Peng Ying, Zhang Han-Wen, Cao Wei-Han, Mao Ying, Cheng Ruo-Chuan
Kunming Medical University of Yunnan Province, Kunming, Yunnan 650500, People's Republic of China.
Thyroid Disease Diagnosis and Treatment Center, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, People's Republic of China.
Cancer Manag Res. 2020 Sep 28;12:9235-9246. doi: 10.2147/CMAR.S266473. eCollection 2020.
Papillary thyroid carcinoma (PTC) has increased rapidly over recent years, and radiation, hormone effects, gene mutations, and others were viewed as closely related. However, the molecular mechanisms of PTC have not been cleared. Therefore, we intended to screen more accurate key genes and pathways of PTC by combining RT profiler PCR arrays and bioinformatics methods in this study.
RT profiler PCR arrays were firstly analyzed to identify differential expression genes (DEGs) in PTC. RT-qPCR were performed to verify the most significant differential expression genes. The TCGA database was used to further verify for expanded data. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was analyzed. To construct the protein-protein interaction (PPI) network, we used STRING and Cytoscape to make module analysis of these DEGs.
Sixteen differentially expressed genes were presented in RT profiler PCR arrays, including 13 down-regulated DEGs (DEGs) and three up-regulated DEGs (DEGs), while 13 stable DEGs were eventually verified. A total of 155 DEGs were presented in the TCGA database, including 82 up-regulated DEGs (DEGs) and 73 down-regulated DEGs (dDEGs). A total of 29 important genes were extracted after integrating these two results, GO and KEGG analyses were used to observe the possible mechanisms of action of these DEGs. The PPI network was constructed to observe hub genes. Prognostic analysis further demonstrated the involvement of these genes in the biological processes of PTC.
This study identified some potential molecular targets and signal pathways, which might help us raise our awareness of the mechanisms of PTC.
近年来,甲状腺乳头状癌(PTC)发病率迅速上升,辐射、激素作用、基因突变等被认为与之密切相关。然而,PTC的分子机制尚未明确。因此,本研究旨在通过结合RT Profiler PCR阵列和生物信息学方法筛选出更准确的PTC关键基因和信号通路。
首先通过RT Profiler PCR阵列分析鉴定PTC中的差异表达基因(DEG)。采用RT-qPCR验证差异最显著的基因。利用TCGA数据库进行进一步验证以扩大数据量。对基因本体论(GO)和京都基因与基因组百科全书(KEGG)进行富集分析。为构建蛋白质-蛋白质相互作用(PPI)网络,我们使用STRING和Cytoscape对这些DEG进行模块分析。
RT Profiler PCR阵列中呈现16个差异表达基因,包括13个下调的DEG和3个上调的DEG,最终验证了13个稳定的DEG。TCGA数据库中共呈现155个DEG,包括82个上调的DEG和73个下调的DEG。整合这两个结果后共提取出29个重要基因,利用GO和KEGG分析观察这些DEG可能的作用机制。构建PPI网络以观察枢纽基因。预后分析进一步证明这些基因参与PTC的生物学过程。
本研究确定了一些潜在的分子靶点和信号通路,这可能有助于提高我们对PTC发病机制的认识。