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Published on: July 22, 2020
Identification of Novel Genes and Associated Drugs in Cervical Cancer by Bioinformatics Methods
Dan Wang1, Yanling Liu2, Shuyu Cheng1
1Institute of Gastrointestinal Oncology, Medical College of Xiamen University, Xiamen, Fujian, China (mainland).
Abstract:
BACKGROUND Cervical cancer is one of the common gynecological tumors that seriously harm women's health, so it is particularly important to accurately explore the underlying mechanism of its occurrence and clinical prognosis. MATERIAL AND METHODS In the GEO database, GEO2R was used to analyze the differentially expressed genes from the 4 databases: GSE6791, GSE9750, GSE63514, and GSE67522. Then, the DAVID website was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. These protein-protein interaction (PPI) networks of DEGS were visualized and analyzed using the STRING website and the hub genes were further screened using the Cytohubba plugin. Lastly, the functions of the hub genes were further analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) online tools, Human Protein Atlas (HPA) databases, and the QuartataWeb database. RESULTS In the 4 Profile datasets, 101 cancer tissues and 67 normal tissues were collected. Among the 78 differentially expressed genes in the 4 datasets, 51 genes were upregulated and 27 genes were downregulated. The PPIs of these differentially expressed genes were visualized using Cytoscape and the Interaction Gene Search Tool (STRING). Then, further analysis of hub genes using the GEPIA tool and Kaplan-Meier curves that showed upregulation of CDK1 and PRC1 is associated with better survival, while AURKA is associated with worse survival. Among these hub genes, only AURKA was closely related to the prognosis of cervical cancer, and 21 potential drugs were found. CONCLUSIONS These results suggest that AURKA and its drug candidates can improve the individualized diagnosis and treatment of cervical cancer in the future.
Insights
Identifying key genes in cervical cancer is crucial for understanding its development and prognosis. AURKA gene upregulation is linked to poorer survival, suggesting it as a potential therapeutic target for cervical cancer treatment.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Cervical cancer poses a significant threat to women's health.
- Understanding its underlying mechanisms and clinical prognosis is vital.
Purpose of the Study:
- To explore the molecular mechanisms and prognostic factors of cervical cancer.
- To identify potential therapeutic targets and drug candidates.
Main Methods:
- Differential gene expression analysis using GEO2R across four datasets.
- Gene Ontology (GO) and KEGG pathway analyses via DAVID.
- Protein-protein interaction (PPI) network construction and hub gene identification using STRING and Cytohubba.
- Prognostic analysis of hub genes using GEPIA, HPA, and QuartataWeb.
Main Results:
- Analysis of 101 cervical cancer tissues and 67 normal tissues identified 78 differentially expressed genes (51 upregulated, 27 downregulated).
- Hub gene analysis revealed AURKA is strongly associated with poor cervical cancer prognosis.
- CDK1 and PRC1 upregulation correlated with better survival, while AURKA upregulation correlated with worse survival.
Conclusions:
- AURKA is a significant prognostic biomarker for cervical cancer.
- The study identified 21 potential drug candidates targeting AURKA.
- These findings suggest AURKA and its drug candidates could enhance individualized diagnosis and treatment strategies for cervical cancer.
