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PitNET tissue deconvolution: tracing normal tissue residues and immune dynamics.

Mattia Dalle Nogare1, Serena Avallone1,2, Luna Picello1

  • 1Department of Biology, University of Padova, Padova, Italy.

Frontiers in Endocrinology
|December 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to analyze pituitary tumor cell composition using RNA sequencing data. It accurately identifies residual normal pituitary cells in tumor samples, improving understanding of pituitary neuroendocrine tumors.

Keywords:
bulk RNA sequencing (RNA-seq)cellular heterogeneitydeconvolution methodspituitary neuroendocrine tumors (PitNETs)single-nucleus RNA sequencing (snRNA-seq)

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Area of Science:

  • Endocrinology
  • Genomics
  • Computational Biology

Background:

  • Bulk RNA sequencing (RNA-seq) advances pituitary neuroendocrine tumor (PitNET) research.
  • Resolving cellular heterogeneity in PitNETs with residual normal cells is challenging.

Purpose of the Study:

  • Develop and validate a tissue deconvolution framework for PitNETs.
  • Estimate cellular composition and characterize the tumor microenvironment (TME) using bulk RNA-seq data.
  • Utilize single-nucleus RNA sequencing (snRNA-seq) reference data.

Main Methods:

  • Benchmarked marker-based (CIBERSORT, MuSiC) and single-cell-based (CIBERSORTx, MuSiC) deconvolution approaches.
  • Used simulated, pseudobulk, and bulk RNA-seq datasets for validation.
  • Applied the framework to GH-secreting PitNETs and public datasets.

Main Results:

  • CIBERSORTx showed high sensitivity (r > 0.85) for pituitary cell type detection.
  • Residual normal tissue was consistently detected in hormone-secreting PitNETs.
  • Contaminated samples exhibited distinct transcriptomic profiles compared to uncontaminated tumors.

Conclusions:

  • snRNA-seq-based deconvolution is a robust strategy for PitNET cellular composition analysis.
  • This approach mitigates histological contamination.
  • Improves the reliability of downstream transcriptomic analyses in PitNETs.