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Differential Gene Expression and Pathway Analysis in Growth Hormone-Secreting Pituitary Tumors According to Granulation Pattern

GSE264621 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/04/28 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 11 × Densely granulated somatotroph adenoma vs 7 × Sparsely granulated somatotroph adenoma vs 6 × Normal pituitaryGroups 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)
This study investigated differential gene expression between granulation patterns in growth hormone (GH)-secreting pituitary tumors, aiming to elucidate novel transcriptomes that explain clinical variances in patients with acromegaly. Transcriptome analysis was conducted on 6 normal pituitary tissues and 15 GH-secreting pituitary tumors, including 9 densely granulated somatotroph tumors (DGSTs) and 6 sparsely granulated somatotroph tumors (SGSTs).
Read the full summary ↓
We identified 3111 differentially expressed genes (DEGs) in tumors compared to normal pituitaries, with 1117 DEGs unique to a specific granulation within tumors. SGST showed enrichment of neuronal development and acute inflammatory response pathways, along with a significant enhancement of JAK–STAT, phosphatidylinositol 3-kinase, and MAPK signaling. The results suggest that granulation-specific gene expression may underpin diverse clinical presentations in acromegaly, highlighting the potential for further investigation into these transcriptomic variations and their roles in disease pathology, particularly the involvement of genes linked to neuronal development, inflammatory response, and JAK–STAT signaling in SGST.
This dataset
Open GSE264621 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 GSE264621_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 PRJNA1103668 and SRA study SRP503523. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
11 × Densely granulated somatotroph adenoma vs 7 × Sparsely granulated somatotroph adenoma vs 6 × Normal pituitary

Supports a between-group comparison across 24 samples.

How we read this: 3 replicated groups read from 24 sample titles; they account for 24 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

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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.