基于生物信息学的未分化甲状腺癌恶性机制研究

Malignant Mechanism of Anaplastic Thyroid Carcinoma Identified by Integrated Bioinformatics Analysis

关键词:  未分化甲状腺癌  生物信息学  信号通路  蛋白-蛋白相互作用网络  差异基因

Key words:  anaplastic thyroid carcinoma  bioinformatics  signaling pathway  protein-protein interactions (PPI) network  differentially expressed genes

摘要:

目的 阐明未分化甲状腺癌（anaplastic thyroid carcinoma，ATC）与分化型甲状腺癌的分子通路差异，揭示ATC的恶性机制。方法 利用GEO数据库联合R语言分析ATC与乳头状甲状腺癌（papillary thyroid cancer，PTC）肿瘤组织的基因表达差异，以及ATC、PTC与正常甲状腺组织的共有差异基因；基于STRING数据库及Cytoscape软件构建蛋白-蛋白相互作用（PPI）网络，分析其关键网络节点和基因簇，并利用KEGG、DAVID数据库及ClueGO插件进行基因功能富集及注释。最后，利用RT-PCR检测关键节点在不同甲状腺癌细胞株中的表达差异。结果 和正常甲状腺组织相比，ATC和PTC存在775个共同差异基因，通路富集于PI3K-Akt信号通路、p53通路、肿瘤通路、细胞周期等与调控肿瘤发生发展相关的经典通路，以及炎症反应、胞外基质重塑、免疫反应、血管新生等肿瘤微环境相关通路改变。进一步筛选ATC和PTC差异基因发现，与PTC相比，ATC组织中p53通路、PI3K-Akt信号通路、胞外基质-受体相互作用、细胞周期、蛋白消化及吸收、吞噬体、补体途径等肿瘤关键通路显著激活。研究通过构建PPI网络并分析出3个关键基因簇，其功能主要与细胞周期、双链修复、细胞趋化、蛋白泛素化等重要生物进程相关。其中，TOP2A、IL8、UBE2S是各基因簇的关键节点，并与甲状腺恶性演进潜在相关。结论 研究利用生物信息学发现ATC发生发展的潜在分子机制网络，并揭示该网络中的关键基因簇及节点，为ATC的恶性机制及潜在治疗靶点提供理论依据。

Abstract:

OBJECTIVE To elucidate the differential molecular pathways between anaplastic thyroid carcinoma (ATC) and differentiated thyroid cancer, and revealing the malignant mechanism of ATC. METHODS Using the GEO database and R language to analyze differentially expressed genes between ATC and papillary thyroid cancer (PTC), and common differential gene sharing between ATC and PTC compared with normal thyroid tissue, respectively. Protein-protein interactions (PPI) were constructed based on STRING database and Cytoscape software. The key network nodes and gene clusters were analyzed. KEGG, DAVID database, and ClueGO plug-in were used for gene enrichment and annotation. Finally, the expression of key nodes in different thyroid cancer cell lines was detected by RT-PCR. RESULTS Compared with normal thyroid tissue, there were 775 common differential genes in ATC and PTC. Pathways were enriched in the PI3K-Akt signaling pathway, p53 pathway, cancer pathway, cell cycle, which were classic pathways involved in the regulation of tumorigenesis. Tumor microenvironment associated pathways such as inflammatory response, extracellular matrix remodeling, immune response, and angiogenesis also changed. Further screening of differentially expressed gene between ATC and PTC showed that p53 signaling pathway, PI3K-Akt signaling pathway, ECM-receptor interaction, cell cycle, protein digestion and absorption, phagosome, complement and coagulation cascades were significantly activated in ATC. Through the construction of the PPI network, there were 3 key gene clusters, which were related to important biological processes such as cell cycle, double-strand break repair, cell chemotaxis, and protein ubiquitination. Among them, TOP2A, IL8, UBE2S were the key nodes of each gene cluster. CONCLUSION This study unveil the potential molecular mechanisms for the malignancy of ATC by bioinformatics analysis, and reveal key gene clusters and nodes in the network. It provides a theoretical basis for the malignant mechanisms and potential therapeutic targets of ATC.