Objective: To identify the genes involved in prolactinoma by bioinformatics methods and provide new potential biomarkers for prolactinoma.
Materials And Methods: The gene-expression profile data, GSE36314, including 4 prolactinoma samples and 3 controls, was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package in R and were then classified into different functional groups by COG (Clusters of Orthologous Groups) annotation based on BLASTX (Basic Local Alignment Search Tool). Transcriptional factors (TFs) were screened out by employing the Transcription Factor (TRANSFAC) database. An interaction network among DEGs and TFs was constructed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis were then performed for the genes in this network.
Results: A total of 52 genes were identified as being significantly different between prolactinomas and normal samples which were classified into 29 COG functional categories. Three TFs, ZIC3 (Zic family member 3), NGFIC (nerve growth factor-induced protein C) and SP1 (Specificity Protein 1) were screened out, which can regulate part of DEGs. Two down-regulated genes, FSHB (follicle stimulating hormone β subunit) and LHB (luteinizing hormone β subunit) were involved in GnRH (gonadotropin-releasing hormone) signaling pathway.
Conclusion: Several DEGs between prolactinoma and normal samples were identified in our study and candidate agents such as LHB and FSHB may provide the groundwork for a targeted therapy approach for prolactinomas.
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