Differential Gene Expression Analysis of Tumor Samples and Cell Lines

Sequence data quality check was performed using FastQC. The RNA-Seq data were mapped to the hg19 reference genome by TopHat for Illumina using default options. Assembly of transcripts and estimation of their abundance (fragments per kilobase of exon per million fragments mapped (FPKM)) were carried out using Cufflinks software. Differential gene expression analyses between groups were performed using HTSeq software for tumor samples^{66 (link)} and DESeq2 software for cell line samples^{67 (link)}. Heatmap.2 in the ‘gplots’ package of the R program was used for the construction of heat maps. Genes that were up- or downregulated in both C13^* and SKOV3 cells with a P value of less than 0.05 were analyzed using IPA software (Qiagen, Redwood City, CA, USA; http://www.ingenuity.com) in order to assign the genes to different functional networks. Fisher’s exact test was utilized to calculate P values with IPA. IPA generated a z-score for each predefined canonical pathway, where a z-score of at least 2 was associated with a confidence level of at least 99% that results were not chance. Positive and negative z-scores represented the activated and suppressed states, respectively.