Background: Ovarian carcinoma is the leading cause of cancer death worldwide among gynecological malignancies, and the majority of cases are related with recurrence and chemoresistance. Cancer stem cells (CSCs) are believed to be one of the causes of recurrent or chemoresistant ovarian cancer, and microRNAs are regulatory molecules newly implicated to control a variety of cellular processes, including CSCs. Therefore, we identified ovarian CSC-specific microRNAs and investigated their clinicopathological implication in ovarian carcinomas. Methods: We isolated ALDH1 (+) cell population using the Aldefluor assay, and examined the differential expression pattern of miRNAs between ALDH1 (+) and ALDH1 (−) cells using a high-throughput microRNA microarray. We further investigated the expression patterns of differentially expressed miRNAs in human ovarian cancer samples using the real-time reverse transcription-polymerase chain reaction and analyzed their clinical impact in patients with ovarian cancer. Results: We found that high ALDH1 expression was associated with chemoresistance in in vitro and ex vivo samples (p = 0.024). We identified six miRNAs, including miR-23b, miR-27a, miR-27b, miR-346, miR-424, and miR-503, overexpressed in ALDH1 (+) cells, and they were significantly upregulated in chemoresistant ovarian cancer cells (1.4 ~ 3.5-fold) and tumor samples (2.8 ~ 5.5-fold) compared with chemosensitive group. Upregulation of ALDH1 (p = 0.019) and miR-503 (p = 0.033) correlated with high clinical stage, and upregulation of miR-27a was related with distant metastasis (p = 0.046) in patients with ovarian cancer. Conclusion: Our findings indicate that ALDH1 is a useful marker for enriching ovarian CSCs, and high expression of ALDH1 and its related miRNAs, particularly miR-23b, miR-27b, miR-424, and miR-503, are significantly implicated in chemoresistance and tumor progression in ovarian cancer.

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.

Bae HS, et al. J Korean Med Sci. 2014 Jun;29(6):793-797. https://doi.org/10.3346/jkms.2014.29.6.793