and peripheral blood mononuclear cells (PBMCs) from seven patients. Together, these datasets support integrated profiling of the metastatic tumor microenvironment and the systemic immune landscape.
Read the full summary ↓
The processed data include log-normalized gene expression matrices and detailed cell-level metadata, enabling analyses of tumor–immune interactions, circulating immune phenotypes, and molecular features associated with metastatic disease progression. A related bulk RNA-seq dataset from primary tumor biopsies is available (see Related Series: GSE297742), providing opportunities for comparative transcriptomic analyses between primary and metastatic disease states.
Published in
CD45⁺ hybrid circulating cells may reflect tumor-immune interactions and serve as transcriptomic indicators of metastatic potential in prostate cancer
Kim BG, Jang Y, Kim MG et al. · Theranostics 2026 · PMID 41355965 · doi:10.7150/thno.122226
This dataset
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.

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 GSE297652_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1265622. Searching any of these in the dataset finder brings you back here.

Study design
7 × PBMC_M1 vs 5 × Single-cell RNA-seq of metastatic prostate cancer sample M

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 of them. 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
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