USP19 and RPL23 as Candidate Prognostic Markers for Advanced-Stage High-Grade Serous Ovarian Carcinoma

SIMPLE SUMMARY: Although ovarian cancer is one of the leading causes of deaths among female patients with cancer, some patients live longer than others. In order to predict the outcome of patients with ovarian cancer, we investigated the expression levels of all human genes in 51 patients with ovarian cancer and constructed a prediction model based on artificial intelligence. The model identified two genes—USP19 and RPL23—as the most important genes for this prediction. Cancer recurrence occurred more frequently in the patients with lower USP19 mRNA levels and those with higher RPL23 mRNA levels. The same pattern was observed in 208 independent patients with ovarian cancer listed in The Cancer Genome Atlas. Therefore, we suggest USP19 and RPL23 as candidate biomarkers for predicting the survival of patients with ovarian cancer. ABSTRACT: Ovarian cancer is one of the leading causes of deaths among patients with gynecological malignancies worldwide. In order to identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. In order to predict the prognosis of patients with HGSC, we created machine learning-based models and identified USP19 and RPL23 as candidate prognostic markers. Specifically, patients with lower USP19 mRNA levels and those with higher RPL23 mRNA levels had worse prognoses. This model was then used to analyze the data of patients with HGSC hosted on The Cancer Genome Atlas; this analysis validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of USP19 and RPL23 determined using a machine-learning model could serve as prognostic markers for patients with HGSC receiving conventional therapy.