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Malignant pheochromocytoma (PHEO) can only be fully diagnosed when metastatic foci develop. However, at this point in time, patients gain little benefit from traditional therapeutic methods. Methylation plays an important role in the pathogenesis of PHEO. The aim of this research was to use integrated bioinformatics analysis to identify differentially expressed genes (DEGs) showing aberrant methylation patterns in PHEO and therefore provide further understanding of the molecular mechanisms underlying this condition. Aberrantly methylated DEGs were first identified by using R software (version 3.6) to combine gene expression microarray data (GSE19422) with gene methylation microarray data (GSE43293). An online bioinformatics database (DAVID) was then used to identify all overlapping DEGs showing aberrant methylation; these were annotated and then functional enrichment was ascertained by gene ontology (GO) analysis. The online STRING tool was then used to analyze interactions between all overlapping DEGs showing aberrant methylation; these results were then visualized by Cytoscape (version 3.61). Next, using the cytoHubba plugin within Cytoscape, we identified the top 10 hub genes and found that these were predominantly enriched in pathways related to cancer. Reference to The Cancer Genome Atlas (TCGA) further confirmed our results and further identified an upregulated hypomethylated gene ( SCN2A ) and a downregulated hypermethylated gene ( KCNQ1 ). Logistic regression analysis and receiver operating characteristic (ROC) curve analysis indicated that  KCNQ1  and  SCN2A  represent promising differential diagnostic biomarkers between benign and malignant PHEO. Finally, clinical data showed that there were significant differences in the concentrations of potassium and sodium when compared between pre-surgery and post-surgery day 1. These suggest that  KCNQ1  and  SCN2A , genes that encode potassium and sodium channels, respectively, may serve as putative diagnostic targets for the diagnosis and prognosis of PHEO and therefore facilitate the clinical management of PHEO.

The Identification of Differentially Expressed Genes Showing Aberrant Methylation Patterns in Pheochromocytoma by Integrated Bioinformatics Analysis