Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
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DOI：
10.12659/msm.918620
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发表时间：
2019-12-05
影响因子：
3.1
通讯作者：
Ren, Fu
中科院分区：
文献类型：
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作者：
Liu, Xingyuan;Liu, Xuefeng;Ren, Fu
背景资料：非小细胞肺癌（Non-small cell lung cancer，NSCLC）是影响人类健康的主要肺癌组织学类型，但目前缺乏用于治疗诊断和预后判断的生物标志物。材料/方法：本前瞻性研究从Gene Expression Omnibus数据库中下载GSE 18842基因表达谱，包括46例肿瘤和45例对照。在筛选差异表达基因（DEG）后，分别对上调的差异表达基因（uDEG）和下调的差异表达基因（dDEG）进行功能富集分析和KEGG分析。蛋白质相互作用（PPI）网络之间的DEG和相应的编码蛋白质复合物，使用STRING数据库构建，使用Cytoscape分析。Kaplan-Meier方法用于验证与hub基因相关的生存率。结果：在基因整合后，NSCLC组织和正常对照组织中共检测到368个DEG（168个uDEG和200个dDEG）。我们建立了一个PPI网络的DEG，其中有249个节点和1472条边的蛋白质对。生存分析证实了10个连接度最高的未定义枢纽基因（CDK 1、UBE 2C、AURKA、CCNA 2、CDC 20、CCNB 1、TOP 2A、ASPM、MAD 2L 1和KIF 11），其中9个与NSCLC的总生存率较差相关。结论：UBE 2C、AURKA、CCNA 2、CDC 20、CCNB 1、TOP 2A、ASPM、MAD 2L 1和KIF 11是NSCLC诊断和预后的关键生物标志物，而KEGG分析结果显示有丝分裂细胞周期通路可能是NSCLC进展的信号通路。这些基因有望成为诊断NSCLC的生物标志物，并为开发靶向治疗NSCLC的药物提供了新的途径。
Background: Non-small cell lung cancer (NSCLC) is the main histologic form of lung cancer that affects human health, but biomarkers for therapeutic diagnosis and prognosis of the disease are currently lacking.Material/Methods: The gene expression profile GSE18842 was downloaded from the Gene Expression Omnibus database in this prospective study, which consisted of 46 tumors and 45 controls. After screening differentially expressed genes (DEGs), we conducted functional enrichment analysis and KEGG analysis with upregulated differentially expressed genes (uDEGs) and downregulated differentially expressed genes (dDEGs), respectively. Protein-protein interaction (PPI) networks among DEGs and corresponding coding protein complexes, constructed using the STRING database, were analyzed using Cytoscape. Kaplan-Meier method was used to verify survival associated with hub genes. The GEPIA webserver was used to plot the gene expression level heat map of hub genes between NSCLC and adjacent lung tissues in the TCGA database.Results: We identified 368 DEGs (168 uDEGs and 200 dDEGs) in NSCLC samples relative to control samples after gene integration. We established a PPI network for the DEGs, which had 249 nodes and 1472 edges protein pairs. Ten undefined hub genes with the highest connectivity degree (CDK1, UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11) were verified by survival analysis, and 9 of them were associated with poorer overall survival in NSCLC. The expression reliability of hub genes was verified by use of the GEPIA web tool.Conclusions: The results suggested that UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11 are inherent key biomarkers for diagnosis and prognosis, while KEGG analysis results showed the mitotic cell cycle pathway is a probable signaling pathway contributing to NSCLC progression. These genes could be promising biomarkers for diagnosis and provide a new approach for developing targeted therapeutic NSCLC drugs.