Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China.
School of Business, Hanyang University, Seoul 15588, Republic of Korea.
Comput Math Methods Med. 2022 Aug 9;2022:2465598. doi: 10.1155/2022/2465598. eCollection 2022.
Adrenal cortical carcinoma (ACC) is a severe malignant tumor with low early diagnosis rates and high mortality. In this study, we used a variety of bioinformatic analyses to find potential prognostic markers and therapeutic targets for ACC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets were used to perform differential expressed analysis. WebGestalt was used to perform enrichment analysis, while String was used for protein-protein analysis. Our study first detected 28 up-regulation and 462 down-regulation differential expressed genes through the GEO and TCGA databases. Then, GO functional analysis, four pathway analyses (KEGG, REACTOME, PANTHER, and BIOCYC), and protein-protein interaction network were performed to identify these genes by WebGestalt tool and KOBAS website, as well as String database, respectively, and finalize 17 hub genes. After a series of analyses from GEPIA, including gene mutations, differential expression, and prognosis, we excluded one candidate unrelated to the prognosis of ACC and put the remaining genes into pathway analysis again. We screened out CCNB1 and NDC80 genes by three algorithms of Degree, MCC, and MNC. We subsequently performed genomic analysis using the TCGA and cBioPortal databases to better understand these two hub genes. Our data also showed that the CCNB1 and NDC80 genes might become ACC biomarkers for future clinical use.
肾上腺皮质癌（ACC）是一种严重的恶性肿瘤，早期诊断率低，死亡率高。在本研究中，我们使用了多种生物信息学分析方法来寻找 ACC 的潜在预后标志物和治疗靶点。我们使用基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据集进行差异表达分析。使用 WebGestalt 进行富集分析，使用 String 进行蛋白质-蛋白质分析。我们的研究首先通过 GEO 和 TCGA 数据库检测到 28 个上调和 462 个下调的差异表达基因。然后，通过 WebGestalt 工具和 KOBAS 网站以及 String 数据库分别进行 GO 功能分析、四个途径分析（KEGG、REACTOME、PANTHER 和 BIOCYC）和蛋白质-蛋白质相互作用网络分析，确定这些基因，并最终确定了 17 个枢纽基因。在经过 GEPIA 的一系列分析，包括基因突变、差异表达和预后之后，我们排除了一个与 ACC 预后无关的候选基因，并将剩余的基因重新纳入途径分析。我们通过 Degree、MCC 和 MNC 三种算法筛选出 CCNB1 和 NDC80 基因。随后，我们使用 TCGA 和 cBioPortal 数据库进行基因组分析，以更好地了解这两个枢纽基因。我们的数据还表明，CCNB1 和 NDC80 基因可能成为未来临床应用的 ACC 生物标志物。
