Wei Lei, Xu Dong, Qian Yechang, Huang Guoyi, Ma Wei, Liu Fangying, Shen Yanhua, Wang Zhongfu, Li Li, Zhang Shanfang, Chen Yafang
Department of Respiratory Disease, Baoshan District Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, People's Republic of China.
Medical College of Soochow University, Suzhou, People's Republic of China.
Int J Chron Obstruct Pulmon Dis. 2015 Jun 10;10:1103-9. doi: 10.2147/COPD.S68570. eCollection 2015.
To investigate the gene-expression profile of chronic obstructive pulmonary disease (COPD) patients and explore the possible therapeutic targets.
The microarray raw dataset GSE29133, including three COPD samples and three normal samples, was obtained from Gene Expression Omnibus. After data preprocessing with the Affy package, Student's t-test was employed to identify the differentially expressed genes (DEGs). The up- and downregulated DEGs were then pooled for gene-ontology and pathway-enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The upstream regulatory elements of these DEGs were also explored by using Whole-Genome rVISTA. Furthermore, we constructed a protein-protein interaction (PPI) network for DEGs. The surfactant protein D (SP-D) serum level and HLA-A gene frequency in COPD patients and healthy controls were also measured by enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction, respectively.
A total of 39 up- and 15 downregulated DEGs were screened. Most of the upregulated genes were involved in the immune response process, while the downregulated genes were involved in the steroid metabolic process. Moreover, we also found that HLA-A has the highest degree in the PPI network. The SP-D serum level and HLA-A gene frequency in COPD patients were significantly higher than those in healthy controls (13.62±2.09 ng/mL vs 10.28±2.86 ng/mL; 62.5% vs 12.5%; P<0.05).
Our results may help further the understanding of the mechanisms of COPD. The identified DEGs, especially HLA-A, may serve as diagnosis markers for COPD.
研究慢性阻塞性肺疾病（COPD）患者的基因表达谱，并探索可能的治疗靶点。
从基因表达综合数据库获取微阵列原始数据集GSE29133，其中包括3个COPD样本和3个正常样本。使用Affy软件包进行数据预处理后，采用学生t检验来识别差异表达基因（DEG）。然后将上调和下调的DEG汇总，使用注释、可视化和综合发现数据库（DAVID）进行基因本体论和通路富集分析。还通过全基因组rVISTA探索这些DEG的上游调控元件。此外，我们构建了DEG的蛋白质-蛋白质相互作用（PPI）网络。分别采用酶联免疫吸附测定（ELISA）和实时聚合酶链反应测量COPD患者和健康对照者的表面活性蛋白D（SP-D）血清水平和HLA-A基因频率。
共筛选出39个上调和15个下调的DEG。大多数上调基因参与免疫反应过程，而下调基因参与类固醇代谢过程。此外，我们还发现HLA-A在PPI网络中的度数最高。COPD患者的SP-D血清水平和HLA-A基因频率显著高于健康对照者（13.62±2.09 ng/mL对10.28±2.86 ng/mL；62.5%对12.5%；P<0.05）。
我们的结果可能有助于进一步了解COPD的发病机制。所鉴定的DEG，尤其是HLA-A，可能作为COPD的诊断标志物。
