Identification Of Novel Biomarkers In Ovarian Cancer Systems Biology Approaches — Esra Göv

1 Introduction 1 1 RNA based ovarian cancer research 1 1 1 RNA expression profiling in ovarian cancer 1 1 2 Expression profiling of microRNAs 1 1 3 Ovarian cancer associated signaling pathways 1 1 4 Integrative approaches in ovarian cancer research 1 2 Ovarian cancer research should meet integrative multi omics science 1 2 1 Human transcriptional regulatory network 1 2 2 Integration of transcriptome data with biological networks 1 2 3 Differential co expression network in ovarian cancer 1 2 4 Differential interactome in ovarian cancer 1 3 Ovarian diseases including polycystic ovarian syndrome PCOS ovarian endometriosis and ovarian cancer 1 4 Aim of the Study 2 Materials and Methods 2 1 Reconstruction of transcriptional regulatory network of H sapiens 2 2 Topological analysis of transcriptional regulatory networks 2 3 Selection of gene expression datasets 2 4 Identification of differentially expressed genes 2 5 Reconstruction of ovarian cancer specific subnetwork 2 6 Analysis of network performance 2 7 Robustness analysis 2 8 Identification of reporter receptors membrane proteins transcription factors and miRNAs 2 9 Determination of reporter metabolites 2 10 Enrichment analyses of DEGs and reporter metabolites 2 11 Comprehensive networks in CEPI stroma and tumor tissues 2 12 Construction of co expression networks in diseased and healthy states 2 13 Determination of network modules and their differential co expression 2 14 Prognostic power analysis of module genes 2 15 Identification of transcriptional regulatory network including module genes 2 16 Screening the differential expression of the module in different tumor types 2 17 Differential Protein Interactome Analysis 2 17 1 Protein interaction data 2 17 2 Determination of entropies corresponding to each interaction 3 Results and Discussion 3 1 A generic transcriptional regulatory network of H sapiens was reconstructed 3 1 1 The network motifs provide a deeper investigation into the topological architecture 3 1 2 Core network topology endorses the previous findings on miRNA and gene interactions 3 1 3 Target genes may be regulated in cooperation of regulators 3 1 4 A target gene may be regulated by multiple upstream effectors in a hierarchical operation 3 1 5 Process specific subnetworks were also dominated by hierarchical operation of regulators 3 1 6 Ovarian cancer specific transcriptional regulatory network 3 2 Reporter biomolecules of ovarian cancer were identified through network medicine perspective 3 2 1 Transcriptomic signatures of ovarian CEPI stroma and tumor tissues 3 2 2 Signaling receivers reporter receptors and membrane proteins 3 2 3 Regulatory signatures reporter transcription factors and microRNAs 3 2 4 Metabolomic signatures reporter metabolites 3 2 5 Biological insights of transcriptomic signatures and reporter metabolites 3 2 6 Tissue specific comprehensive networks with enriched reporter biomolecules 3 3 Differential co expression analysis reveals a novel prognostic gene module in ovarian cancer 3 3 1 Differential gene expression in ovarian cancer 3 3 2 Co expression profiles in ovarian cancer 3 3 3 Co expressed gene modules in diseased and healthy states 3 3 4 The module was differentially co expressed in ovarian cancer 3 3 5 Prognostic performance of the gene module 3 3 6 Transcriptional regulators of the module genes 3 3 7 Differential expression of the module genes in different tumor types 3 4 Ovarian cancer differential interactome and network entropy analysis reveal new candidate biomarkers 3 4 1 DNA repair responses 3 4 2 Alternative splicing mechanisms and abnormal protein expression in tumor cells 3 4 3 Separation of sister chromatids through ESPL1 3 4 4 Suppression of EGFR associated proliferation via EGFR endocytosis and retinoids 3 4 5 Nucleocytoplasmic translocation of estrogen receptor in ovarian cancer 3 4 6 Cellular response to malignancies 3 5 Integrative and compe