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Chemoradiation Reprograms Tumor Cells and Immune Microenvironment In Cervical Cancer [bulk RNA-seq]

GSE297038 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2025/11/10 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 34 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Cervical cancerThis series is part of our curated briefing on it.
Samples
34 samples: 34 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.
Paper
Chemoradiation Reprograms Tumor Cells and the Immune Microenvironment in Cervical CancerCancer research · 2026
Summary (written by the submitters)
Despite advances in screening and prevention, cervical cancer remains a leading cause of cancer-related deaths worldwide, underscoring the need for better treatments. Here, we present a multi-cohort longitudinal study of human cervical tumors and the tumor microenvironment during chemoradiation therapy (CRT). Integrating RNA sequencing, single-cell transcriptomics, and in vivo validation, we define the cellular and molecular programs shaping cell interactions and document how CRT alters them.
Read the full summary ↓
We identify multiple therapeutic targets in CRT-resistant tumors, notably MDM2, a key mediator of radiation responses in tumor and immune cells. MDM2 inhibition enhanced radiotherapy effects in HPV-positive, TP53 wild-type cervical cancer cells, improved radiation response and reshaped the immune landscape in preclinical models. These findings highlight the potential of combining MDM2 inhibition with CRT to overcome resistance and improve patient outcomes. Our data provides novel insights into therapy-induced changes in tumor and immune compartments, guiding new strategies against treatment-resistant HPV-positive cancers.
Published in
Chemoradiation Reprograms Tumor Cells and the Immune Microenvironment in Cervical Cancer
Sandoval TA, Li Y, Muhammad N et al. · Cancer research 2026 · PMID 41860764 · doi:10.1158/0008-5472.CAN-25-3776
This dataset
Open GSE297038 in finder → Find more human RNA-seq datasets → Cervical 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 GSE297038_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1262424 and SRA study SRP585065. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41860764) ↗
Study design
34 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 34 sample titles: 34 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 Cervical cancer datasets

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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.