Tantai J-C, Pan X-F, Zhao H
Department of Thoracic Surgery, Shanghai Chest Hospital Shanghai Jiao Tong University, Shanghai, China.
Eur Rev Med Pharmacol Sci. 2015 Apr;19(8):1364-72.
A combination of comparative analysis of gene expression profiles between normal tissue samples and small cell lung cancer (SCLC) samples and network analysis was performed to identify key genes in SCLC.
Microarray data set GSE43346 was downloaded from Gene Expression Omnibus (GEO), including 43 normal tissue samples and 23 clinical SCLC samples. Differentially expressed genes (DEGs) were screened out with t-test. Coexpression network and gene regulatory network were then constructed for the DEGs. GO enrichment analysis as well as KEGG pathway were performed with DAVID online tools to reveal over-represented biological processes.
A total of 457 DEGs were obtained in SCLC, 259 up-regulated and 198 down-regulated. Some of them exhibited enzyme inhibitor activity and chemokine activity. A coexpression network including 457 nodes was constructed, from which a functional module was extracted. Genes in the modules were closely related with cell cycle. Top 10 nodes in the regulatory network were acquired and their sub-networks were extracted from the whole network. Genes in these sub-networks were related to cell cycle, apoptosis and transcription. A network comprising 43 microRNAs (miRNAs) and their target genes (also DEGs) were also constructed. Regulation of cell proliferation, cell cycle and regulation of programmed cell death were over-represented in these genes.
A range of DEGs were revealed in SCLC, which could enhance the understandings about the pathogenesis of this disease and provide potential molecular targets for diagnosis as well as treatment.
通过对正常组织样本和小细胞肺癌（SCLC）样本之间的基因表达谱进行比较分析，并结合网络分析，以鉴定SCLC中的关键基因。
从基因表达综合数据库（GEO）下载微阵列数据集GSE43346，其中包括43个正常组织样本和23个临床SCLC样本。采用t检验筛选差异表达基因（DEG）。然后为这些DEG构建共表达网络和基因调控网络。使用DAVID在线工具进行GO富集分析以及KEGG通路分析，以揭示过度富集的生物学过程。
在SCLC中总共获得了457个DEG，其中259个上调，198个下调。其中一些表现出酶抑制剂活性和趋化因子活性。构建了一个包含457个节点的共表达网络，并从中提取了一个功能模块。该模块中的基因与细胞周期密切相关。获取了调控网络中的前10个节点，并从整个网络中提取了它们的子网络。这些子网络中的基因与细胞周期、细胞凋亡和转录相关。还构建了一个由43个微小RNA（miRNA）及其靶基因（也是DEG）组成的网络。这些基因中细胞增殖调控、细胞周期调控和程序性细胞死亡调控过度富集。
在SCLC中揭示了一系列DEG，这可以增强对该疾病发病机制的理解，并为诊断和治疗提供潜在的分子靶点。
