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Machine-learning detects non-invasive 5-hydroxymeythylcytosine biomarkers for predicting response to chemotherapy in patients with metastatic colorectal cancer

GSE203071 Homo sapiens (human) RNA-seq (gene expression) 155 samples Submitted 2025/01/01 Platform GPL18573Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 155 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
155 samples: 153 × 5hmC_Seal SEQ vs 2 × 5hmC_Seal SEQFGroups 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)
Background: For the majority of patients of metastatic colorectal cancer (mCRC), systematic chemotherapy has been a choice for palliative treatment. When first-line chemotherapies do not work, targeted therapies are administered. Because of inter-individual genetic and epigenetic variations, patients with mCRC who are treated with these therapies may respond differently.
Read the full summary ↓
It is of great clinical interest to elucidate response mechanisms that could be utilized in predicting individual response to these treatments. Methods: A highly sensitive technique, the 5hmC-Seal was applied to profile genome-wide 5-hydroxymethylcytosines (5hmC) in 155 cfDNA samples from patients with mCRC recruited for two clinical trials at Zhongshan Hospital, Fudan University in China. Response to systematic chemotherapy and targeted therapy (becacizumab or cetuximab) (CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease) was measured and a 5hmC-based predictive model in cfDNA was developed using a machine learning approach in the PROVE Study (NCT03679039) (n = 100), followed by validation in the METHOD Study (NCT03599947) (n = 55). Results: Genome-wide mapping of 5hmC in patient-derived cfDNA suggested a distinct 5hmC landscape showing tissue origin and gene regulatory relevance. A eleven-gene model involving relevant pathways such as Rap1 signaling pathway, cAMP signaling pathway, Phospholipase D signaling pathway and cGMP-PKG signaling pathway, distinguished patients with mCRC who responded to chemotherapy, regardless of targeted therapy, resectability, or mutation status in the validation samples (AUC=0.77; 95% CI, 0.64-0.90), outperforming RAS mutation status (AUC=0.70; 95% CI, 0.57-0.83). In addition, the 5hmC model also showed a trend of capacity for predicting progression-free survival (PFS) for mCRC. Conclusions: Genome-wide mapping reflected relevant pathways and functional interaction networks implicated in the determination of treatment response for patients with mCRC. The 5hmC-Seal model in cfDNA provided a promising non-invasive method for predicting response to systematic chemotherapy in patients with mCRC, who will be essential for personalized medicine.
This dataset
Open GSE203071 in finder → Find more human RNA-seq datasets → Colorectal 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 GSE203071_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 PRJNA838485. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
153 × 5hmC_Seal SEQ vs 2 × 5hmC_Seal SEQF

Supports a between-group comparison across 155 samples.

How we read this: 2 replicated groups read from 155 sample titles; they account for 155 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

+ 115 more — browse all 155 samples with per-sample file links →

More Colorectal 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.