Shi W-Y, Liu K-D, Xu S-G, Zhang J-T, Yu L-L, Xu K-Q, Zhang T-F
Department of Respiratory Disease, Dachang Hospital, Baoshan District, Shanghai, China.
Eur Rev Med Pharmacol Sci. 2014;18(2):217-28.
We aim to explore the expression difference between lung cancer cells and normal lung cells, and to investigate the mechanism of lung cancer development. Besides, we predicted the potential target site of transcriptional factors and microRNAs for differentially expressed genes (DEGs), which may help to regulate expression of DEGs. Small molecules were also identified to cure lung cancer.
Gene expression profiles we used were downloaded from Gene Expression Omnibus (GEO) using accession number of GSE2378. Firstly, we identified differential genes between lung cancer cells and normal lung cells by using R package limma. Then, we detected the processes and pathways that changed in lung cancer cells by Gene Ontology (GO) and KEGG pathway enrichment analysis. Potential target sites of transcriptional factors and microRNAs were also detected based on gene annotation data in MSigDB. Finally, small molecule drugs were screened via querying Connectivity Map database.
We obtained 2961 differentially expressed genes between lung cancer cells and normal lung cells. Besides changes in cell cycle, metabolic processes and proteasome were also dramatically disordered. Some DEGs shared target sites of the transcription factor such as E2F, ETS and CEBPB. Target sites of hsa-miR-196a and hsa-miR-200c were also significantly enriched by DEGs. Iloprost simulated the state of normal cells, while MS-275 might be potential pathogenic substances.
We investigate the lung cancer from Gene Ontology, pathway, transcription factors and microRNAs based on gene expression profiles. All these results may facilitate lung cancer treatment with a new breakthrough.
我们旨在探索肺癌细胞与正常肺细胞之间的表达差异,并研究肺癌发生发展的机制。此外,我们预测了差异表达基因(DEGs)的转录因子和微小RNA的潜在靶位点,这可能有助于调控DEGs的表达。还鉴定出了可治疗肺癌的小分子。
我们使用的基因表达谱是从基因表达综合数据库(GEO)下载的,登录号为GSE2378。首先,我们使用R包limma鉴定肺癌细胞与正常肺细胞之间的差异基因。然后,通过基因本体论(GO)和KEGG通路富集分析检测肺癌细胞中发生变化的过程和通路。还基于MSigDB中的基因注释数据检测转录因子和微小RNA的潜在靶位点。最后,通过查询连通性图谱数据库筛选小分子药物。
我们获得了肺癌细胞与正常肺细胞之间的2961个差异表达基因。除细胞周期变化外,代谢过程和蛋白酶体也严重紊乱。一些DEGs共享转录因子如E2F、ETS和CEBPB的靶位点。hsa-miR-196a和hsa-miR-200c的靶位点也被DEGs显著富集。伊洛前列素模拟正常细胞状态,而MS-275可能是潜在的致病物质。
我们基于基因表达谱从基因本体论、通路、转录因子和微小RNA方面研究肺癌。所有这些结果可能有助于肺癌治疗取得新突破。