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Updated: Feb 12, 2026
Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated
Xiao Yang1, Shaoming Zhu2, Li Li3
1Department of Obstetrics and Gynecology.
Background:
The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.
Results:
The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network.
Conclusion:
This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.
Insights
Integrated bioinformatics identified key genes and pathways in ovarian cancer. This research offers potential targets for early diagnosis, prevention, and treatment of this high-mortality gynecological malignancy.
Area of Science:
- Genomics
- Bioinformatics
- Oncology
Background:
- Ovarian cancer has the highest mortality rate among gynecological cancers.
- The precise causes and molecular mechanisms of ovarian cancer remain unclear.
- Identifying key pathogenic genes is crucial for understanding ovarian cancer development.
Purpose of the Study:
- To identify key pathogenic genes in ovarian cancer using integrated bioinformatics.
- To reveal potential molecular mechanisms underlying ovarian cancer development.
- To provide potential therapeutic targets for ovarian cancer.
Main Methods:
- Downloaded and integrated gene expression profiles from GEO datasets (GDS3592, GSE54388, GSE66957).
- Identified differentially expressed genes (DEGs) using bioinformatics analysis.
- Performed Gene Ontology (GO) and KEGG pathway enrichment analyses.
- Constructed protein-protein interaction (PPI) networks using the STRING database.
Main Results:
- Identified 190 DEGs (99 upregulated, 91 downregulated) across 115 ovarian samples.
- GO analysis revealed DEGs involved in cell proliferation, adhesion, differentiation, and signaling cascades.
- KEGG analysis highlighted involvement in Wnt signaling, amino acid metabolism, and tumor signaling pathways.
- Identified 17 key genes from the PPI network.
Conclusions:
- Integrated bioinformatics analysis effectively screens for DEGs and pathways in ovarian cancer.
- Findings enhance understanding of molecular mechanisms in ovarian cancer development.
- This approach offers clinical significance for early diagnosis, prevention, and treatment targeting.
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