Abstract
Simple SummaryPituitary neuroendocrine tumors are non-cancerous tumors of the pituitary gland, that may overproduce hormones leading to serious health conditions or due to tumor size cause chronic headache, vertigo or visual impairment. In recent years pituitary neuroendocrine tumors are studied with the latest molecular biology methods that simultaneously investigate a large number of factors to understand the mechanisms of how these tumors develop and how they could be diagnosed or treated. In this review article, we have studied literature reports, compiled information and described molecular factors that could affect the development and clinical characteristics of pituitary neuroendocrine tumors, discovered factors that overlap between several studies using large scale molecular analysis and interpreted the potential involvement of these factors in pituitary tumor development. Overall, this study provides a valuable resource for understanding the biology of pituitary neuroendocrine tumors.Pituitary neuroendocrine tumors (PitNETs) are non-metastatic neoplasms of the pituitary, which overproduce hormones leading to systemic disorders, or tumor mass effects causing headaches, vertigo or visual impairment. Recently, PitNETs have been investigated in large scale (exome and genome) molecular analyses (transcriptome microarrays and sequencing), to uncover novel markers. We performed a literature analysis on these studies to summarize the research data and extrapolate overlapping gene candidates, biomarkers, and molecular mechanisms. We observed a tendency in samples with driver mutations (GNAS, USP8) to have a smaller overall mutational rate, suggesting driver-promoted tumorigenesis, potentially changing transcriptome profiles in tumors. However, direct links from drivers to signaling pathways altered in PitNETs (Notch, Wnt, TGF-β, and cell cycle regulators) require further investigation. Modern technologies have also identified circulating nucleic acids, and pinpointed these as novel PitNET markers, i.e., miR-143-3p, miR-16-5p, miR-145-5p, and let-7g-5p, therefore these molecules must be investigated in the future translational studies. Overall, large-scale molecular studies have provided key insight into the molecular mechanisms behind PitNET pathogenesis, highlighting previously reported molecular markers, bringing new candidates into the research field, and reapplying traditional perspectives to newly discovered molecular mechanisms.
Highlights
Pituitary neuroendocrine tumors (PitNETs) are glandular neoplasms of the anterior pituitary
The approach grouped PitNET tumors according to transcription factors specifying the cell lineage of endocrine cell origin. This classification is widely accepted in Cancers 2021, 13, 1395 the literature, and is more predictive in terms of tumor prognosis [31,32,33]. We used this classification system where appropriate, as many review studies were published before 2017, and used a classification system based on clinical characteristics, e.g., PitNETs not secreting hormones were grouped as non-functional pituitary adenomas (NFPA)
We observed overlapping factors, such as DLK1 related to Notch signaling, NNAT involved in pituitary development [92,93,95], GAL related to growth hormone release [87,88,92,95], NPTX2 involved in cell plasticity [92,93,95], and
Summary
Pituitary neuroendocrine tumors (PitNETs) are glandular neoplasms of the anterior pituitary. This classification is widely accepted in Cancers 2021, 13, 1395 the literature, and is more predictive in terms of tumor prognosis [31,32,33] We used this classification system where appropriate, as many review studies were published before 2017, and used a classification system based on clinical characteristics, e.g., PitNETs not secreting hormones were grouped as non-functional pituitary adenomas (NFPA). According to objectives, publications reported results using different filtering stages, ranging from almost unfiltered lists of genetic variants to a single or few molecular markers reinforced by experimental data For the former, we performed additional filtering to exclude common population variants (variant has a dbSNP identification code and population frequency above 0.5%) or expression changes under a certain threshold (
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