← BioTransfer GEO Dataset Finder
GEO series

Deciphering CRC-Hepatocyte Interactions: A Multiomic Platform for Interrogation of Metabolic Crosstalk in the Liver-Tumor Microenvironment

GSE282081 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/01/31 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 15 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)
Samples
15 samples: 5 conditions, mostly in triplicateGroups 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
Deciphering Colorectal Cancer-Hepatocyte Interactions: A Multiomic Platform for Interrogation of Metabolic Crosstalk in the Liver-Tumor MicroenvironmentbioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of liver metastasis, these interactions play a crucial role in promoting tumor survival and progression.
Read the full summary ↓
This study leverages multiomics coverage of the microenvironment via liquid chromatography and high-resolution, high-mass accuracy mass spectrometry-based untargeted metabolomics, stable isotope tracing, and RNA sequencing to uncover the metabolic impact of co-localized primary hepatocytes and a colon adenocarcinoma cell line, SW480, using a 2D co-culture model. Metabolic profiling revealed disrupted Warburg metabolism with an 80% decrease in glucose consumption and 94% decrease in lactate production by hepatocyte-SW480 co-cultures relative to SW480 control cultures. Decreased glucose consumption was coupled with alterations in glutamine and ketone body metabolism, suggesting a possible fuel switch upon co-culturing. Further, integrated multiomic analysis indicates disruptions in metabolic pathways, including nucleoside biosynthesis, amino acids, and TCA cycle, correlate with altered SW480 transcriptional profiles and highlight the importance of oxygen homeostasis in tumor adaptation. Finally, these findings were replicated in 3-dimensional microtissue organoids supporting a bioinformatic approach for studying metabolic crosstalk and discovery of potential therapeutic targets in preclinical models of the tumor microenvironment.
Published in
Deciphering Colorectal Cancer-Hepatocyte Interactions: A Multiomic Platform for Interrogation of Metabolic Crosstalk in the Liver-Tumor Microenvironment
Nelson AB, Reese LE, Rono E et al. · bioRxiv : the preprint server for biology 2024 · PMID 39713297 · doi:10.1101/2024.12.06.627264
This dataset
Open GSE282081 in finder → Find more human RNA-seq datasets →
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 GSE282081_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 PRJNA1186971 and SRA study SRP545616. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39713297) ↗
Study design
5 conditions, mostly in triplicate
3D organoid, SW480 cells, ×3 3D organoid, SW480 cells cultured with J2 cel… ×3 2D culture, SW480 cells, ×3 2D culture, SW480 cells cultured with J2 cell… ×3 2D culture, SW480 cells cultured with J2 cell… ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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
Datasets on a similar topic

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.