GEO series
Combined DNA and RNA profiling refines prognostic stratification independent of therapeutic actionability in cholangiocarcinoma
GSE317889
Homo sapiens (human)
Single-nucleus RNA-seq
62 samples
Submitted 2026/05/01
Platform GPL18573Platform GPL15520
At a glance
Single-nucleus RNA-seq · human · 62 samples
- What it is
- Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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
- CholangiocarcinomaThis series is part of our curated briefing on it.
- Samples
- 62 samples: 62 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
- Integrated DNA and RNA profiling refines prognostic stratification independent of therapeutic actionability in cholangiocarcinomaBMC cancer · 2026
Summary (written by the submitters)
Background Cholangiocarcinoma (CCA) exhibits marked biological heterogeneity. While genomic profiling identifies targetable alterations such as FGFR2 fusions and IDH1 mutations, transcriptomic analyses reveal distinct immune, proliferative and mesenchymal profiles. How these genomic and transcriptomic features jointly influence clinical outcomes remains unclear.
Read the full summary ↓
Methods We performed integrated DNA and RNA profiling in 62 patients with intrahepatic and extrahepatic CCA treated at a single tertiary center. Targeted DNA sequencing assessed single-nucleotide variants, copy-number alterations, and microsatellite instability. Extended RNA sequencing evaluated gene fusions and pathway-level transcriptomic subtypes. Associations with progression-free survival (PFS) and overall survival (OS) were examined. Results Among 58 DNA-profiled tumors, the most frequent alterations were TP53 (43.1%) and KRAS (29.3%), followed by IDH1 (10.3%, restricted to intrahepatic CCA). Actionable alterations (IDH1, FGFR2 fusions, ERBB2 amplification, microsatellite instability-high) were detected in 25% of cases and associated with longer overall survival (67.5 vs 20.8 months; p=0.0004), consistent with benefit from matched therapies rather than intrinsic tumor biology. RNA profiling identified five transcriptomic subtypes: Immune (21%), Proliferative (18%), Mesenchymal (42%), Immune-Proliferative (8%), and Unclassified (11%). KRAS-TP53 co-mutation was the strongest adverse prognostic factor (38.1 vs 13.2 months; p=0.0004). The Mesenchymal subtype was associated with shorter PFS (5.9 vs 17.9 months; p=0.045) in patients treated with chemotherapy ± immunotherapy but did not significantly affect OS. Conclusions Integrated genomic and transcriptomic profiling refines prognostic stratification in cholangiocarcinoma independent of therapeutic actionability. KRAS-TP53 co-mutation and the Mesenchymal transcriptomic subtype represent independent high-risk markers detectable on routine FFPE tissue. These features complement actionable alterations and may inform patient selection and clinical trial design.
Published in
Vendrell JA, Dalmon I, Cabello-Aguilar S et al.
· BMC cancer 2026
· PMID 42087104
· doi:10.1186/s12885-026-16064-5
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 GSE317889_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: 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 PRJNA1415763 and SRA study SRP668993. Searching any of these in the dataset finder brings you back here.
Study design
62 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 62 sample titles: 62 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
- GSM9480748 Patient 01 intrahepathic cholangiocarcinoma
- GSM9480749 Patient 02 intrahepathic cholangiocarcinoma
- GSM9480750 Patient 03 intrahepathic cholangiocarcinoma
- GSM9480751 Patient 04 intrahepathic cholangiocarcinoma
- GSM9480752 Patient 05 intrahepathic cholangiocarcinoma
- GSM9480753 Patient 75 intrahepathic cholangiocarcinoma
- GSM9480754 Patient 77 extrahepathic cholangiocarcinoma
- GSM9480755 Patient 79 intrahepathic cholangiocarcinoma
- GSM9480756 Patient 08 extrahepathic cholangiocarcinoma
- GSM9480757 Patient 80 intrahepathic cholangiocarcinoma
- GSM9480758 Patient 86 intrahepathic cholangiocarcinoma
- GSM9480759 Patient 87 intrahepathic cholangiocarcinoma
- GSM9480760 Patient 89 intrahepathic cholangiocarcinoma
- GSM9480761 Patient 09 extrahepathic cholangiocarcinoma
- GSM9480762 Patient 90 intrahepathic cholangiocarcinoma
- GSM9480763 Patient 92 extrahepathic cholangiocarcinoma
- GSM9480764 Patient 94 intrahepathic cholangiocarcinoma
- GSM9480765 Patient 10 intra hepathic cholangiocarcinoma
- GSM9480766 Patient 11 intrahepathic cholangiocarcinoma
- GSM9480767 Patient 12 intrahepathic cholangiocarcinoma
- GSM9480768 Patient 13 extrahepathic cholangiocarcinoma
- GSM9480769 Patient 15 intrahepathic cholangiocarcinoma
- GSM9480770 Patient 16 intrahepathic cholangiocarcinoma
- GSM9480771 Patient 17 intrahepathic cholangiocarcinoma
- GSM9480772 Patient 20 extrahepathic cholangiocarcinoma
- GSM9480773 Patient 21 intrahepathic cholangiocarcinoma
- GSM9480774 Patient 22 intrahepathic cholangiocarcinoma
- GSM9480775 Patient 26 intrahepathic cholangiocarcinoma
- GSM9480776 Patient 27 extrahepathic cholangiocarcinoma
- GSM9480777 Patient 28 intrahepathic cholangiocarcinoma
- GSM9480778 Patient 29 intrahepathic cholangiocarcinoma
- GSM9480779 Patient 30 intrahepathic cholangiocarcinoma
- GSM9480780 Patient 31 intrahepathic cholangiocarcinoma
- GSM9480781 Patient 32 intrahepathic cholangiocarcinoma
- GSM9480782 Patient 35 intrahepathic cholangiocarcinoma
- GSM9480783 Patient 36 extrahepathic cholangiocarcinoma
- GSM9480784 Patient 37 extrahepathic cholangiocarcinoma
- GSM9480785 Patient 40 intrahepathic cholangiocarcinoma
- GSM9480786 Patient 42 intrahepathic cholangiocarcinoma
- GSM9480787 Patient 43 intrahepathic cholangiocarcinoma
+ 22 more — browse all 62 samples with per-sample file links →
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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.