GEO series

Delayed processing of CPT-collected blood preserves transcriptomic and genomic features of propagated circulating tumor cells

GSE333160 Homo sapiens (human) RNA-seq (gene expression) 9 samples Submitted 2026/05/27 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 9 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: 9 samples: 7 distinct titles, 1 of them repeatedGroups 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)

Circulating tumor cells (CTCs) provide a minimally invasive window into metastatic disease but are constrained by the need for rapid specimen processing after blood collection. We evaluated whether a workflow utilizing BD Vacutainer CPT-based blood collection could support delayed processing while preserving molecular features of propagated CTC-derived material.

Read the full summary ↓

In a pilot cohort of four patients, paired CPT tubes were collected; one tube was processed within 2 hours of phlebotomy, whereas the second was stored at 4°C and processed 24 hours later. Both tubes were then harvested for CTCs, which were propagated ex vivo and analyzed by bulk RNA sequencing and whole-exome sequencing. Transcriptomic analyses showed that paired CPT-derived samples demonstrated phenotypes consistent with CTCs, including reduced immune-associated signatures and enrichment of epithelial-mesenchymal transition and KRAS signaling pathways. Direct comparison of paired early- and delayed-processed CPT samples demonstrated strong within-patient gene expression concordance. Whole-exome sequencing further revealed preservation of patient-specific oncogenic alterations and high overlap of detected variants across paired processing conditions. These findings altogether support the feasibility of a CPT-based delayed processing workflow for propagating and studying CTCs, thereby reducing a key logistical barrier to broader CTC research.

This dataset

Open GSE333160 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 GSE333160_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 PRJNA1469676 and SRA study SRP703138. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

7 distinct titles, 1 of them repeated

WBM ×3

How we read this: Read from 9 sample titles: 7 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

GSM9758112 CTC 111 CPT B
GSM9758113 CTC 114 CPT A
GSM9758114 CTC 114 CPT B
GSM9758115 CTC 110 CPT A
GSM9758116 CTC 110 CPT B
GSM9758117 CTC 111 CPT A
GSM9758118 WBM 110
GSM9758119 WBM 111
GSM9758120 WBM 114

Datasets on a similar topic

GSE310095 Whole Blood Transcriptomics Differentiates Circulating Gene Expression Between Coronary Artery Disease and Peripheral Artery Disease 71 samples shares circulating, blood
GSE81314 Shotgun sequencing of 5-hydroxymethylcysotine circulating cell-free DNA from the blood distinguishes solid tumor tissues of origin 126 samples shares circulating, blood
GSE279168 Systematic characterization of the composition and dynamics of processing body-associated mRNAs [dataset 1] 458 samples shares processing
GSE285943 A reference model for circulating hematopoietic stem cells with applications to diagnostics 299 samples shares circulating
GSE200996 Tissue-resident Memory and Circulating T cells are Early Responders to Pre-surgical Cancer Immunotherapy 204 samples shares circulating
GSE293840 Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) 168 samples shares circulating
GSE307931 Circulating transcriptome analysis finds gene signatures and immune cell activation and abundance predict response to immunotherapy in bladder cancer 164 samples shares circulating
GSE171741 Albumin treatment transcriptionally reprograms circulating immune cells in patients with acutely decompensated cirrhosis 139 samples shares circulating

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.