← BioTransfer GEO Dataset Finder
GEO series

Integrative multi-omics deciphers the potential mechanism and microbial biomarkers for lymph node metastasis in colorectal cancer

GSE308020 Homo sapiens (human) RNA-seq (gene expression) 66 samples Submitted 2025/12/04 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 66 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
Colorectal cancerThis series is part of our curated briefing on it.
Samples
66 samples: 33 × Colon, normal vs 33 × Colon, tumoGroups 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
Integrative multi-omics deciphers the potential mechanism and microbial biomarkers for lymph node metastasis in colorectal cancerScientific reports · 2025
Summary (written by the submitters)
Understanding and accurate diagnosis of lymph node metastasis (LNM) for patients with colorectal cancer (CRC) is essential to determine treatment and follow-up strategies. Therefore, in this study, we aimed to elucidate the biological process and identify the potential biomarker for LNM in CRC. In total, 12 significant DEGs between the patients with and without LNM were identified, consisting of significantly upregulated S100A8 gene, a proinflammatory gene.
Read the full summary ↓
The GSEA revealed that gene sets involving “MULTI CANCER INVASIVENESS” in terms related to epithelial-mesenchymal transition was significantly upregulated in the patients with LNM. Integrated functional analysis of DNA methylation with transcriptome profile shows that significantly hypomethylated promoters of the genes are enriched for LNM. The phylum Proteobacteria, unassigned (p_PU) presented significantly higher proportions in cancer tissues from the adjacent normal tissues. Notably, when compared to the patients without LNM, the gut microbiota of those with LNM appears to exhibit a significantly lower abundance of the p_PU, indicating its potential as promising biomarker for LNM in CRC. We explained the mechanism of tumor spreading using multi-omics analysis and identified the relevant metagenomic biomarker to predict the LNM in CRC by the recognition of host-microbial interaction, thereby can make the cancer surveillance of the patients more individualized and convincing.
Published in
Integrative multi-omics deciphers the potential mechanism and microbial biomarkers for lymph node metastasis in colorectal cancer
Kwak MS, Cha JM, Kim CW et al. · Scientific reports 2025 · PMID 41188324 · doi:10.1038/s41598-025-22350-2
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 GSE308020_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 PRJNA1328601 and SRA study SRP620381. Searching any of these in the dataset finder brings you back here.

Study design
33 × Colon, normal vs 33 × Colon, tumo

Supports a between-group comparison across 66 samples.

How we read this: 2 replicated groups read from 66 sample titles; they account for 66 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

+ 26 more — browse all 66 samples with per-sample file links →

More Colorectal cancer datasets

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.