Identification of novel candidate genes and small molecule drugs in ovarian cancer by bioinformatics strategy

Background: Ovarian cancer (OC) is the most lethal type of malignancies in the female reproductive system. This study aimed to identify novel biomarkers and potential small molecule drugs in OC by integrating two expression profile datasets. Methods: GSE18520 and GSE14407 from the Gene Expression Omnibus (GEO) database were selected and the overlapped differentially expressed genes (DEGs) were detected. The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis were performed to establish the protein-protein interaction (PPI) network of DEGs and identified the hub genes. Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine database and The Human Protein Atlas (HPA) were used to validate the expression of the identified hub genes. The prognostic value of these hub genes were evaluated by the Kaplan Meier plotter online tool. The expression of NCAPG was further explored by immunohistochemistry in our OC tissues. Moreover, CMap database was used to look for prospective small compounds with therapeutic efficacy based on OC RNA-seq. Results: A total of 433 DEGs were identified. The DEGs were mainly enriched in negative regulation of transcription and pathways in cancer. A PPI network was constructed with 344 nodes and 1,596 interactions. The top ten module genes were chosen as hub genes. Among which, survival analysis showed that patients with high expression of CCNB1, TOP2A, NUSAP1, NCAPG, KIF20A and DLGAP5 had poorer survival results than those with low expression. These six genes were all overexpressed in OC tissue by means of bioinformatics analysis. In our clinical patients, the expression rate of NCAPG in OC tissues was significantly higher than that in benign serous ovarian cystadenoma and borderline serous ovarian cystadenoma tissues. Meanwhile, several small molecules with potential therapeutic efficacy against OC were identified in our study. Conclusions: By means of bioinformatics analysis, we identified six real hub genes and indicated a group of candidate small molecule drugs as adjunctive agents for OC. They could be the potential novel biomarkers for the diagnosis and promising therapeutic targets of OC.

Automated summary

This study identified novel biomarkers and potential small molecule drugs in ovarian cancer (OC) through analysis of gene expression datasets. Six hub genes with potential prognostic value and high expression rates in OC tissues were identified. These findings have implications for the diagnosis and treatment of OC.