Abstract
The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.
Highlights
Ovarian cancer (OC) represents the most lethal gynaecological malignancy and the fifth leading cause of death in women, with a 5-year survival rate around 10% [1]
We employed a multistep bioinformatic strategy that uses omics information and clinical data to build a gene expression prognostic scoring system in High-grade serous ovarian carcinoma (HGSOC). We previously developed this approach to identify and successfully validate a 53-gene signature associated with overall survival (OS) of gastric cancer [11] and a 27-gene signature for lung adenocarcinoma [12]
In comparison with an existing 5-gene expression signature for ovarian serous cystadenocarcinoma (CAC) [15], we showed that our signature was superior in determining overall survival for this type of epithelial ovarian carcinoma
Summary
Ovarian cancer (OC) represents the most lethal gynaecological malignancy and the fifth leading cause of death in women, with a 5-year survival rate around 10% [1]. A number of groups have sought to use genomewide gene expression data to identify multigene signatures aimed at predicting clinical outcomes, therapy responses, and subtypes in OC [13,14,15,16,17,18]. We employed a multistep bioinformatic strategy that uses omics information and clinical data to build a gene expression prognostic scoring system in HGSOC. We previously developed this approach to identify and successfully validate a 53-gene signature associated with OS of gastric cancer [11] and a 27-gene signature for lung adenocarcinoma [12]. In comparison with an existing 5-gene expression signature for ovarian serous cystadenocarcinoma (CAC) [15], we showed that our signature was superior in determining overall survival for this type of epithelial ovarian carcinoma
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