Identification Of Novel Biomarkers In Ovarian Cancer: Systems Biology Approaches

İçindekiler

• 1. Introduction
•        1.1. RNA-based ovarian cancer research       
•        1.2. Ovarian cancer research should meet integrative multi-omics science
•        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
•        3.2. Reporter biomolecules of ovarian cancer were identified through network medicine perspective
•        3.3. Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
•        3.4. Ovarian cancer differential interactome and network entropy analysis reveal new candidate biomarkers
•        3.5. Integrative and comperative analysis of ovarian diseases point out molecular signatures
• 4. Conclusion
• 5. References