Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [bulkRNA-seq]
RNA-seq (gene expression) · human · 85 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)
- Samples
- 85 samples: 85 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 GSE235092_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 PRJNA984318. 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 85 sample titles: 85 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.
- GSM7494881 MO_MCC_002, bulk RNA
- GSM7494882 MO_MCC_003, bulk RNA
- GSM7494883 MO_MCC_004, bulk RNA
- GSM7494884 MO_MCC_007.1, bulk RNA
- GSM7494885 MO_MCC_007.2, bulk RNA
- GSM7494886 MO_MCC_009, bulk RNA
- GSM7494887 MO_MCC_010, bulk RNA
- GSM7494888 MO_MCC_011, bulk RNA
- GSM7494889 MO_MCC_012, bulk RNA
- GSM7494890 MO_MCC_014, bulk RNA
- GSM7494891 MO_MCC_016, bulk RNA
- GSM7494892 MO_MCC_019, bulk RNA
- GSM7494893 MO_MCC_025, bulk RNA
- GSM7494894 MO_MCC_026.1, bulk RNA
- GSM7494895 MO_MCC_027, bulk RNA
- GSM7494896 MO_MCC_031, bulk RNA
- GSM7494897 MO_MCC_032, bulk RNA
- GSM7494898 MO_MCC_034, bulk RNA
- GSM7494899 MO_MCC_035.1, bulk RNA
- GSM7494900 MO_MCC_035.2, bulk RNA
- GSM7494901 MO_MCC_036, bulk RNA
- GSM7494902 MO_MCC_041.2, bulk RNA
- GSM7494903 MO_MCC_042, bulk RNA
- GSM7494904 MO_MCC_044, bulk RNA
- GSM7494905 MO_MCC_046.1, bulk RNA
- GSM7494906 MO_MCC_046.2, bulk RNA
- GSM7494907 MO_MCC_048, bulk RNA
- GSM7494908 MO_MCC_049, bulk RNA
- GSM7494909 MO_MCC_051, bulk RNA
- GSM7494910 MO_MCC_054.2, bulk RNA
- GSM7494911 MO_MCC_055, bulk RNA
- GSM7494912 MO_MCC_056.1, bulk RNA
- GSM7494913 MO_MCC_058.1, bulk RNA
- GSM7494914 MO_MCC_060, bulk RNA
- GSM7494915 MO_MCC_061, bulk RNA
- GSM7494916 MO_MCC_062, bulk RNA
- GSM7494917 MO_MCC_063, bulk RNA
- GSM7494918 MO_MCC_064, bulk RNA
- GSM7494919 MO_MCC_065, bulk RNA
- GSM7494920 MO_MCC_066, bulk RNA
+ 45 more — browse all 85 samples with per-sample file links →
- GSE235093 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy 333 samples shares cd8, skin-resident, immunotherapy, pre-existing, circuits, efficacy, mediate
- GSE235090 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue] 192 samples shares cd8, skin-resident, immunotherapy, pre-existing, circuits, efficacy, mediate
- GSE308745 The Cd4+ T cell population Partners with Tpex CD8 T cells to mediate Antitumor Immunity in the Tumor Microenvironment.[PB cohort2] 87 samples shares cd8, mediate
- GSE284015 GZMK-expressing tissue-resident memory CD8+ T cells mediate human intestinal acute graft-versus-host disease 26 samples shares cd8, mediate
- GSE295601 The Cd4+ T cell population Partners with Tpex CD8 T cells to mediate Antitumor Immunity in the Tumor Microenvironment. [P6] 18 samples shares cd8, mediate
- GSE300793 Spatial tumour-immune ecosystems shape the efficacy of anti-PD1 immunotherapy in primary cutaneous melanoma [RNAseq] 57 samples shares immunotherapy, efficacy
- GSE289185 Safety and efficacy of neoadjuvant radiotherapy and immunotherapy in the treatment of esophageal squamous cell carcinoma 29 samples shares immunotherapy, efficacy
- GSE309309 Intermediate cryopreservation and transcriptomic profiling as strategy to identify optimal donors for functional NK cell expansion for AML immunotherapy 16 samples shares immunotherapy, mediate
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.