Liu Xiao, Wang Jun, Chen Mei, Liu Shilan, Yu Xiaodan, Wen Fuqiang
Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China,
Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China,
Onco Targets Ther. 2019 Jan 21;12:709-720. doi: 10.2147/OTT.S183944. eCollection 2019.
The aim of this study was to predict and explore the possible mechanism and clinical value of genetic markers in the development of lung cancer with a combined database to screen the prognostic genes of lung cancer.
Common differential genes in two gene expression chips (GSE3268 and GSE10072 datasets) were investigated by collecting and calculating from Gene Expression Omnibus and The Cancer Genome Atlas databases using R language. Five markers of gene composition (ribonucleotide reductase regulatory subunit M2 [RRM2], trophoblast glycoprotein [TPBG], transmembrane protease serine 4[TMPRFF4], chloride intracellular channel 3 [CLIC3], and WNT inhibitory factor-1 [WIF1]) were found by the stepwise Cox regression function when we further screened combinations of gene models, which were more meaningful for prognosis. By analyzing the correlation between gene markers and clinicopathological parameters of lung cancer and its effect on prognosis, the TPBG gene was selected to analyze differential expression, its possible pathways and functions were predicted using gene set enrichment analysis (GSEA), and its protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database; then, quantitative PCR and the Oncomine database were used to verify the expression differences of TPBG in lung cancer cells and tissues.
The expression levels of five genetic markers were correlated with survival prognosis, and the total survival time of the patients with high expression of the genetic markers was shorter than those with low expression (<0.001). GSEA showed that these high-expression samples enriched the gene sets of cell adhesion, cytokine receptor interaction pathway, extracellular matrix receptor pathway, adhesion pathway, skeleton protein regulation, cancer pathway and TGF-β pathway.
The high expression of five gene constituent markers is a poor prognostic factor in lung cancer and may serve as an effective biomarker for predicting metastasis and prognosis of patients with lung cancer.
本研究旨在通过联合数据库预测和探索基因标志物在肺癌发生发展中的可能机制及临床价值,以筛选肺癌的预后基因。
利用R语言从基因表达综合数据库(Gene Expression Omnibus)和癌症基因组图谱数据库(The Cancer Genome Atlas)收集并计算两个基因表达芯片(GSE3268和GSE10072数据集)中的常见差异基因。在进一步筛选对预后更有意义的基因模型组合时,通过逐步Cox回归函数发现了五个基因组成标志物(核糖核苷酸还原酶调节亚基M2 [RRM2]、滋养层糖蛋白[TPBG]、跨膜蛋白酶丝氨酸4 [TMPRSS4]、氯离子细胞内通道3 [CLIC3]和WNT抑制因子1 [WIF1])。通过分析基因标志物与肺癌临床病理参数之间的相关性及其对预后的影响,选择TPBG基因分析其差异表达,使用基因集富集分析(GSEA)预测其可能的途径和功能,并使用检索相互作用基因/蛋白质的搜索工具(STRING)数据库构建其蛋白质相互作用网络;然后,采用定量PCR和Oncomine数据库验证TPBG在肺癌细胞和组织中的表达差异。
五个基因标志物的表达水平与生存预后相关,基因标志物高表达患者的总生存时间短于低表达患者(<0.001)。GSEA显示,这些高表达样本富集了细胞黏附、细胞因子受体相互作用途径、细胞外基质受体途径、黏附途径、骨架蛋白调节、癌症途径和TGF-β途径的基因集。
五个基因组成标志物的高表达是肺癌预后不良的因素,可能作为预测肺癌患者转移和预后的有效生物标志物。