{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T01:40:09Z","timestamp":1789090809524,"version":"build-2803163510"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003500","name":"Universit\u00e0 degli Studi di Padova","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003500","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Bulk RNA sequencing (RNA-seq) has substantially advanced the understanding of pituitary neuroendocrine tumors (PitNETs). However, its limited ability to resolve cellular heterogeneity \u2013 particularly in samples containing residual non-tumor pituitary cells \u2013 remains a significant challenge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We developed and validated a tissue deconvolution framework using a reference dataset derived from single-nucleus RNA sequencing (snRNA-seq) of normal pituitary tissue, aimed at estimating cellular composition in PitNETs from bulk RNA-seq data and characterizing the tumor microenvironment (TME).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Marker-based (CIBERSORT, MuSiC) and single-cell\u2013based (CIBERSORTx, MuSiC) deconvolution approaches were benchmarked across simulated, pseudobulk, and bulk RNA-seq datasets to identify the most reliable tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>CIBERSORTx demonstrated the highest sensitivity (r &amp;gt; 0.85) for detecting pituitary cell types, although accuracy decreased for TME components. Application to ten GH-secreting PitNETs with known histological contamination and to public datasets consistently revealed residual normal tissue across hormone-secreting subtypes, excluding silent tumors. Contaminated samples \u2013 averaging 43% \u00b1 19% with CIBERSORTx and 37% \u00b1 22% with CIBERSORT \u2013 displayed distinct transcriptomic profiles compared to uncontaminated, lineage-matched tumors, based on clustering analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study establishes snRNA-seq\u2013based deconvolution as a robust strategy for reconstructing cellular composition in PitNETs, mitigating the impact of histological contamination and improving the reliability of downstream transcriptomic analyses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1674625","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T05:13:32Z","timestamp":1764220412000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["PitNET tissue deconvolution: 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