Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis
Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis
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DOI：
10.1002/jcp.28932
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发表时间：
2019-12-01
影响因子：
5.6
通讯作者：
Liu, Jintao
中科院分区：
文献类型：
--
作者：
Liu, Lu;He, Chen;Liu, Jintao
甲状腺癌是一种常见的内分泌恶性肿瘤，在世界范围内发病率迅速上升。虽然由于早期诊断，其死亡率稳定或下降，但其存活率因不同的肿瘤类型而异。因此，本研究的目的是确定甲状腺癌的关键生物标志物和新的治疗靶点。从Gene expression Omnibus数据库中下载GSE3467、GSE5364、GSE29265和GSE53157的表达谱，共包括97例甲状腺癌和48例正常样本。在每个数据集中筛选显著差异表达基因（deg）后，我们使用鲁棒秩聚集法在四个数据集中鉴定出358个鲁棒差异表达基因，其中包括135个上调基因和224个下调基因。基因本体（Gene Ontology）和京都基因百科全书（Kyoto encyclopedia of Genes and Genomes）途径富集分析分别由DAVID和KOBAS在线数据库进行。结果表明，这些deg在多种癌症相关功能和途径中显著富集。然后，利用STRING数据库构建蛋白-蛋白互作网络，并进行模块分析。最后，我们从整个网络中筛选出LPAR5、NMU、FN1、NPY1R和CXCL12 5个枢纽基因。基于the Cancer Genome Atlas数据库对这些枢纽基因的表达验证和生存分析表明了上述结果的稳健性。总之，这些结果为甲状腺癌的诊断、预后和靶向治疗提供了新的、可靠的生物标志物，为进一步的临床应用提供了依据。
Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.