USP19 and RPL23 as candidate prognostic markers in advanced-stage high-grade serous ovarian carcinoma

Ovarian cancer is one of the leading causes of death among patients with gynecological malignancies worldwide. 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. To predict the prognosis of HGSC patients, we created machine learning-based models and identified RPL23 and USP19 as candidate prognostic markers. Specifically, patients with higher RPL23 mRNA levels had a worse prognosis and patients with higher USP19 mRNA levels had a better prognosis. This model was then used to analyze HGSC patient data hosted on The Cancer Genome Atlas; this exercise validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of RPL23 and USP19 determined using a machine-learning model could serve as prognostic markers in HGSC patients receiving conventional therapy. The mRNA expression of 51 primary high-grade serous ovarian cancer were measured by RNA sequencing.