Targeting UGCG sensitizes AML cells to Venetoclax through RAB32-mediated endoplasmic reticulum-mitochondria communication
RNA-seq (gene expression) · human · 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
- Homo sapiens (human)
- Samples
- 12 samples: 3 × ABT vs 3 × Comb vs 3 × DMSO vs 3 × ELIGroups 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 GSE315549_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 PRJNA1398244 and SRA study SRP659712. 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.
- GSM9431004 ABT_1 ABT
- GSM9431005 ABT_2 ABT
- GSM9431006 ABT_3 ABT
- GSM9431007 Comb_1 Comb
- GSM9431008 Comb_2 Comb
- GSM9431009 Comb_3 Comb
- GSM9431010 DMSO_1 DMSO
- GSM9431011 DMSO_2 DMSO
- GSM9431012 DMSO_3 DMSO
- GSM9431013 ELI_1 ELI
- GSM9431014 ELI_2 ELI
- GSM9431015 ELI_3 ELI
- GSE289786 Improved Prediction of Venetoclax and Azacitidine Efficacy in Acute Myeloid Leukemia through Machine Learning [AML patients] 142 samples shares aml, venetoclax
- GSE338135 NRAS knockdown reverses monocytic cell state and resensitizes resistant FLT3-AML cells to gilteritinib 75 samples shares aml, sensitizes
- GSE270621 Gene epression analyses on pre- and post-treatment samples obtained from AML patients who respond or failed to respond to Venetoclax/Decitabine treatment 26 samples shares aml, venetoclax
- GSE327563 Mediator kinase inhibition exposes a transcriptional vulnerability and primes AML for venetoclax sensitivity 18 samples shares aml, venetoclax
- GSE280391 Targeting CERT sensitizes AML Cells to FLT3 Inhibitors through activating GRP78-ATF6-CHOP Axis 12 samples shares aml, sensitizes
- GSE232130 Leucegene: AML sequencing (GRCh38 reference) 691 samples shares aml
- GSE232129 Leucegene: AML sequencing (part 1-6, GRCh38 reference) 452 samples shares aml
- GSE297413 Genomic and transcriptomic determinants of clinical outcomes in patients with AML and DNMT3A mutations 264 samples shares aml
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.