Genome-Wide Identification and Validation of Gene Expression Biomarkers in the Diagnosis of Ovarian Serous Cystadenocarcinoma

SIMPLE SUMMARY: Despite ovarian serous adenocarcinoma (OSCA) is a high-incidence type of cancer, limited molecular screening methods are available and the diagnosis mostly occurs at a late stage. The aim of this study is screening the potential of gene expression for identifying OSCA-specific molecular biomarkers for improving diagnosis. A genome-wide survey was performed on high-throughput RNA-sequencing experiments on hundreds ovarian cancer samples and healthy ovarian tissues, providing a number of putative OSCA biomarkers, which were then validated on an independent sample set and using a different RNA-quantification technology. Combinations of gene expression biomarkers were identified, which showed high accuracy in discriminating OSCA tissues from the normal counterpart and from other tumor types. The detected biomarkers can improve the molecular diagnosis of OSCA on tissue samples and are, in principle, translatable to the analysis of liquid biopsies. ABSTRACT: Ovarian cancer is the second most prevalent gynecologic malignancy, and ovarian serous cystadenocarcinoma (OSCA) is the most common and lethal subtype of ovarian cancer. Current screening methods have strong limits on early detection, and the majority of OSCA patients relapse. In this work, we developed and cross-validated a method for detecting gene expression biomarkers able to discriminate OSCA tissues from healthy ovarian tissues and other cancer types with high accuracy. A preliminary ranking-based approach was applied, resulting in a panel of 41 over-expressed genes in OSCA. The RNA quantity gene expression of the 41 selected genes was then cross-validated by using NanoString nCounter technology. Moreover, we showed that the RNA quantity of eight genes (ADGRG1, EPCAM, ESRP1, MAL2, MYH14, PRSS8, ST14 and WFDC2) discriminates each OSCA sample from each healthy sample in our data set with sensitivity of 100% and specificity of 100%. For the other three genes (MUC16, PAX8 and SOX17) in combination, their RNA quantity may distinguish OSCA from other 29 tumor types.