mRNA-seq Analysis of Transcriptomes of the PC9R and PC9 cells

The goals of this study is to compare the whole genome transcriptome of gefitinib-resistant NSCLC cell line (PC9R) with its gefitinib-sensitive counterpart (PC9) using RNA-seq tecnology Methods: Genome-wide mRNA profiles of the PC9R and PC9 cells were generated by deep sequencing, using Illumina Hiseq2000. The sequence reads that passed quality filters were analyzed in the following steps: 1) RNA-seq reads were aligned to the hg19 genome assembly using TopHat (http://bioinformatics.oxfordjournals.org/content/25/9/1105.short) with the default parameters; 2) Expression index was generated using GFOLD V1.0.9 job count (http://bioinformatics.oxfordjournals.org/content/early/2012/08/23/bioinformatics.bts515); 3) Differential expression were calculated using GFOLD V1.0.9 job diff. Gene expression was quantified in rpkm (reads per kilobase of exon per million mapped sequence reads); 4) GFOLD, a generalized fold change, was used to rank the differentially expressed genes from the RNA-seq data. The GFOLD value can be considered as a reliable log2-fold change when only a single biological replicate is available Results: We found that hundreds of genes were either down- or up-regulated in the PC9R cells compared with the PC9 cells. Specifically, 6% of the total detected genes (1487 genes) were up-regulated in the PC9R cells, with a GFOLD value over 1, and 5% of the total detected genes (1112 genes) were down-regulated, with a GFOLD value less than -1. Conclusions: Our study reveals the differentially expressed genes in gefitinib-resistant NSCLC cells comparing with the sensitive cells in a genome-wide scale. This results help to provide the novel insight into the gefitinib-resistant mechanism. Overall design: The genome-wdie transcriptome study of gefitinib-resistant NSCLC cells (PC9R) comparing with the sensitive cells (PC9) using mRNA-seq technology

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Qi M, Tian Y, Li W, Li D, Zhao T, Yang Y, Li Q, Chen S, Yang Y, Zhang Z, Tang L, Liu Z, Su B, Li F, Feng Y, Fei K, Zhang P, Zhang F, Zhang L