Predicting responses to cisplatin based on zFiltering of the Gene Signature in ovarian cancer

Background and Aim: Ovarian cancer is a gynecological carcinoma and accounts for about ۵% of all cancer deaths worldwide. Chemotherapy with platinum-based drugs such as cisplatin and carboplatin is the main strategy for ovarian cancer treatment. Nonetheless, resistance to these drugs is growing among patients. Different cellular pathways can be involved in chemotherapy resistance. Therefore, the evaluation of these pathways in chemo-resistance would help to identify patients who are resistant to chemotherapy. In this regard gene expression profiling and using the molecular signature for prediction of drug response would be a promising approach that can lead to finding the most effective drugs according to the tumor characteristics of patients. In this study, we analyzed the differential expression and differential correlation of the genes based on Affymetrix microarrays using two GSE۲۸۶۴۶ and GSE۱۵۳۷۲ datasets.Methods: Firs, Gene Fuzzy Scoring (GFS) method was applied to remove the batch effect from the data. After that Wilcoxon Rank Sum test (aka Mann-Whitney U Test) was used to identify the differentially expressed genes between the sensitive and resistant samples. Finally, structures of transcription factor-target regulatory network, miRNA-target and lncRNA-target regulatory network and Differential correlation analysis were implemented on drug resistance markers that were obtained from the previous step.Results: Analysis of microarray results showed that ۸۹ genes were highly differentially (up-regulated and down-regulated) expressed in the ۸ cisplatin-resistant samples. Twenty-five of the genes were not previously reported in the resistance of any type of cancers. While pathway analysis revealed a complex interrelationship between some of these genes, their regulating transcription factors, miRNA and lncRNA regulatory network and differentially expression correlation exhibited that these genes are independently involved in anti-cancer drug resistance.Conclusion: We identified genes that are differentially expressed in cisplatin-resistant ovarian cancer cells. Our study provided useful information on novel drug resistance genes in a potential candidate for ovarian cancer. These data may lead to the discovery of new drug resistance targets and perhaps the development of improved cancer chemotherapy strategies.

Keywords:

cisplatin-resistance , gene expression analysis , ovarian neoplasm , regulatory network.