ReviewCancers2022
Transcriptomic Profiles of Normal Pituitary Cells and Pituitary Neuroendocrine Tumor Cells.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Unraveling the pathogenic mechanisms of osteoarthritis and obesity: An integration of GWAS, cellular specificity, and spatial transcriptomics.Osteoarthritis and cartilage open · 2026Article
- Comparative analysis of SCGN expression patterns in the anterior pituitary gland of mouse, pig, and human by single-cell transcriptomics.Journal of neuroendocrinology · 2026Article
- Autocrine and paracrine regulation of pituitary endocrine cells.Frontiers in endocrinology · 2026Review
- Cell Lineage-Specific Differences in Clinical Behavior of Non-Functioning Pituitary Adenomas.The Journal of clinical endocrinology and metabolism · 2025Article
- Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.Nature communications · 2025Article
- The evolution and application of multi-omic analysis for pituitary neuroendocrine tumors.Frontiers in medicine · 2025Review
- Single-cell transcriptomics link gene expression signatures to clinicopathological features of gonadotroph and lactotroph PitNET.Journal of translational medicine · 2024Article
- Comprehensive transcriptomic analysis identifies three distinct subtypes of pituitary adenomas: insights into tumor behavior, prognosis, and stem cell characteristics.Journal of translational medicine · 2024Article
- From Genes to Therapy: Pituitary Adenomas in the Era of Precision Medicine.Biomedicines · 2023Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pituitary gland is one of the most cellularly diverse regions of the brain. Recent advancements in transcriptomic biology, such as single-cell RNA sequencing, bring an unprecedented glimpse into the molecular composition of the pituitary, both in its normal physiological state and in disease. Deciphering the normal pituitary transcriptomic signatures provides a better insight into the ontological origin and development of five types of endocrine cells, a process involving complex cascades of transcription factors that are still being established. In parallel with these observations about normal pituitary development, recent transcriptomic findings on pituitary neuroendocrine tumors (PitNETs) demonstrate both preservations and changes in transcription factor expression patterns compared to those seen during gland development. Furthermore, recent studies also identify differentially expressed genes that drive various tumor behaviors, including hormone hypersecretion and tumor aggression. Understanding the comprehensive multiomic profiles of PitNETs is essential in developing molecular profile-based therapies for PitNETs not curable with current treatment modalities and could eventually help align PitNETs with the breakthroughs being made in applying precision medicine to other tumors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.