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Patient-Specific Pharmacogenomics demonstrates xCT as Predictive Therapeutic Target in Colon Cancer with Possible Implications in Tumor Connectivity

GSE305337 Homo sapiens (human) RNA-seq (gene expression) 73 samples Submitted 2025/09/21 Platform GPL18460
At a glance

RNA-seq (gene expression) · human · 73 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
73 samples: 64 distinct titles, 7 of them repeatedGroups 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
Patient-specific pharmacogenomics demonstrates xCT as predictive therapeutic target in colon cancer with possible implications in tumor connectivityMolecular oncology · 2026
Summary (written by the submitters)
Colorectal cancer (CRC) represents the third-leading cause of cancer-related deaths. Knowledge covering diverse cellular and molecular data from individual patients has become valuable for diagnosis, prognosis, and treatment selection. Here, we present an in-depth comparative mRNA-seq analysis of tissue samples from 32 CRC patients, pairing tumors with adjacent healthy tissues.
Read the full summary ↓
The differential expression gene (DEG) analysis revealed apparently dysregulated metabolic programs,in what we focused on the impact of overexpressed SLC7A11 (xCT) and SLC3A2 genes which compose the cystine/glutamate transporter (Xc-) system. To assess the oncogenic potency of the Xc- system in a cellular setting, we applied a knowledge-based approach for analyzing gene perturbations from CRISPR screens across various cell types as well as using a variety of functional assays in five primary patient-derived organoid cell models to functionally verify our hypothesis. We identified a previously undescribed cell surface protein signature predicting chemotherapy resistance and further highlighted the causality and potential of pharmacological blockage of ferroptosis as promising avenue for cancer therapy. Biological processes such as redox homeostasis, ion/amino acid transporters and regulators of neuronal survival and differentiation were associated with these co-dependent genes in patient specimens. This study highlights a number of potential clinical targets for CRC and promotes patient-individual in vitro model systems as functionally validate in silico predictions.
Published in
Patient-specific pharmacogenomics demonstrates xCT as predictive therapeutic target in colon cancer with possible implications in tumor connectivity
Strecker M, Zohar K, Böttcher M et al. · Molecular oncology 2026 · PMID 40988536 · doi:10.1002/1878-0261.70129
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 GSE305337_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 PRJNA1305329 and SRA study SRP608769. Searching any of these in the dataset finder brings you back here.

Study design
64 distinct titles, 7 of them repeated
Tumor colon cells, Patient V sample ×3 Healthy colon cells, Patient V sample ×3 Healthy colon cells, Patient A sample ×2 Tumor colon cells, Patient A sample ×2 Tumor colon cells, Patient U sample ×2 Healthy colon cells, Patient U sample ×2 Healthy colon cells, Patient AA sample ×2

How we read this: Read from 73 sample titles: 64 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

+ 33 more — browse all 73 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.