Transcriptomic Profiling of Lactotroph Pituitary Neuroendocrine Tumors via RNA Sequencing and Ingenuity Pathway Analysis
Background: Lactotroph pituitary neuroendocrine tumors (PitNETs) are common pituitary tumors, but their underlying molecular mechanisms remain unclear. This study aimed to investigate the transcriptomic landscape of lactotroph PitNETs and identify potential molecular mechanisms and therapeutic targets through RNA sequencing and ingenuity pathway analysis (IPA). Methods: Lactotroph PitNET tissues from five surgical cases without dopamine agonist treatment underwent RNA sequencing. Normal pituitary tissues from three patients served as controls. Differentially expressed genes (DEGs) were identified, and the functional pathways and gene networks were explored by IPA. Results: Transcriptome analysis revealed that lactotroph PitNETs had gene expression patterns that were distinct from normal pituitary tissues. We identified 1,172 upregulated DEGs, including nine long intergenic noncoding RNAs (lincRNAs) belonging to the top 30 DEGs. IPA of the upregulated DEGs showed that the estrogen receptor signaling, oxidative phosphorylation signaling, and EIF signaling were activated. In gene network analysis, key upstream regulators, such as EGR1, PRKACA, PITX2, CREB1, and JUND, may play critical roles in lactotroph PitNETs. Overall design: We studied five patients with lactotroph PitNETs who had markedly elevated prolactin levels and subsequently underwent endoscopic transsphenoidal surgery without preoperative medications such as cabergoline and other dopamine agonists, with rapid postoperative normalization of prolactin levels. As for the control, three normal pituitary gland tissues were obtained from other patients undergoing endoscopic transsphenoidal surgery through legitimate reasons (e.g., ensuring intraoperative visual field for complete removal of suprasellar tumor) and having normal pituitary hormone sampling values, including hormone stimulation test, preoperatively. After extracting RNA from the tissue, we constructed the transcriptome landscapes of these PitNETs using RNA-seq data and compared them to normal pituitary tissues to analyze functional pathways and gene networks.