Novel gene signatures for prognosis prediction in ovarian cancer.

Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi-gene signature based on the LASSO Cox regression model in the TCGA database and time-dependent ROC curves showed good predictive accuracy for 1-, 3- and 5-year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi-gene signature stratified patients into high-risk and low-risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi-gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug-gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.