Molecular Mechanisms and Potential Treatment Targets for Ovarian Cancer by Analyzing Transcriptional Regulatory Network

Background: Ovarian cancer is the ninth most common cancer. Microarray technology could analyze genes differentially expressed during cancer progression. Purpose: To analyze the molecular mechanisms of the development in ovarian cancer and screen potential therapeutic targets. Methods: GSE37582 was downloaded from Gene Expression Omnibus database. The dataset contained 121 lymphoblastoid cell lines (LCLs) from 74 ovarian cancer patients and 47 cancer-free controls. Lymphocytes isolated from blood samples of each patient and control were used to establish LCLs via EBV transformation. The differentially expressed genes (DEGs) were identified by LIMMA package, followed by functional enrichment analysis. TRANSFAC database was utilized to select transcription factors (TFs) and construct a transcriptional regulatory network. Networked Gene Prioritizer method was performed to prioritize cancer-associated regulatory subnets. Results: Totally, 131 up- and 112 down- regulated genes were screened in ovarian cancer, which were enriched in several processes such as response to protein stimulus, and anti-apoptosis. A transcriptional regulatory network was constructed including 2630 nodes and 5462 interactions. HSF1 (heat shock transcription factor 1), E2F2 (E2F transcription factor 2), EGR1 (early growth response 1) and ETV4 (ets variant 4) were identified as differentially expressed TFs. Three transcriptional regulatory subnets were obtained as candidate subnets, based on which RPL26 and MST1 were regulated by MYC and DUSP1 was regulated by USF1. Conclusion: The differentially expressed TF, HSF1, and regulatory interactions of MYCRPL26/ MST1 and USF1-DUSP1 might play critical roles in ovarian cancer progression and these molecules could provide theoretical bases for further researches on ovarian cancer treatment.