Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy
RNA-seq (gene expression) · human · 333 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
- Merkel cell carcinomaThis series is part of our curated briefing on it.
- Samples
- 333 samples: 333 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
- Preexisting Skin-Resident CD8 and γδ T-cell Circuits Mediate Immune Response in Merkel Cell Carcinoma and Predict Immunotherapy EfficacyCancer discovery · 2024
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE235093_RAW.tar (it can run to tens of gigabytes).
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 PRJNA984312. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 333 sample titles: 333 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.
- GSM7494647 MO_MCC_011, skin - RNA
- GSM7494648 MO_MCC_011, skin - ab VDJ
- GSM7494649 MO_MCC_057, lymph node - RNA
- GSM7494650 MO_MCC_057, lymph node - ADT
- GSM7494651 MO_MCC_057, lymph node - ab VDJ
- GSM7494652 MO_MCC_057, lymph node - gd VDJ
- GSM7494653 MO_MCC_059, lymph node - RNA
- GSM7494654 MO_MCC_059, lymph node - ADT
- GSM7494655 MO_MCC_059, lymph node - ab VDJ
- GSM7494656 MO_MCC_059, lymph node - gd VDJ
- GSM7494657 MO_MCC_061, skin - RNA
- GSM7494658 MO_MCC_061, skin - ab VDJ
- GSM7494659 MO_MCC_066, skin - RNA
- GSM7494660 MO_MCC_066, skin - ab VDJ
- GSM7494661 MMO_MCC_068skin_RNA,skin
- GSM7494662 MMO_MCC_068skin_ADT,skin
- GSM7494663 MMO_MCC_068skin_abVDJ,skin
- GSM7494664 MO_MCC_068skin_gdVDJ,skin
- GSM7494665 MO_MCC_068LN_RNA,skin
- GSM7494666 MO_MCC_068LN_ADT,skin
- GSM7494667 MO_MCC_068LN_abVDJ,skin
- GSM7494668 MO_MCC_068LN_gdVDJ,skin
- GSM7494669 MO_MCC_069, skin - RNA
- GSM7494670 MO_MCC_069, skin - ADT
- GSM7494671 MO_MCC_069, skin - ab VDJ
- GSM7494672 MO_MCC_069, skin - gd VDJ
- GSM7494673 MO_MCC_071, skin - RNA
- GSM7494674 MO_MCC_071, skin - ADT
- GSM7494675 MO_MCC_071, skin - ab VDJ
- GSM7494676 MO_MCC_071, skin - gd VDJ
- GSM7494677 MO_MCC_072, skin - RNA
- GSM7494678 MO_MCC_072, skin - ADT
- GSM7494679 MO_MCC_072, skin - ab VDJ
- GSM7494680 MO_MCC_072, skin - gd VDJ
- GSM7494681 MO_MCC_073, skin - RNA
- GSM7494682 MO_MCC_073, skin - ADT
- GSM7494683 MO_MCC_073, skin - ab VDJ
- GSM7494684 MO_MCC_073, skin - gd VDJ
- GSM7494685 MO_MCC_074, skin - RNA
- GSM7494686 MO_MCC_074, skin - ADT
+ 293 more — browse all 333 samples with per-sample file links →
- GSE281260 Spatially organized inflammatory myeloid-CD8+ T cell aggregates linked to Merkel-cell Polyomavirus driven Reorganization of the Tumor Microenvironment 146 samples same disease briefing · shares cd8
- GSE154938 Glucose-6-Phosphate Dehydrogenase as a Prognostic Predictor Correlates with the Tumor Immune Activity Including Programmed Death Ligand-1 Expression in Merkel Cell Carcinoma 41 samples same disease briefing · shares predict
- GSE227708 Cancer-specific CD8 T cell frequency at baseline in blood correlates with response to PD-1 blockade in Merkel cell carcinoma. 16 samples same disease briefing · shares cd8
- GSE290609 PRMT5-mediated SRSF1 methylation promotes virus-positive Merkel cell carcinoma tumorigenesis by increasing Tip60 exon inclusion 16 samples same disease briefing · shares mediate
- GSE290569 PRMT5-mediated SRSF1 methylation promotes virus-positive Merkel cell carcinoma tumorigenesis by increasing Tip60 exon inclusion [PacBio Sequel IIe] 8 samples same disease briefing · shares mediate
- GSE261681 Interlocking core regulatory circuits enable viral hijacking in Merkel cell carcinoma 43 samples same disease briefing · shares circuits
- GSE292992 MDM2 degradation overcomes feedback regulation of p53 signaling in Merkel cell carcinoma 66 samples same disease briefing
- GSE317774 ATR inhibition induces NF-κB-dependent immunogenicity in Merkel cell carcinoma independent of cGAS-STING 48 samples same disease briefing
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.