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Multidimensional analysis of patient-matched primary and recurrent glioblastoma identifies microglial FCGR1A (CD64) and other FCGRs as contributors of tumor recurrence [seq]

GSE254145 Homo sapiens (human) RNA-seq (gene expression) 79 samples Submitted 2024/11/18 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 79 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
79 samples: 58 distinct titles, 17 of them repeatedGroups 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
Multidimensional analysis of matched primary and recurrent glioblastoma identifies contributors to tumor recurrence influencing time to relapseJournal of neuropathology and experimental neurology · 2025
Summary (written by the submitters)
Introduction: Glioblastoma (GBM) is a lethal brain tumor without effective treatment options. Here, we sought to characterize longitudinal tumor immune microenvironment (iTME) changes in order to find potential actionable targets to prevent GBM-induced immune evasion mechanisms. This study included 15 patient-matched treatment-naïve (primary, pGBM, WHO grade 4) and recurrent (rGBM) IDH1 wild type tumors.
Read the full summary ↓
Methods:Extracted RNA and proteins from fresh frozen tumor samples from matched pGBM and rGBM were profiled via transcriptomics and proteomics, respectively. Paired formalin-fixed paraffin-embedded tumor samples were processed for spatial transcriptomics analysis.Results: Differentially expressed genes and proteins between pGBM and rGBM were involved in pathways responsible for synapse development and myelination which have been shown to play a role in GBM recurrence. By categorizing patients into short and long time-to-relapse (STTR vs LTTR), and by correlating TTR with gene expression, we detected genes positively or negatively associated with tumor relapse. Expression of FCG receptor and complement system genes such as FCGR1A (CD64), FCGR3A (CD16a) and the complement C3 were negatively correlated with STTR, whereas high expression of DNMT1/3A, and SMARCA4, involved in DNA methylation, were positively correlated with LTTR. Spatial transcriptomic analysis of the tumor cells compartment defined by GFAP expression showed enrichment in the proportion of oligodendrocytes in recurrent tumors in rGBM, whereas CD64-positive myeloid cell compartment showed a switch of quiescent to activated microglia, appearance of B cells and a reduction in macrophage proportion in recurrent tumors with STTR. Conclusion: Our results uncover a role of CD64-expressing myeloid cells, mostly activated microglia, in GBM recurrence and suggest that interfering with these cells may represent a new therapeutic option for preventing GBM relapse.
Published in
Multidimensional analysis of matched primary and recurrent glioblastoma identifies contributors to tumor recurrence influencing time to relapse
Shekarian T, Ritz MF, Hogan S et al. · Journal of neuropathology and experimental neurology 2025 · PMID 39423857 · doi:10.1093/jnen/nlae108
This dataset
Open GSE254145 in finder → Find more human RNA-seq datasets →
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.

Series matrix ↓
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 GSE254145_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1068630 and SRA study SRP488905. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39423857) ↗
Study design
58 distinct titles, 17 of them repeated
195_primary_GFAP_long_TalaSlide1Again ×3 417_recurrent_CD64_short_TalaSlide1Again ×3 38_primary_GFAP_long_TalaSlide1Again ×3 599_recurrent_GFAP_short_TalaSlide1Again ×3 185_recurrent_GFAP_short_Tala ×2 417_primary_CD64_short_Tala ×2 356_primary_CD64_short_TalaSlide1Again ×2 356_primary_GFAP_short_TalaSlide1Again ×2 +9 more

How we read this: Read from 79 sample titles: 58 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.

Samples in this study

+ 39 more — browse all 79 samples with per-sample file links →

Datasets on a similar topic

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