Abstract
Epithelial ovarian cancer (EOC) is a lethal gynecological cancer, of which paclitaxel resistance is the major factor limiting treatment outcomes, and identification of paclitaxel resistance-related genes is arduous. We obtained transcriptomic data from seven paclitaxel-resistant ovarian cancer cell lines and corresponding sensitive cell lines. Define genes significantly up-regulated in at least three resistant cell lines, meanwhile they did not down-regulate in the other resistant cell lines as candidate genes. Candidate genes were then ranked according to the frequencies of significant up-regulation in resistant cell lines, defining genes with the highest rankings as paclitaxel resistance-related genes (PRGs). Patients were grouped based on the median expression of PRGs. The lipid metabolism-related gene set and the oncological gene set were established and took intersections with genes co-upregulated with PRGs, obtaining 229 co-upregulated genes associated with lipid metabolism and tumorigenesis. The PPI network obtained 19 highly confidential synergistic targets (interaction score > 0.7) that directly associated with CPT1A. Finally, FASN and SCD were up-stream substrate provider and competitor of CPT1A, respectively. Western blot and qRT-PCR results confirmed the over-expression of CPT1A, SCD and FASN in the A2780/PTX cell line. The inhibition of CPT1A, SCD and FASN down-regulated cell viability and migration, pharmacological blockade of CPT1A and SCD increased apoptosis rate and paclitaxel sensitivity of A2780/PTX. In summary, our novel bioinformatic methods can overcome difficulties in drug resistance evaluation, providing promising therapeutical strategies for paclitaxel-resistant EOC via taregting lipid metabolism-related enzymes.