Oncogenic and tumor-suppressive forces converge on a progenitor-orchestrated niche to shape early tumorigenesis
RNA-seq (gene expression) · mouse · 69 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
- 69 samples: 18 conditions, mostly in duplicateGroups 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.
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 GSE315243_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 PRJNA1395391 and SRA study SRP654144. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 17 samples read as cases, 6 as controls.
How we read this: 18 replicated groups read from 69 sample titles; they account for 55 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.
- GSM9365527 Pre-tumor p53 proficient 0 Pre-tumor p53 proficient
- GSM9365528 Pre-tumor p53 proficient 1 Pre-tumor p53 proficient
- GSM9365529 Pre-tumor p53 proficient 2 Pre-tumor p53 proficient
- GSM9365530 Pre-tumor p53 proficient 3 Pre-tumor p53 proficient
- GSM9365531 Pre-tumor p53 proficient 4 Pre-tumor p53 proficient
- GSM9365532 Pre-tumor p53 proficient 5 Pre-tumor p53 proficient
- GSM9365533 Pre-tumor p53 deficient 1 Pre-tumor p53 deficient
- GSM9365534 Pre-tumor p53 deficient 2 Pre-tumor p53 deficient
- GSM9365535 Pre-tumor p53 deficient 4 Pre-tumor p53 deficient
- GSM9365536 Pre-tumor p53 deficient 5 Pre-tumor p53 deficient
- GSM9365537 Tumor p53 proficient 6 Tumor p53 proficient
- GSM9365538 Tumor p53 proficient 7 Tumor p53 proficient
- GSM9365539 Tumor p53 proficient 8 Tumor p53 proficient
- GSM9365540 Tumor p53 proficient 12 Tumor p53 proficient
- GSM9365541 Tumor p53 deficient 6 Tumor p53 deficient
- GSM9365542 Tumor p53 deficient 7 Tumor p53 deficient
- GSM9365543 Tumor p53 deficient 8 Tumor p53 deficient
- GSM9423536 K3_FLEX
- GSM9423537 K3_shp53_batch3 K3_shp53
- GSM9423538 K3_shCtrl_batch3 K3_shCtrl
- GSM9423539 K3_shp53_batch5 K3_shp53
- GSM9423540 K3_shCtrl_batch5 K3_shCtrl
- GSM9423541 K3_shp53_batch3_cellHash
- GSM9423542 K3_shCtrl_batch3_cellHash
- GSM9423543 K3_shp53_batch5_cellHash
- GSM9423544 K3_shCtrl_batch5_cellHash
- GSM9433805 0004279_Region_1 0004279_Region
- GSM9433806 0004279_Region_2 0004279_Region
- GSM9433807 0004279_Region_3 0004279_Region
- GSM9433808 0004329_Region_1 0004329_Region
- GSM9433809 0004329_Region_2 0004329_Region
- GSM9433810 0004329_Region_3 0004329_Region
- GSM9433811 0015409_Region_1 0015409_Region
- GSM9433812 0015409_Region_2 0015409_Region
- GSM9433813 0015409_Region_3 0015409_Region
- GSM9433814 0015409_Region_4 0015409_Region
- GSM9433815 0015409_Region_5 0015409_Region
- GSM9433816 0015410_Region_1 0015410_Region
- GSM9433817 0015410_Region_2 0015410_Region
- GSM9433818 0015410_Region_3 0015410_Region
+ 29 more — browse all 69 samples with per-sample file links →
- GSE313305 Oncogenic and tumor-suppressive forces converge on a progenitor-orchestrated niche to shape early tumorigenesis [KPLOH scRNA-seq] 17 samples shares progenitor-orchestrated, tumor-suppressive, tumorigenesis, oncogenic, converge, forces, niche, shape
- GSE216741 Oncogenic fatty acid oxidation senses sleep deficiency-induced circadian disruption to promote tumorigenesis 48 samples shares tumorigenesis, oncogenic
- GSE275908 CXCL12-CXCR4 signaling mediates the formation of a macrophageal niche for mammary gland development and tumorigenesis 30 samples shares tumorigenesis, niche
- GSE301214 EML4-ALK variant-specific genetic interactions shape lung tumorigenesis 24 samples shares tumorigenesis, shape
- GSE240359 Dynamic Chromatin Alteration Induces Oncogenic Hijacking by Essential Transcriptional Factors during Medulloblastoma Tumorigenesis [CUT&Tag] 18 samples shares tumorigenesis, oncogenic
- GSE297023 Aging represses oncogenic KRAS-driven lung tumorigenesis and alters tumor suppression 16 samples shares tumorigenesis, oncogenic
- GSE275906 CXCL12-CXCR4 signaling mediates the formation of a macrophageal niche for mammary tumorigenesis [tumor] 10 samples shares tumorigenesis, niche
- GSE243608 Dynamic Chromatin Alteration Induces Oncogenic Hijacking by Essential Transcriptional Factors during Medulloblastoma Tumorigenesis [[CUT&Tag 2] 9 samples shares tumorigenesis, oncogenic
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.