Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis

Pituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was acquired from Gene Expression Omnibus datasets, which included 5 pituitary prolactinoma samples and 4 normal pituitaries samples. We screened differentially expressed genes (DEGs) with limma and investigated their biological function by pathway and Gene Ontology (GO) enrichment analysis. A protein-protein interaction (PPI) network of the up and down DEGs were constructed and analyzed by HIPPIE and Cytoscape software. Module analyses were performed. In addition, a target gene - miRNA network and target gene - TF network of the up and down DEGs were constructed by NetworkAnalyst and Cytoscape software. The set of DEGs exhibited an intersection consisting of 989 genes (461 up-regulated and 528 down-regulated), which may be associated with pituitary prolactinoma. Pathway enrichment analysis showed that the 989 DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis also showed that sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene miRNA network and target gene - TF network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which may improve our understanding of the mechanisms of the pathogenesis and integration; genes may be therapeutic targets and prognostic markers for pituitary prolactinoma.