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Transcriptomic analysis of GKB202-treated circulating tumor cells isolated from patients with lung cancer and pancreatic cancer

GSE333988 Homo sapiens (human) RNA-seq (gene expression) 10 samples Submitted 2026/06/04 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 10 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
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
10 samples: 10 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)
Circulating tumor cells (CTCs) are associated with metastatic dissemination and adaptive stress responses in cancer. In this study, patient-derived CTCs isolated from lung cancer and pancreatic cancer samples were treated with GKB202, a natural compound derived from Antrodia cinnamomea, to investigate treatment-associated transcriptomic changes.
Read the full summary ↓
Bulk RNA sequencing was performed to compare untreated/control and GKB202-treated CTC samples. This dataset provides raw sequencing reads and processed gene-level expression matrices for evaluating the effects of GKB202 on CTC-related transcriptional programs, including stress-response and cancer-associated pathways.
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 GSE333988_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: 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 PRJNA1473344 and SRA study SRP705882. Searching any of these in the dataset finder brings you back here.

Study design
10 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 5 samples read as cases, 3 as controls.

How we read this: Read from 10 sample titles: 10 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
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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.