Molecular differences between tumor endothelial cells from low- and high-grade gliomas
RNA-seq (gene expression) · human · 34 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
- 34 samples: 34 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.
- Paper
- Low- and High-Grade Glioma-Associated Vascular Cells Differentially Regulate Tumor GrowthMolecular cancer research : MCR · 2024
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 GSE236571_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 PRJNA991575 and SRA study SRP447443. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 3 samples read as cases, 6 as controls.
How we read this: Read from 34 sample titles: 34 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.
- GSM7554418 REC_GVC3 [3778TEC]
- GSM7554420 REC_GVC2 [3768TEC]
- GSM7554422 REC_GVC4 [3811TEC]
- GSM7554423 REC_GVC5 [3812TEC]
- GSM7554424 REC_GVC1 [3499TEC]
- GSM7554425 PRI_GVC4 [3815TEC]
- GSM7554426 PRI_GVC3 [3810TEC]
- GSM7554427 PRI_GVC2 [3383TEC]
- GSM7554428 PRI_GVC1 [3706TEC]
- GSM7554429 LGG_GVC1 [3707TEC]
- GSM7554431 LGG_GVC2 [3744TEC]
- GSM7554433 LGG_GVC4 [3761TEC]
- GSM7554434 LGG_GVC5 [3788TEC]
- GSM7554435 LGG_GVC3 [3746TEC]
- GSM7554436 HBEC [HBEC_S45]
- GSM7554447 GBM [GBM_3465]
- GSM7554448 TEC [TEC_3465]
- GSM7554449 PRI_GVC1 (EP) [EP_3367]
- GSM7554450 PRI_GVC1 (LP) [LP_3367]
- GSM7554451 REC_GVC1(EP) [EP_3499]
- GSM7554453 REC_GVC1(LP) [LP_3499]
- GSM7554454 HBEC [HBEC_S1]
- GSM7554466 IDH-wildtype Control replicate 1 [408_CTL1]
- GSM7554467 IDH-wildtype Control replicate 2 [408_CTL2]
- GSM7554469 IDH-wildtype Control replicate 3 [408_CTL3]
- GSM7554471 IDH-wildtype Recombinant ASPN replicate 1 [408_ASPN1]
- GSM7554473 IDH-wildtype Recombinant ASPN replicate 2 [408_ASPN2]
- GSM7554474 IDH-wildtype Recombinant ASPN replicate 3 [408_ASPN3]
- GSM7554476 IDH-mutant Control replicate 1 [252_CTL1]
- GSM7554477 IDH-mutant Control replicate 2 [252_CTL2]
- GSM7554478 IDH-mutant Control replicate 3 [252_CTL3]
- GSM7554480 IDH-mutant Recombinant ASPN replicate 1 [252_ASPN1]
- GSM7554481 IDH-mutant Recombinant ASPN replicate 2 [252_ASPN2]
- GSM7554483 IDH-mutant Recombinant ASPN replicate 3 [252_ASPN3]
- GSE287078 Time- and Dose-Dependent Effects of Irradiation on Endothelial and Tumor Endothelial Cells: Transcriptional, Molecular, and Functional Changes Driving Activation In Vitro and In Vivo 30 samples shares endothelial
- GSE242299 Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype 31 samples shares endothelial
- GSE244101 Single-cell transcriptomics of non-functioning and functioning PitNET subtypes reveals differences in the tumor microenvironment and tumor-specific gene expression signatures that correlate with aggressive tumor growth 10 samples shares differences
- GSE275647 Role of Cancer-Associated Fibroblasts in Prognostic Stratification of Advanced-Stage Gastric Cancer through Interacting with Endothelial and Malignant Epithelial Cells [AGC RNA-seq] 216 samples shares endothelial
- GSE237429 Single-cell transcriptomics reveals distinct molecular alterations of human renal cell carcinoma-derived endothelial cells that are stable in ex vivo primary culture 19 samples shares endothelial
- GSE254249 Remodeling of T and endothelial cells during total neoadjuvant therapy in rectal cancer 92 samples shares endothelial
- GSE294621 Vascular-type heterogeneity is associated with differential gene expression profiles of endothelial cells under shear stress 74 samples shares endothelial
- GSE286888 Comparative gene expression profiling of high-grade serous ovarian cancer cells exposed to cisplatin or CX-5461 60 samples shares high-grade
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.