Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model...

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.