Identification Of Novel Biomarkers In Ovarian Cancer: Systems Biology Approaches

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 comperative analysis of ovarian diseases
point out molecular signatures
3.5.1. Transcriptomic signatures: Differentially
expressed genes
3.5.2. Metabolic signatures: Reporter metabolites
3.5.3. Regulatory signatures: Reporter TFs and
miRNAs
4. Conclusion
5. References