J-C Tantai1, X-F Pan, H Zhao. 1. Department of Thoracic Surgery, Shanghai Chest Hospital Shanghai Jiao Tong University, Shanghai, China. hengzhao22@hotmail.com.
Abstract
OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.