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基于网络的遗传分析揭示滤泡性甲状腺癌和滤泡状甲状腺腺瘤之间的细胞通路差异。

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma.

机构信息

Department of Computer Science & Engineering, Manarat International University, Khagan, Dhaka 1343, Bangladesh.

Electrical and Electronic Engineering, Islamic University, Kushtia 7005, Bangladesh.

出版信息

Int J Environ Res Public Health. 2020 Feb 20;17(4):1373. doi: 10.3390/ijerph17041373.

Abstract

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

摘要

恶性甲状腺癌(如滤泡状甲状腺癌 [FTC])的发病机制和进展的分子机制,以及这些机制与良性甲状腺病变的区别,目前仍知之甚少。在这项研究中,我们采用基于网络的整合分析方法,分析了 FTC 和良性滤泡状甲状腺腺瘤 [FTA] 病变转录组,以鉴定它们之间存在差异的关键基因和途径。我们首先分析了来自 FTC 和 FTA 组织的基因表达微阵列数据集(基因表达综合数据库 GSE82208,n=52),以鉴定差异表达基因(DEG)。然后,使用基因本体论(GO)和京都基因与基因组百科全书(KEGG)资源对这些 DEG 进行途径分析,以鉴定潜在的重要途径,并检查蛋白质-蛋白质相互作用(PPI)以鉴定途径枢纽基因。我们的数据分析确定了 598 个 DEG,其中 133 个基因在 FTC 中表达较高,465 个基因表达较低。我们确定了四个与 FTC 高表达相关的重要途径(叶酸一碳池、p53 信号通路、孕激素介导的卵母细胞成熟信号通路和细胞周期途径);八个途径与相对 FTC 低表达的 DEG 相关。十个 GO 组与 FTC 高表达 DEG 显著相关,80 个与 FTC 低表达 DEG 相关。PPI 分析然后基于度和中间中心性确定了 12 个潜在的枢纽基因;即 TOP2A、JUN、EGFR、CDK1、FOS、CDKN3、EZH2、TYMS、PBK、CDH1、UBE2C 和 CCNB2。此外,还确定了可能导致 FTC 和 FTA 之间观察到的基因表达差异的转录因子(TF),包括 FOXC1、GATA2、YY1、FOXL1、E2F1、NFIC、SRF、TFAP2A、HINFP 和 CREB1。我们还确定了可能影响 DEG 转录水平的 microRNA(miRNA);这些包括 hsa-mir-335-5p、-26b-5p、-124-3p、-16-5p、-192-5p、-1-3p、-17-5p、-92a-3p、-215-5p 和 -20a-5p。因此,我们的研究确定了 FTC 和良性 FTA 之间存在差异的 DEG、分子途径、TF 和 miRNA,反映了不同的分子机制。鉴于这些病变的一般相似性和共同的组织起源,其中一些差异可能反映了恶性进展的潜力,包括 FTC 的有用候选生物标志物和确定 FTC 发病机制的重要因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/895c/7068514/a8c57e1b28ec/ijerph-17-01373-g001.jpg

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