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This study aimed to identify hub genes and drugs for ovarian cancer (OC) using bioinformatics analysis. Gene expression data and clinical information were downloaded from public databases. Differentially expressed genes (DEGs) were identified, and ovarian disease-related genes were extracted from DISEASES database. Disease-associated DEGs were further screened. Functional enrichment, protein–protein interaction (PPI) analysis, drug prediction, and survival analysis were performed. Key gene-drug interactions were also predicted. Finally, hub genes expression in an OC cell line (SKOV3) was verified using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). Totally 354 overlapping DEGs were identified. PPI network revealed genes such as  CCL5  and  CXCR4  with higher degrees, which were mainly involved in chemokine-related functions.  CCND1 ,  CCNE1 ,  CXCL10 ,  ERBB4 ,  LPAR3 , and  SST  were correlated with prognosis of OC patients. Among them,  CXCL10  was considered as an independent prognostic factor. Moreover, 63 possible drugs were predicted and the sirolimus- DCN  pair was screened via a literature search. RT-qPCR confirmed that the levels of  PDGFRB ,  DCN ,  PDGFRA ,  LYN , and  CENPE  were consistent with the bioinformatics analysis results. Our findings provide new insights into the underlying mechanisms of OC pathogenesis. The identified hub genes and drugs may improve individualized OC diagnosis and therapy.

Identification of potential hub genes and therapeutic drugs in ovarian cancer via bioinformatics analysis