Gene expression in prolactinomas: a systematic review

• Published in: Pituitary 2016-02, Vol.19 (1), p.93-104
• Main Authors: Seltzer, Justin, Scotton, Thomas C., Kang, Keiko, Zada, Gabriel, Carmichael, John D.
• Format: Article
• Language: English
• Subjects: Endocrinology; Gene Expression Regulation, Neoplastic; Human Physiology; Humans; Medicine; Medicine & Public Health; Pituitary Neoplasms - genetics; Pituitary Neoplasms - metabolism; Prolactinoma - genetics; Prolactinoma - metabolism
• ISSN: 1386-341X; 1573-7403
• DOI: 10.1007/s11102-015-0674-1

Introduction Prolactinomas are the most common functional pituitary adenomas. Current classification systems rely on phenotypic elements and have few molecular markers for complementary classification. Treatment protocols for prolactinomas are also devoid of molecular targets, leaving those refractory to standard treatments without many options. Methods A systematic literature review was performed utilizing the PRISMA guidelines. We aimed to summarize prior research exploring gene and protein expression in prolactinomas in order to highlight molecular variations associated with tumor development, growth, and prolactin secretion. A PubMed search of select MeSH terms was performed to identify all studies reporting gene and protein expression findings in prolactinomas from 1990 to 2014. Results 1392 abstracts were screened and 51 manuscripts were included in the analysis, yielding 54 upregulated and 95 downregulated genes measured by various direct and indirect analytical methods. Of the many genes identified, three upregulated ( HMGA2 , HST , SNAP25 ), and three downregulated ( UGT2B7 , Let7 , miR - 493 ) genes were selected for further analysis based on our subjective identification of strong potential targets. Conclusions Many significant genes have been identified and validated in prolactinomas and most have not been fully analyzed for therapeutic and diagnostic potential. These genes could become candidate molecular targets for biomarker development and precision drug targeting as well as catalyze deeper research efforts utilizing next generation profiling/sequencing techniques, particularly genome scale expression and epigenomic analyses.