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Recapitulating Patient-to-Patient Colorectal Cancer Tumor Heterogeneity Using Patient-Derived Xenograft Cells in an Engineered Tissue Model

GSE311262 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/12/01 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 18 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)
Establishing in vitro cancer models that more closely recapitulate patient tumor microenvironmental heterogeneity, including variations in stromal cells and mechanical properties that influence colorectal cancer (CRC) progression, is crucial for advancing CRC research. This study evaluated the ability of 3D engineered CRC-PDX (3D-eCRC-PDX) tissues to recapitulate the heterogeneity found between patient-derived xenograft (PDX) tumors from three CRC patients (stage II, III-B, and IV).
Read the full summary ↓
To form the 3D-eCRC-PDX tissues, CRC-PDX tumor cells were encapsulated in PEG-fibrinogen hydrogels and maintained for 29 days in vitro. 3D-eCRC-PDX tissues recapitulated key patient-specific tumor characteristics. During long-term culture, 3D-eCRC-PDX tissues mimicked the patient-specific growth rates of the originating CRC-PDX tumors. Importantly, tumor cellular subpopulations, including the ratio of human cancer cells to mouse stromal cells and the ratios of proliferative human cancer cells and CK20+ cells were maintained in 3D-eCRC-PDX tissues, unlike in 2D cell culture. Differences in mechanical stiffness between the originating CRC-PDX tumors were also recapitulated by the 3D-eCRC-PDX tissues. Principle component analysis of transcriptomic data clustered 3D-eCRC-PDX tissues and CRC-PDX tumors together by patient, indicating similar gene expression profiles. These findings highlight the potential of 3D-eCRC-PDX tissues as a promising tool for CRC research, capable of maintaining patient-specific tumor microenvironment heterogeneity.
This dataset
Open GSE311262 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 GSE311262_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 PRJNA1368849 and SRA study SRP648633. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 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 18 sample titles: 18 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 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.