A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

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.

Multigene Expression Biomarkers and Score Systems for Predicting Therapeutic Benefit in Gastrointestinal Cancers

“…In addition to the gastrointestinal cancers, we have also successfully established a 27-gene panel for lung adenocarcinoma (22), and a 11-gene panel for ovarian cancer (23). In such cases, we first demonstrated that these expression-based signatures were able to better predict prognosis in comparison with other already published multigene signatures using the same datasets, as described in detail below, which clearly indicate that our strategy has its own advantage.…”

Expression of cyclin-dependent kinases and their clinical significance with immune infiltrates could predict prognosis in colorectal cancer

“…Bioinformatics analyses have provided useful results based on large experimentally validated data accesible in various databases. Gene expression prognostic signature for high-grade serous ovarian cancer was investigated by resampling/cross-validation method with Cox regression analysis followed by Kaplan–Meier analysis and log-rank testing [ 62 ]. Similarly, He et al [ 63 ] probed the survival analysis of the expression level of MANF in HCC using the Kaplan–Meier method.…”

Identification of a novel 15‐gene expression signature predicting overall survival of human colorectal cancer

“…In this study, we developed a clinically applicable and robust prognostic signature for CRC patients using our multistep bioinformatics analysis strategy. [5][6][7][8] For our gene expression signature development, we first identified 738 genes (971 gene probe IDs) that were consistently deregulated in CRC versus normal colon tissue in six transcriptome datasets ( Figure 1A and B, Figure S1A, Table S1). Kaplan-Meier survival analysis together with Cox regression was used to evaluate whether their expression levels were associated with overall survival (OS) in GSE17536 dataset.…”

“…However, there is still an urgent need to develop better and more robust gene signatures in this area. In this study, we developed a clinically applicable and robust prognostic signature for CRC patients using our multistep bioinformatics analysis strategy 5–8 …”