Pituitary adenomas are the most common intrasellar tumors. Patients should be identified at an early stage so that effective treatment can be implemented. The study aims at detecting the potential biomarkers with diagnostic value of pituitary adenomas. Using a total of seven gene expression profiles (GEPs) of the datasets from the Gene Expression Omnibus (GEO) database, we first screened 1980 significant differentially expressed genes (DEGs). Then, we employed the prediction analysis for microarray (PAM) algorithm to identify 340 significant DEGs able to differ pituitary tumor from normal samples, which include 208 upregulated DEGs and 132 downregulated DEGs. DAVID database was used to carry out the enrichment analysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathways. We found that upregulated candidates were enriched in protein folding and metabolic pathways. Downregulated DEGs saw a significant enrichment in insulin receptor signaling pathway and hedgehog signaling pathway. Based on the protein-protein interaction (PPI) network as well as module analysis, we determined ten hub genes including PHLPP, ENO2, ACTR1A, EHHADH, EHMT2, FOXO1, DLD, CCT2, CSNK1D, and CETN2 that could be potential biomarkers with diagnostic value in pituitary adenomas. In conclusion, the study contributes to reliable and potential molecular biomarkers with diagnostic value. Moreover, these potential biomarkers may be used for prognosis and new therapeutic targets for the pituitary adenomas.
Keywords: Bioinformatics; Diagnosis; GEO; Microarray; Pituitary adenomas; Prediction analysis.