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Proteogenomic analysis reveals a compendium of therapeutic targets for treatment-resistant subtypes of locally advanced cervical cancers

GSE279998 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/03/19 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
Cervical cancerThis series is part of our curated briefing on it.
Samples
16 samples: 12 × SCC_patient vs 3 × ADC_patientGroups 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
Proteogenomic characterization of molecular and cellular targets for treatment-resistant subtypes in locally advanced cervical cancersMolecular cancer · 2025
Summary (written by the submitters)
We report proteogenomic analysis of locally advanced cervical cancer (LACC). Exome-seq data revealed predominant alterations of keratinization-TP53 regulation and O-glycosylation-TP53 regulation axes in squamous and adeno-LACC, respectively, compared to in early-stage cervical cancer. Integrated clustering of mRNA, protein, and phosphorylation data identified six subtypes (Sub1-6) of LACC among which Sub3, 5, and 6 showed the treatment-resistant nature with poor local recurrence-free survival.
Read the full summary ↓
Elevated immune and extracellular matrix (ECM) activation mediated by activated stroma (PDGFD and CXCL1high fibroblasts) characterized the immune-hot Sub3 enriched with MUC5AChigh epithelial cells (ECs). Increased epithelial-mesenchymal-transition (EMT) and ECM remodeling characterized the immune-cold squamous Sub5 enriched with PGK1 and CXCL10high ECs. We further demonstrated that CIC mutations could trigger EMT activation by upregulating ETV4, and the elevation of the immune checkpoint PVR and neutrophil-like myeloid-derived suppressive cells (FCN1 and FCGR3Bhigh macrophages) could cause suppression of T-cell activation in Sub5. Increased O-linked glycosylation of mucin characterized adeno-LACC Sub6 enriched with MUC5AChigh ECs. These results provide a battery of somatic mutations, cellular pathways, and cellular players that can be used to predict treatment-resistant LACC subtypes and can serve as potential therapeutic targets for these LACC subtypes.
Published in
Proteogenomic characterization of molecular and cellular targets for treatment-resistant subtypes in locally advanced cervical cancers
Hyeon DY, Nam D, Shin HJ et al. · Molecular cancer 2025 · PMID 40087745 · doi:10.1186/s12943-025-02256-3
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 GSE279998_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 PRJNA1175711 and SRA study SRP539998. Searching any of these in the dataset finder brings you back here.

Study design
12 × SCC_patient vs 3 × ADC_patient

Supports a between-group comparison across 15 samples.

How we read this: 2 replicated groups read from 16 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
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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.