Splicing diversity enhances the molecular classification of pituitary neuroendocrine tumors

Yue Huang, Jing Guo, Xueshuai Han, Yang Zhao, Xuejing Li, Peiqi Xing, Yulou Liu, Yingxuan Sun, Song Wu, Xuan Lv, Lei Zhou, Yazhuo Zhang, Chuzhong Li (), Weiyan Xie () and Zhaoqi Liu ()
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Yue Huang: China National Center for Bioinformation
Jing Guo: Capital Medical University
Xueshuai Han: China National Center for Bioinformation
Yang Zhao: China National Center for Bioinformation
Xuejing Li: Capital Medical University
Peiqi Xing: China National Center for Bioinformation
Yulou Liu: Capital Medical University
Yingxuan Sun: Capital Medical University
Song Wu: University of Chinese Academy of Sciences
Xuan Lv: China National Center for Bioinformation
Lei Zhou: China National Center for Bioinformation
Yazhuo Zhang: Capital Medical University
Chuzhong Li: Capital Medical University
Weiyan Xie: Capital Medical University
Zhaoqi Liu: China National Center for Bioinformation

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Pituitary neuroendocrine tumors (PitNETs) are one of the most common intracranial tumors with diverse clinical manifestations. Current pathological classification systems rely primarily on histological hormone staining and transcription factors (TFs) expression. While effective in identifying three major lineages, molecular characteristics based on hormones and TFs lack sufficient resolution to fully capture the complex tumor heterogeneity. Transcriptional diversity by alternative splicing (AS) offered additional insight to address this challenge. Here, we perform bulk and full-length single-cell RNA sequencing to comprehensively investigate AS dysregulation across all PitNET lineages. We reveal pervasive splicing dysregulations that better depict tumor heterogeneity. Additionally, we delineate fundamental splicing heterogeneity at single-cell resolution, confirming bulk findings and refining splicing dysregulation varying among tumor cell types. Notably, we effectively distinguish the silent corticotroph subtype and define a distinct TPIT lineage subtype, which is associated with worse clinical outcomes and increased splicing abnormalities driven by altered ESRP1 expression. In conclusion, our results characterize the subtype specific AS landscape in PitNETs, enhancing the understanding of the PitNETs subtyping.

Date: 2025
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DOI: 10.1038/s41467-025-56821-x

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