Targeting the oncohistone-ASCL1 axis disrupts plasticity and suppresses pediatric gliomas [RNA-seq]
RNA-seq (gene expression) · human · 44 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
- 44 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.
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 GSE269557_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 PRJNA1122517 and SRA study SRP513143. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 44 samples.
How we read this: 18 replicated groups read from 44 sample titles; they account for 44 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.
- GSM8322080 ASCL1KO_DM_Day3_rep1 ASCL1KO_DM_Day3
- GSM8322081 ASCL1KO_DM_Day3_rep2 ASCL1KO_DM_Day3
- GSM8322082 ASCL1KO_DM_Week10_rep1 ASCL1KO_DM_Week10
- GSM8322083 ASCL1KO_DM_Week10_rep2 ASCL1KO_DM_Week10
- GSM8322084 ASCL1KO_TSM_rep1 ASCL1KO_TSM
- GSM8322085 ASCL1KO_TSM_rep2 ASCL1KO_TSM
- GSM8322086 ASCL1KO_TSM_rep3 ASCL1KO_TSM
- GSM8322087 Control_Adh_DM_Week10_rep1 Control_Adh_DM_Week10
- GSM8322088 Control_Adh_DM_Week10_rep2 Control_Adh_DM_Week10
- GSM8322089 Control_DM_Day3_rep1 Control_DM_Day3
- GSM8322090 Control_DM_Day3_rep2 Control_DM_Day3
- GSM8322091 Control_Sph_DM_BackInTSM_rep1 Control_Sph_DM_BackInTSM
- GSM8322092 Control_Sph_DM_BackInTSM_rep2 Control_Sph_DM_BackInTSM
- GSM8322093 Control_Sph_DM_Week10_rep1 Control_Sph_DM_Week10
- GSM8322094 Control_Sph_DM_Week10_rep2 Control_Sph_DM_Week10
- GSM8322095 Control_TSM_rep1 Control_TSM
- GSM8322096 Control_TSM_rep2 Control_TSM
- GSM8322097 Control_TSM_rep3 Control_TSM
- GSM8322098 DKO_DM_Day3_rep1 DKO_DM_Day3
- GSM8322099 DKO_DM_Day3_rep2 DKO_DM_Day3
- GSM8322100 DKO_DM_Week10_rep1 DKO_DM_Week10
- GSM8322101 DKO_DM_Week10_rep2 DKO_DM_Week10
- GSM8322102 DKO_TSM_rep1 DKO_TSM
- GSM8322103 DKO_TSM_rep2 DKO_TSM
- GSM8322104 DKO_TSM_rep3 DKO_TSM
- GSM8322105 K27MKO_DM_Day3_rep1 K27MKO_DM_Day3
- GSM8322106 K27MKO_DM_Day3_rep2 K27MKO_DM_Day3
- GSM8322107 K27MKO_DM_Week10_rep1 K27MKO_DM_Week10
- GSM8322108 K27MKO_DM_Week10_rep2 K27MKO_DM_Week10
- GSM8322109 K27MKO_TSM_rep1 K27MKO_TSM
- GSM8322110 K27MKO_TSM_rep2 K27MKO_TSM
- GSM8322111 K27MKO_TSM_rep3 K27MKO_TSM
- GSM8322112 BT869_ASCL1KO_Day0_Rep1 BT869_ASCL1KO_Day0
- GSM8322113 BT869_ASCL1KO_Day0_Rep2 BT869_ASCL1KO_Day0
- GSM8322114 BT869_ASCL1KO_Day0_Rep3 BT869_ASCL1KO_Day0
- GSM8322115 BT869_ASCL1KO_Day3_Rep1 BT869_ASCL1KO_Day3
- GSM8322116 BT869_ASCL1KO_Day3_Rep2 BT869_ASCL1KO_Day3
- GSM8322117 BT869_ASCL1KO_Day3_Rep3 BT869_ASCL1KO_Day3
- GSM8322118 BT869_Control_Day0_Rep1 BT869_Control_Day0
- GSM8322119 BT869_Control_Day0_Rep2 BT869_Control_Day0
+ 4 more — browse all 44 samples with per-sample file links →
- GSE269555 Targeting the oncohistone-ASCL1 axis disrupts plasticity and suppresses pediatric gliomas [CUT&RUN] 39 samples shares oncohistone-ascl1, plasticity, suppresses, targeting, pediatric, disrupts, gliomas
- GSE281471 Targeting CD206+ macrophages disrupts the establishment of a key anti-tumor immune axis [Bulk RNA-seq] 243 samples shares targeting, disrupts, rna-seq
- GSE222556 Neoadjuvant PARPi or Chemotherapy in Ovarian Cancer Informs Targeting Effector Treg Cells for Homologous-Recombination-Deficient Tumors [10x scRNA-seq] 67 samples shares targeting, rna-seq
- GSE316970 CYD-4-61 Suppresses Gastric Tumor Growth by Targeting SOX9–CDK4–Driven Oncogenic Programs and Enhances Anti-PD-1 Therapy 18 samples shares suppresses, targeting
- GSE253929 ROR2 regulates cellular plasticity in pancreatic neoplasia and adenocarcinoma [RNA-seq siROR2 ] 12 samples shares plasticity, rna-seq
- GSE230601 ZNF397 Deficiency Triggers TET2-driven Lineage Plasticity and AR-Targeted Therapy Resistance in Prostate Cancer [RNA-seq] 12 samples shares plasticity, rna-seq
- GSE215024 Targeting DCAF5 suppresses SMARCB1-mutant cancer via stabilizing SWI/SNF 12 samples shares suppresses, targeting
- GSE293843 Targeting the ferritinophagy-lysosome axis as a therapeutic vulnerability in gastroenteropancreatic neuroendocrine tumors [RNA-Seq] 10 samples shares targeting, rna-seq
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.