bioRxivMolecular BiologyPreprint
Transcriptomic perspective on acromegaly-related PIT-1/SF-1 positive pituitary tumors
Abstract
Neuroendocrine pituitary tumors (PitNETs) are classified based on clinical manifestation and expression of pituitary cell lineage-specific transcription factors (TFs) and hormones. A subtype of acromegaly-related tumors was found to express PIT-1 and SF-1 TFs, two markers of distinct pituitary cells. They were considered as multilineage or "somatogonadotoph" tumors. The aim of our study was to determine their identity and cell type origin by extensive transcriptomic analysis. For this purpose, we analyzed the RNA sequencing (RNAseq) data from 546 PitNETs (including 193 acromegaly-related tumors) and single cell RNAseq data from somatotroph and gonadotroph tumors and normal pituitary. Somatrotroph PitNETs co-expressing PIT-1 and SF-1 TFs were found in each included RNAseq dataset. Their transcriptomic profile and activity of pituitary TFs closely resembles other somatotroph tumors, but they substantially differ from gonadotroph PitNETs yet retain activity of NR5A1 (SF-1) and expression of some of SF-1-regulated genes (LHB and GNHRH). SF-1 apparently regulates slightly different set of genes in double positive somatotroph PitNETs and gonadotroph tumors. Based on scRNAseq data a subcluster of normal gonadotroph cells with POU1F1 (PIT-1) expression was identified, but tumor cells of PIT-1/SF-1 PitNETs were not similar to this subtype of normal gonadotrophs. A difference in genes expression profiles between 3 subtypes of somatotroph tumors was determined by analysis of both bulk- and scRNAseq data. From transcriptomic perspective of genes-coregulation and the activity of pituitary transcription factors, acromegaly-related PitNETs co-expressing PIT-1 and SF-1 are subtype of PIT-1 lineage tumors and molecular data do not support considering them as multilineage.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Transcriptomic classification of pituitary neuroendocrine tumors causing acromegaly
- Developmental Plasticity and Stromal Co-option Shape a Pituitary Neuroendocrine Tumor Transcriptional Continuum
Robert C. Osorio, Jun Yeop Oh, … Manish Kumar Aghi·bioRxiv·Jun 18, 2025
- Single-Cell Atlas of Non-Functioning Gonadotroph Tumors and Anterior Pituitary Reveals Lineage Infidelity and Clinically Associated Transcriptional Programs
Evan Dennis, Gyeong Dae Kim, … Allegra A. Petti·bioRxiv·Oct 27, 2026
- Transcriptomic heterogeneity of Sox2-expressing pituitary cells
Patrick A. Fletcher, Kosara Smiljanić, … Stanko S. Stojilković·GliaPeer-reviewed·Sep 12, 2022
- Single nucleus pituitary transcriptomic and epigenetic landscape reveals human stem cell heterogeneity with diverse regulatory mechanisms
Z. Zhang, M. Zamojski, … F. M. Ruf-Zamojski·bioRxiv·Jun 18, 2021
- Distinct malignant cell states and myeloid glutamate signaling associated with aggressive pancreatic neuroendocrine tumors
Jeanna M. Qiu, Jae Won Cho, … William L. Hwang·Clinical Cancer ResearchPeer-reviewed·Mar 18, 2026