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Multiomic profiling of medulloblastoma reveals subtype-specific targetable alterations at the proteome and N-glycan level [RNA-Seq]

GSE243795 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2024/04/23 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 21 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)
Topic
MedulloblastomaThis series is part of our curated briefing on it.
Samples
21 samples: 7 × SHH MB vs 6 × WNT MB vs 4 × GR4 MB vs 3 × GR3 MBGroups 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.
Summary (written by the submitters)
Medulloblastomas (MBs) are malignant pediatric brain tumors that are molecularly and clinically very heterogenous. To unravel phenotypically relevant MB subtypes, we compiled a harmonized proteome dataset of 167 MBs and integrated findings with DNA methylation and N-glycome data. Six proteome MB subtypes emerged, that could be assigned to two main molecular programs: transcription/translation (pSHHt, pWNT and pGroup3-Myc), and synapses/immunological processes (pSHHs, pGroup3 and pGroup4).
Read the full summary ↓
Multiomic analysis revealed different conservation levels of proteome features across MB subtypes at the DNA-methylation level. Aggressive pGroup3-Myc MBs and favorable pWNT MBs were most similar in cluster hierarchies concerning overall proteome patterns but showed different protein abundances of the vincristine resistance associated multiprotein complex TriC/CCT and of N-glycan turnover associated factors. The N-glycome reflected proteome subtypes and complex-bisecting N-glycans characterized pGroup3-Myc tumors. Our results shed light on new targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures
This dataset
Download · which file do I need?

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.

1 · Just have a look

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.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE243795_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

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 PRJNA1020067 and SRA study SRP462679. Searching any of these in the dataset finder brings you back here.

Study design
7 × SHH MB vs 6 × WNT MB vs 4 × GR4 MB vs 3 × GR3 MB

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 21 sample titles; they account for 20 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.

Samples in this study
More Medulloblastoma datasets

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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.