S-nitrosylation of IRF7 induced by NOS1 expression in Melanoma suppresses anti-tumor immunity
RNA-seq (gene expression) · mouse · 12 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
- Mus musculus (mouse)
- Samples
- 12 samples: 3 × WTCTRL vs 3 × MUTCTRL vs 3 × WTIFN vs 3 × MUTIFNGroups 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.
Read the full summary ↓
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 GSE279140_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 PRJNA1170966 and SRA study SRP537541. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.
- GSM8562369 WTCTRL1 WTCTRL
- GSM8562370 WTCTRL2 WTCTRL
- GSM8562371 WTCTRL3 WTCTRL
- GSM8562372 MUTCTRL1 MUTCTRL
- GSM8562373 MUTCTRL2 MUTCTRL
- GSM8562374 MUTCTRL3 MUTCTRL
- GSM8562375 WTIFN1 WTIFN
- GSM8562376 WTIFN2 WTIFN
- GSM8562377 WTIFN3 WTIFN
- GSM8562378 MUTIFN1 MUTIFN
- GSM8562379 MUTIFN2 MUTIFN
- GSM8562380 MUTIFN3 MUTIFN
- GSE217803 ARID1A suppresses R-loop mediated STING-Type I Interferon pathway activation of anti-tumor immunity (RNA-Seq) 46 samples shares suppresses, anti-tumor, immunity
- GSE197791 Transdermal nanolipoplex simultaneously inhibits subcutaneous melanoma growth and suppresses systemically metastatic melanoma by activating host immunity 15 samples shares suppresses, melanoma, immunity
- GSE298822 G9a Epigenetically Suppresses CXCL10 Expression and Inhibits Anti-tumor Immunity in Hepatocellular Carcinoma 8 samples shares suppresses, anti-tumor, immunity
- GSE217807 ARID1A suppresses R-loop mediated STING-Type I Interferon pathway activation of anti-tumor immunity 62 samples shares suppresses, anti-tumor, immunity
- GSE286963 C/EBPβ suppresses keratinocyte autonomous type 1 IFN response and p53 to increase cell survival and susceptibility to UVB-induced skin cancer 16 samples shares suppresses, induced
- GSE298345 Gpc3 Selectively Suppresses Subcutaneous Adipogenesis in Diet-Induced Obesity 12 samples shares suppresses, induced
- GSE293222 High-Fat Diet-Induced Liver-Gut Crosstalk Suppresses Autophagy and Exacerbates Colitis [colon] 12 samples shares suppresses, induced
- GSE293183 High-Fat Diet-Induced Liver-Gut Crosstalk Suppresses Autophagy and Exacerbates Colitis [liver] 12 samples shares suppresses, induced
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.