← BioTransfer GEO Dataset Finder
GEO series

Integrative transcriptomics and network analysis reveals core genes driving meningioma pathogenesis and clinical outcomes

GSE285889 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2025/12/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 40 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
40 samples: 14 conditions, mostly in triplicateGroups 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
Integrative transcriptomics and network analysis reveals core genes driving meningioma pathogenesis and clinical outcomesScientific reports · 2025
Summary (written by the submitters)
Meningiomas are the most common primary CNS tumors, often managed conservatively due to their benign histology. Molecular phenotyping is considered the current frontier in meningioma classification to predict progression and guide clinical management of adjuvant therapies. While several biomarkers have been studied, a definitive molecular prognostic panel has yet to be established—a gap this study seeks to address.
Read the full summary ↓
Using patient-resected meningioma tumor tissues paired with normal meninges, transcriptomic analysis identified a subset of significantly differentially-expressed-genes(DEG) linked to cellular proliferation and immune suppression in the tumor microenvironment. Graph-theory analysis revealed a rich-club organization in the gene networks, with highly connected core nodes. Core gene expression levels significantly correlated with tumor WHO grading, achieving an ROC-AUC of 0.8 for distinguishing low-grade(grade-1) from high-grade(grades-2&3) tumors(P < 0.001). For clinical outcomes, core gene expression achieved an AUC of 0.96 for predicting tumor recurrence(P < 0.001) and 0.74 for patient survival(P = 0.018). This study employs an analytical pipeline integrating transcriptomic analysis of meningiomas with a graph-theory approach to identify core regulators within pathological tumor networks. This refined method facilitated the discovery of phenotype-encoding biomarkers, which accurately predicted histological grade and clinical outcomes. The identified core markers provide insights into meningioma-specific pathophysiological pathways and offer potential targets for therapeutic intervention.
Published in
Integrative transcriptomics and network analysis reveals core genes driving meningioma pathogenesis and clinical outcomes
Zohdy YM, Alawieh AM, Jahangiri A et al. · Scientific reports 2025 · PMID 41272189 · doi:10.1038/s41598-025-29334-2
This dataset
Open GSE285889 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 GSE285889_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 PRJNA1206713 and SRA study SRP555367. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41272189) ↗
Study design
14 conditions, mostly in triplicate
D2 ×3 P2 ×3 P3 ×3 P4 ×3 P5A ×3 P5B ×3 P6 ×3 P7 ×3 +6 more

Supports a between-group comparison across 40 samples.

How we read this: 14 replicated groups read from 40 sample titles; they account for 40 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.