早期非小细胞肺癌潜在生物标志物的筛选及其预测价值：一项生物信息学分析
Screening of potential biomarkers and their predictive value in early stage non-small cell lung cancer: a bioinformatics analysis.
作者信息
Tu Hongbin, Wu Meihong, Huang Weiling, Wang Lixin
机构信息
Department of Integrated TCM & Western Medicine, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai 200433, China.
Department of Oncology, Changhai Hospital Affiliated to Second Military Medical University, Shanghai 200438, China.
出版信息
Transl Lung Cancer Res. 2019 Dec;8(6):797-807. doi: 10.21037/tlcr.2019.10.13.
BACKGROUND
Non-small cell lung cancer (NSCLC) remains the first leading cause of death in malignancies worldwide. Despite the early screening of NSCLC by low-dose spiral computed tomography (CT) in high-risk individuals caused a 20% reduction in the mortality, there still exists imperative needs for the identification of novel biomarkers for the diagnosis and treatment of lung cancer.
METHODS
mRNA microarray datasets GSE19188, GSE33532, and GSE44077 were searched, and the differentially expressed genes (DEGs) were obtained using GEO2R. Functional and pathway enrichment analyses were performed for the DEGs using DAVID database. Protein-protein interaction (PPI) network was plotted with STRING and visualized by Cytoscape. Module analysis of the PPI network was done through MCODE. The overall survival (OS) analysis of genes from MCODE was performed with the Kaplan Meier-plotter.
RESULTS
A total of 221 DEGs were obtained, which were mainly enriched in the terms related to cell division, cell proliferation, and signal transduction. A PPI network was constructed, consisting of 221 nodes and 739 edges. A significant module including 27 genes was identified in the PPI network. Elevated expression of these genes was associated with poor OS of NSCLC patients, including UBE2T, UNF2, CDKN3, ANLN, CCNB2, and CKAP2L. The enriched functions and pathways included protein binding, ATP binding, cell cycle, and p53 signaling pathway.
CONCLUSIONS
The DEGs in NSCLC have the potential to become useful targets for the diagnosis and treatment of NSCLC.
背景
非小细胞肺癌（NSCLC）仍然是全球恶性肿瘤死亡的首要原因。尽管对高危个体进行低剂量螺旋计算机断层扫描（CT）早期筛查非小细胞肺癌可使死亡率降低20%，但仍迫切需要鉴定用于肺癌诊断和治疗的新型生物标志物。
方法
搜索mRNA微阵列数据集GSE19188、GSE33532和GSE44077，并使用GEO2R获得差异表达基因（DEG）。使用DAVID数据库对DEG进行功能和通路富集分析。用STRING绘制蛋白质-蛋白质相互作用（PPI）网络，并通过Cytoscape进行可视化。通过MCODE对PPI网络进行模块分析。使用Kaplan Meier绘图仪对来自MCODE的基因进行总生存期（OS）分析。
结果
共获得221个DEG，主要富集在与细胞分裂、细胞增殖和信号转导相关的术语中。构建了一个PPI网络，由221个节点和739条边组成。在PPI网络中鉴定出一个包含27个基因的显著模块。这些基因的表达升高与非小细胞肺癌患者的不良OS相关，包括UBE2T、UNF2、CDKN3、ANLN、CCNB2和CKAP2L。富集的功能和通路包括蛋白质结合、ATP结合、细胞周期和p53信号通路。
结论
非小细胞肺癌中的DEG有可能成为非小细胞肺癌诊断和治疗的有用靶点。
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