GEO series

The differential gene screening for invasive pituitary adenoma and non-invasive pituitary adenoma

GSE260487 Homo sapiens (human) RNA-seq (gene expression) 32 samples Submitted 2024/03/04 Platform GPL28038

At a glance

RNA-seq (gene expression) · human · 32 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: 32 samples: 32 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: Identification of HSPD1 as a novel invasive biomarker associated with mitophagy in pituitary adenomasTranslational oncology · 2024

Summary (written by the submitters)

Pituitary adenomas are benign tumors originating from the endocrine cells of the pituitary gland, but some pathological subtypes are highly invasive, known as invasive pituitary adenomas. Invasive pituitary adenomas are relatively rare, progress rapidly, easily invade surrounding tissues, have a high risk of recurrence, and have poor response to standard treatments.

Read the full summary ↓

This study collected tumor specimens from 17 patients with non-invasive pituitary adenomas (FSH type) and 15 patients with invasive pituitary adenomas (ACTH-silent type), and performed transcriptome sequencing, aiming to explore the genetic differences between invasive and non-invasive pituitary adenomas.

Published in

Identification of HSPD1 as a novel invasive biomarker associated with mitophagy in pituitary adenomas

Zhang Y, Ma X, Liu C et al. · Translational oncology 2024 · PMID 38290248 · doi:10.1016/j.tranon.2024.101886

This dataset

Open GSE260487 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 GSE260487_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 PRJNA1081873 and SRA study SRP493285. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38290248) ↗

Study design

32 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 32 sample titles: 32 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

GSM8118243 A11A, non-invasive
GSM8118244 A12A, non-invasive
GSM8118245 A13A, non-invasive
GSM8118246 A15A, non-invasive
GSM8118247 A16A, non-invasive
GSM8118248 A17A, non-invasive
GSM8118249 A18A, non-invasive
GSM8118250 A19A, non-invasive
GSM8118251 A1A, non-invasive
GSM8118252 A20A, non-invasive
GSM8118253 A2A, non-invasive
GSM8118254 A3A, non-invasive
GSM8118255 A4A, non-invasive
GSM8118256 A5A, non-invasive
GSM8118257 A6A, non-invasive
GSM8118258 A7A, non-invasive
GSM8118259 A9A, non-invasive
GSM8118260 B10A, invasive
GSM8118261 B12A, invasive
GSM8118262 B13A, invasive
GSM8118263 B14A, invasive
GSM8118264 B15A, invasive
GSM8118265 B16A, invasive
GSM8118266 B18A, invasive
GSM8118267 B19A, invasive
GSM8118268 B1A, invasive
GSM8118269 B20A, invasive
GSM8118270 B3A, invasive
GSM8118271 B4A, invasive
GSM8118272 B5A, invasive
GSM8118273 B6A, invasive
GSM8118274 B7A, invasive

Datasets on a similar topic

GSE264621 Differential Gene Expression and Pathway Analysis in Growth Hormone-Secreting Pituitary Tumors According to Granulation Pattern 24 samples shares differential, pituitary
GSE295449 Single-cell epigenetic landscape, microenvironment interactions, and gene regulatory modules of non-functioning pituitary adenomas (non-diseased controls, bulkRNAseq) 22 samples shares pituitary, adenoma
GSE240729 Development of a novel non-invasive biomarker panel for hepatic fibrosis in individuals with MASLD 67 samples shares non-invasive, invasive
GSE289037 Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancer 17 samples shares non-invasive, invasive
GSE263206 Trancriptome analysis of tumor tissue to search for the associations between USP8 mutation status, molecular markers of pituitary adenomas, and clinical, and diagnostic parametres of Cushing disease 16 samples shares pituitary, adenoma
GSE308705 Non-Invasive Detection of Bone Marrow Fibrosis in Myeloproliferative Neoplasms Using Cell-Free RNA 16 samples shares non-invasive, invasive
GSE272200 Differential gene expression in thalamic postmortem tissue of subjects with obsessive compulsive disorder 85 samples shares differential
GSE294621 Vascular-type heterogeneity is associated with differential gene expression profiles of endothelial cells under shear stress 74 samples shares differential

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.