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Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model
Amir Abbas Esmaeilzadeh1, Mahdis Kashian2, Hayder Mahmood Salman3
1Salamat yar Behesht Dayan, Dayan Biotech Company, Tehran 14531, Iran.
Abstract:
Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.
Insights
This study introduces an optimized network model to identify key drivers and pathways in epithelial ovarian cancer (EOC). The findings highlight potential new therapeutic targets and biomarkers for improved EOC treatment strategies.
Area of Science:
- Oncology
- Bioinformatics
- Systems Biology
Background:
- Epithelial ovarian cancer (EOC) is aggressive with poor outcomes, necessitating a deeper understanding of its tumorigenesis.
- Intra-tumoral heterogeneity and pathological mechanisms in EOC require systematic characterization for treatment development.
Purpose of the Study:
- To develop and validate an optimized hit network-target sets (OHNS) model for characterizing EOC.
- To identify driver genes, core modules, and hub genes within the EOC regulatory network.
- To assess the clinical relevance and therapeutic potential of identified network components.
Main Methods:
- Construction of an epithelial ovarian cancer regulatory network using TCGA data.
- Application of three distinct methods to generate hit network-sets (HNSs) for identifying driver nodes, core modules, and core nodes.
- Integration of identified components into an optimized HNS (OHNS) and assessment of its network topology, control potential, and clinical value.
- Validation of hub gene and protein expression using immunohistochemistry (IHC), qRT-PCR, and Western blotting.
Main Results:
- The OHNS demonstrated superior network centrality and controllability compared to other HNSs.
- Analysis revealed the involvement of endometrial cancer signaling, PI3K/AKT, NER, and BMP pathways in EOC.
- Key hub genes including MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN were identified as potential therapeutic targets and biomarkers.
Conclusions:
- The OHNS model effectively captures EOC progression and intra-tumoral heterogeneity.
- Identified hub genes and pathways offer promising candidates for novel multi-target therapies and diagnostic biomarkers for EOC.
- This research provides a foundation for optimizing effective treatment regimens and guiding future therapeutic strategies in ovarian cancer.
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