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Peer-reviewedCC BY 4.0Frontiers in Endocrinology

From histopathology to molecular taxonomy: predicting pituitary neuroendocrine tumors invasiveness

  • Boyuan Zhang1
  • Yuqi Sun2
  • Hongyu Guo1
  • XiLe Pei2
  • Chunfa Wu2
  • Yuanda Yang2
  • Yuzhen Yang2
  • Dahai Wan2
  1. 1The First Clinical Medical College of Shanxi Medical University
  2. 2Department of Neurosurgery, First Hospital of Shanxi Medical University
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Abstract

Pituitary neuroendocrine tumors (PitNETs) are among the most prevalent intracranial tumors in adults. Although most follow an indolent clinical course, a substantial subset exhibits local invasion, which can limit gross total resection and increase the risk of residual disease and recurrence. Importantly, anatomical invasiveness should not be equated with clinical aggressiveness. Aggressive PitNETs may also exhibit a longitudinal pattern of repeated or multiple local recurrences and frequently require multiple therapeutic interventions. The 2022 Fifth Edition of the WHO Classification formally renamed pituitary adenomas to PitNETs and established a lineage classification framework centered on core transcription factors, including PIT1, TPIT, and SF1, thereby driving the transition from traditional pathological diagnosis to molecular subtyping. Driven by recent advances in high-throughput sequencing, liquid biopsy, and single-cell omics, numerous novel molecular biomarkers have emerged, enabling the early pre- and postoperative identification of invasive subpopulations. This review systematically outlines recent progress in multidimensional biomarkers—including DNA methylation, protein markers, non-coding RNAs, liquid biopsy indicators, and tumor microenvironmental features—to provide an updated synthesis of candidate biomarkers that may support future risk stratification and individualized management of invasive PitNETs.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026from,
  title = {{From histopathology to molecular taxonomy: predicting pituitary neuroendocrine tumors invasiveness}},
  author = {Boyuan Zhang and Yuqi Sun and Hongyu Guo and XiLe Pei and Chunfa Wu and Yuanda Yang and Yuzhen Yang and Dahai Wan},
  journal = {Frontiers in Endocrinology},
  year = {2026},
  month = oct,
  doi = {10.3389/fendo.2026.1907825},
  url = {https://www.frontiersin.org/articles/10.3389/fendo.2026.1907825/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - From histopathology to molecular taxonomy: predicting pituitary neuroendocrine tumors invasiveness
AU  - Boyuan Zhang
AU  - Yuqi Sun
AU  - Hongyu Guo
AU  - XiLe Pei
AU  - Chunfa Wu
AU  - Yuanda Yang
AU  - Yuzhen Yang
AU  - Dahai Wan
PY  - 2026
DA  - 2026/10/27
JO  - Frontiers in Endocrinology
DO  - 10.3389/fendo.2026.1907825
UR  - https://www.frontiersin.org/articles/10.3389/fendo.2026.1907825/full
AB  - Pituitary neuroendocrine tumors (PitNETs) are among the most prevalent intracranial tumors in adults. Although most follow an indolent clinical course, a substantial subset exhibits local invasion, which can limit gross total resection and increase the risk of residual disease and recurrence. Importantly, anatomical invasiveness should not be equated with clinical aggressiveness. Aggressive PitNETs may also exhibit a longitudinal pattern of repeated or multiple local recurrences and frequently require multiple therapeutic interventions. The 2022 Fifth Edition of the WHO Classification formally renamed pituitary adenomas to PitNETs and established a lineage classification framework centered on core transcription factors, including PIT1, TPIT, and SF1, thereby driving the transition from traditional pathological diagnosis to molecular subtyping. Driven by recent advances in high-throughput sequencing, liquid biopsy, and single-cell omics, numerous novel molecular biomarkers have emerged, enabling the early pre- and postoperative identification of invasive subpopulations. This review systematically outlines recent progress in multidimensional biomarkers—including DNA methylation, protein markers, non-coding RNAs, liquid biopsy indicators, and tumor microenvironmental features—to provide an updated synthesis of candidate biomarkers that may support future risk stratification and individualized management of invasive PitNETs.
SN  - 1664-2392
ER  -