Epigenetic Atlas of Bladder Cancer Reveals Master Transcription Factors and Risk-Associated Regulatory Elements in Luminal and Basal-Squamous Molecular Subtypes
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 92 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Homo sapiens (human)
- Topic
- Bladder cancerThis series is part of our curated briefing on it.
- Samples
- 92 samples: 92 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.
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 GSE302930_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1292852 and SRA study SRP601648. 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 92 sample titles: 92 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.
- GSM9113898 Patient BL1007; H3K27ac ChIP
- GSM9113899 Patient BL1009; H3K27ac ChIP
- GSM9113900 Patient BL1010; H3K27ac ChIP
- GSM9113901 Patient BL1020; H3K27ac ChIP
- GSM9113902 Patient BL1034; H3K27ac ChIP
- GSM9113903 Patient BL1037; H3K27ac ChIP
- GSM9113904 Patient BL1038; H3K27ac ChIP
- GSM9113905 Patient BL1039; H3K27ac ChIP
- GSM9113906 Patient BL1040; H3K27ac ChIP
- GSM9113907 Patient BL1041; H3K27ac ChIP
- GSM9113908 Patient BL1042; H3K27ac ChIP
- GSM9113909 Patient BL1043; H3K27ac ChIP
- GSM9113910 Patient BL1044; H3K27ac ChIP
- GSM9113911 Patient BL1045; H3K27ac ChIP
- GSM9113912 Patient BL1046; H3K27ac ChIP
- GSM9113913 Patient BL1047; H3K27ac ChIP
- GSM9113914 Patient BL1048; H3K27ac ChIP
- GSM9113915 Patient BL1049; H3K27ac ChIP
- GSM9113916 Patient BL1050; H3K27ac ChIP
- GSM9113917 Patient BL1052; H3K27ac ChIP
- GSM9113918 Patient BL1053; H3K27ac ChIP
- GSM9113919 Patient BL1054; H3K27ac ChIP
- GSM9113920 Patient BL1055; H3K27ac ChIP
- GSM9113921 Patient BL1056; H3K27ac ChIP
- GSM9113922 Patient BL1057; H3K27ac ChIP
- GSM9113923 Patient BL1058; H3K27ac ChIP
- GSM9113924 Patient BL1063; H3K27ac ChIP
- GSM9113925 Patient BL1068; H3K27ac ChIP
- GSM9113926 Patient BL1007; H3K4me2 ChIP
- GSM9113927 Patient BL1009; H3K4me2 ChIP
- GSM9113928 Patient BL1010; H3K4me2 ChIP
- GSM9113929 Patient BL1020; H3K4me2 ChIP
- GSM9113930 Patient BL1034; H3K4me2 ChIP
- GSM9113931 Patient BL1037; H3K4me2 ChIP
- GSM9113932 Patient BL1038; H3K4me2 ChIP
- GSM9113933 Patient BL1039; H3K4me2 ChIP
- GSM9113934 Patient BL1040; H3K4me2 ChIP
- GSM9113935 Patient BL1041; H3K4me2 ChIP
- GSM9113936 Patient BL1042; H3K4me2 ChIP
- GSM9113937 Patient BL1043; H3K4me2 ChIP
+ 52 more — browse all 92 samples with per-sample file links →
- GSE181389 Transcriptome profiling of HOXA13 and HOXB13 expression knockdown in bladder cancer cell lines. 59 samples same disease briefing · shares bladder
- GSE149652 Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer 28 samples same disease briefing · shares bladder
- GSE171024 Potential Roles of IGF2BP3 in Bladder Cancer 12 samples same disease briefing · shares bladder
- GSE193755 Human bladder cancer cell line high throughput sequencing - genes regulated by hMSH2 12 samples same disease briefing · shares bladder
- GSE214886 Downregulation of CBX7 induced by EZH2 upregulates FGFR3 expression to reduce sensitivity to cisplatin in bladder cancer 12 samples same disease briefing · shares bladder
- GSE211924 UCHL5 promotes Tumorigenesis of Bladder Cancer by Activating AKT/mTOR signaling pathway and targeting c-myc, SLC25A19, and ICAM5. 9 samples same disease briefing · shares bladder
- GSE172359 Combination of urine exosomal mRNAs and lncRNAs as novel diagnostic biomarkers for bladder cancer 6 samples same disease briefing · shares bladder
- GSE182310 Transcriptome sequence of Human bladder cancer cell line 6 samples same disease briefing · shares bladder
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.