Table_1_The Identification of Differentially Expressed Genes Showing Aberrant Methylation Patterns in Pheochromocytoma by Integrated Bioinformatics Analysis.xlsx

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.

恶性嗜铬细胞瘤（Malignant pheochromocytoma, PHEO）仅当出现转移灶时才能获得完全确诊，但此时患者已难以从传统治疗手段中获益。甲基化在嗜铬细胞瘤的发病机制中发挥重要作用。本研究旨在通过整合生物信息学分析，筛选出嗜铬细胞瘤中存在异常甲基化模式的差异表达基因（differentially expressed genes, DEGs），以进一步阐明该病的分子机制。研究首先借助R软件（版本3.6），将基因表达微阵列数据（GSE19422）与基因甲基化微阵列数据（GSE43293）进行整合，筛选出异常甲基化差异表达基因；随后通过在线生物信息学数据库DAVID筛选出所有存在异常甲基化的重叠差异表达基因，对其进行功能注释，并通过基因本体（gene ontology, GO）分析开展功能富集研究。继而利用在线STRING工具分析上述异常甲基化重叠差异表达基因之间的相互作用，并通过Cytoscape软件（版本3.61）对分析结果进行可视化处理。随后借助Cytoscape中的cytoHubba插件筛选出排名前十的核心基因（hub genes），发现这些基因主要富集于肿瘤相关通路。通过参考癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据集进一步验证了研究结果，并筛选出1个上调低甲基化基因SCN2A与1个下调高甲基化基因KCNQ1。Logistic回归分析与受试者工作特征（receiver operating characteristic, ROC）曲线分析结果显示，KCNQ1与SCN2A可作为区分良恶性嗜铬细胞瘤的潜在诊断生物标志物。最后，临床数据显示，患者术前与术后第1天的血钾、血钠浓度存在显著差异。上述结果表明，分别编码钾离子通道与钠离子通道的KCNQ1与SCN2A可作为嗜铬细胞瘤诊断与预后评估的潜在靶标，有助于推动嗜铬细胞瘤的临床诊疗工作。