Abstract
Background: Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of OC patients remain largely unclear. The present study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients’ overall survival (OS).Results: We identified 164 up-regulated and 80 down-regulated differentially expressed genes (DEGs) associated with OC. Gene Ontology (GO) term enrichment showed DEGs mainly correlated with spindle microtubes. For Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cell cycle was mostly enriched for the DEGs. The protein–protein interaction (PPI) network yielded 238 nodes and 1284 edges. Top three modules and ten hub genes were further filtered and analyzed. Three candidiate drugs targeting for therapy were also selected. Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival.Conclusions: The identified DEGs and biological pathways may provide new perspective on the pathogenesis and treatments of OC. The identified eight-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients.
Highlights
Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide
We explored the biological functions of overlapped differentially expressed gene (DEG) and hub modules from our protein–protein interaction (PPI) network using Enrichr website
The top 100 significantly up- and down-regulated genes from each microarray dataset were displayed in the heatmaps (Figure 1A–F) and the distribution of all gene expression was presented in volcano plots (Figure 2A–F)
Summary
Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of OC patients remain largely unclear. The present study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients’ overall survival (OS). Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival. The identified eight-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients. Ovarian cancer (OC) is the most lethal malignant disease in the female reproductive system, with over 200000 new cases and 150000 deaths each year worldwide [1]. It is imperative to explore the molecular mechanisms of malignant biological behavior of OC cells and to develop more reliable molecular markers for predicting recurrence and evaluating prognosis, further guiding clinicians for therapy
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