Identification of long noncoding RNAs downregulated specifically in ovarian high-grade serous carcinoma.

Purpose: To investigate whether long noncoding RNAs (lncRNAs) are involved in the development or malignant behavior of ovarian high-grade serous carcinoma (HGSC), we attempted to identify lncRNAs specific to HGSC. Methods: Total RNAs were isolated from HGSC, normal ovarian, and fallopian tube tissue samples and were subjected to a PCR array that can analyze 84 cancer-associated lncRNAs. The lncRNAs that were upregulated and downregulated in HGSC in comparison to multiple samples of normal ovary and fallopian tube were validated by real-time RT-PCR. To infer the function, ovarian cancer cell lines that overexpress the identified lncRNAs were established, and the activation of cell proliferation, migration, and invasion was analyzed. Results: Eleven lncRNAs (ACTA2-AS1, ADAMTS9-AS2, CBR3-AS1, HAND2-AS1, IPW, LINC00312, LINC00887, MEG3, NBR2, TSIX, and XIST) were downregulated in HGSC samples. We established the cell lines that overexpress ADAMTS9-AS2, CBR3-AS1, or NBR2. In cell lines overexpressing ADAMTS9-AS2, cell proliferation was suppressed, but migration and invasion were promoted. In cell lines overexpressing CBR3-AS1 or NBR2, cell migration tended to be promoted, although cell proliferation and invasion were unchanged. Conclusion: We identified eleven lncRNAs that were specifically downregulated in HGSC. Of these, CBR3-AS1, NBR2, and ADAMTS9-AS2 had unique functions in the malignant behaviors of HGSC.

Establishment of Noninvasive Prediction Models for the Diagnosis of Uterine Leiomyoma Subtypes.

OBJECTIVE: To establish prediction models for the diagnosis of the subtypes of uterine leiomyomas by machine learning using magnetic resonance imaging (MRI) data. METHODS: This is a prospective observational study. Ninety uterine leiomyoma samples were obtained from 51 patients who underwent surgery for uterine leiomyomas. Seventy-one samples (49 mediator complex subunit 12 [ MED12 ] mutation-positive and 22 MED12 mutation-negative leiomyomas) were assigned to the primary data set to establish prediction models. Nineteen samples (13 MED12 mutation-positive and 6 MED12 mutation-negative leiomyomas) were assigned to the unknown testing data set to validate the prediction model utility. The tumor signal intensity was quantified by seven MRI sequences (T2-weighted imaging, apparent diffusion coefficient, magnetic resonance elastography, T1 mapping, magnetization transfer contrast, T2* blood oxygenation level dependent, and arterial spin labeling) that can estimate the collagen and water contents of uterine leiomyomas. After surgery, the MED12 mutations were genotyped. These results were used to establish prediction models based on machine learning by applying support vector classification and logistic regression for the diagnosis of uterine leiomyoma subtypes. The performance of the prediction models was evaluated by cross-validation within the primary data set and then finally evaluated by external validation using the unknown testing data set. RESULTS: The signal intensities of five MRI sequences (T2-weighted imaging, apparent diffusion coefficient, T1 mapping, magnetization transfer contrast, and T2* blood oxygenation level dependent) differed significantly between the subtypes. In cross-validation within the primary data set, both machine learning models (support vector classification and logistic regression) based on the five MRI sequences were highly predictive of the subtypes (area under the curve [AUC] 0.974 and 0.988, respectively). External validation with the unknown testing data set confirmed that both models were able to predict the subtypes for all samples (AUC 1.000, 100.0% accuracy). Our prediction models with T2-weighted imaging alone also showed high accuracy to discriminate the uterine leiomyoma subtypes. CONCLUSION: We established noninvasive prediction models for the diagnosis of the subtypes of uterine leiomyomas by machine learning using MRI data.

Different DNA methylome, transcriptome and histological features in uterine fibroids with and without MED12 mutations.

Somatic mutations in Mediator complex subunit 12 (MED12m) have been reported as a biomarker of uterine fibroids (UFs). However, the role of MED12m is still unclear in the pathogenesis of UFs. Therefore, we investigated the differences in DNA methylome, transcriptome, and histological features between MED12m-positive and -negative UFs. DNA methylomes and transcriptomes were obtained from MED12m-positive and -negative UFs and myometrium, and hierarchically clustered. Differentially expressed genes in comparison with the myometrium and co-expressed genes detected by weighted gene co-expression network analysis were subjected to gene ontology enrichment analyses. The amounts of collagen fibers and the number of blood vessels and smooth muscle cells were histologically evaluated. Hierarchical clustering based on DNA methylation clearly separated the myometrium, MED12m-positive, and MED12m-negative UFs. MED12m-positive UFs had the increased activities of extracellular matrix formation, whereas MED12m-negative UFs had the increased angiogenic activities and smooth muscle cell proliferation. The MED12m-positive and -negative UFs had different DNA methylation, gene expression, and histological features. The MED12m-positive UFs form the tumor with a rich extracellular matrix and poor blood vessels and smooth muscle cells compared to the MED12m-negative UFs, suggesting MED12 mutations affect the tissue composition of UFs.

Identification of aberrantly expressed long non-coding RNAs in ovarian high-grade serous carcinoma cells.

PURPOSE: To identify the aberrantly expressed long non-coding RNAs (lncRNAs) in ovarian high-grade serous carcinoma (HGSC). METHODS: Total RNA was isolated in HGSC cell lines, ovarian surface epithelial cells, and normal ovaries. Aberrantly expressed lncRNAs in HGSC were identified by PCR array, which analyzes 84 kinds of lncRNAs. To infer their functions, HGSC cell lines with different levels of expression of the identified lncRNAs were established, and then, activities of proliferation, migration, and apoptosis were examined. Expression levels of the identified lncRNAs were also examined in multiple ovarian HGSC tissues. RESULTS: Ten aberrantly expressed lncRNAs, six upregulated and four downregulated, were identified in the HGSC cell lines. The authors established four HGSC cell lines: in two of the cell lines, one of the upregulated lncRNAs was knocked down, and in two other cell lines, one of the downregulated lncRNAs (MEG3 and POU5F1P5) was overexpressed. Migration activities were inhibited in the HGSC cell lines overexpressing MEG3 or POU5F1P5 while there were no differences in proliferation and apoptosis between the established and control cell lines. The four lncRNAs downregulated in the HGSC cell lines were also observed to be downregulated in ovarian HGSC tissues. CONCLUSION: The authors identified four downregulated lncRNAs in ovarian HGSC.