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Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancer

GSE289037 Homo sapiens (human) RNA-seq (gene expression) 17 samples Submitted 2025/04/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 17 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
Gastric cancerThis series is part of our curated briefing on it.
Samples
17 samples: 17 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.
Paper
Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancerNPJ precision oncology · 2025
Summary (written by the submitters)
Gastric cancer (GC) remains one of the leading causes of cancer-related death worldwide, especially in East Asian countries. Despite advances in treatment, patients with locally advanced gastric cancer (LAGC) still have a poor prognosis due to the high incidence of metastasis and recurrence. Peritoneal metastasis is particularly occult and difficult to detect by conventional imaging techniques at an early stage.
Read the full summary ↓
Existing imaging methods lack high sensitivity and are difficult to detect early metastatic lesions. Although diagnostic laparoscopy has high sensitivity, it is invasive and difficult to use routinely. Therefore, innovative diagnostic methods are urgently needed. Transcriptomics methods can provide a comprehensive understanding of the biological state of cancer, especially showing potential in the early detection of GC and peritoneal metastasis. Our objective is to provide a more sensitive diagnostic method in the latent stage of peritoneal metastasis by identifying the RNA expression pattern of cancer cells.
Published in
Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancer
Ding P, Wu H, Wu J et al. · NPJ precision oncology 2025 · PMID 40234664 · doi:10.1038/s41698-025-00875-z
This dataset
Open GSE289037 in finder → Find more human RNA-seq datasets → Gastric 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 GSE289037_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 PRJNA1220682 and SRA study SRP562235. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40234664) ↗
Study design
17 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 17 sample titles: 17 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
More Gastric cancer 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.