miR‑338‑3p, a recently discovered miRNA, has been shown to play important roles in tumorigenesis and metastasis in various cancers. However, the exact roles and mechanisms of miR‑338‑3p remain unknown in human ovarian epithelial carcinoma (EOC). The relationship between miR‑338‑3p expression pattern and clinicopathological features of patients with EOC were determined by real-time quantitative RT-PCR. Furthermore, the role of miR‑338‑3p and possible molecular mechanisms in EOC was investigated by several in vitro approaches and in a nude mouse model. We first showed that the expression of miR‑338‑3p was significantly downregulated in EOC tissues compared to those in adjacent normal tissues, and the value was negatively related to advanced FIGO stage, high histological grading and lymph node metastasis (P<0.01). An in vitro analysis revealed that the overexpression of miR‑338‑3p in EOC cells significantly inhibited cell proliferation, colony formation, migration and invasion, inducing cell apoptosis and enhancing caspase-3, -8, and -9 activities. Bioinformatic analysis and dual luciferase assays identified Runx2 as a direct target of miR‑338‑3p. We also found that enforced expression of miR‑338‑3p markedly inhibited the in vivo tumorigenicity in a nude mouse xenograft model system. Furthermore, overexpression of miR‑338‑3p inhibited phosphorylation of PI3K and AKT, which contributed to suppression of ovarian cancer cell growth. These findings revealed that miR‑338‑3p may act as a tumor suppressor that blocks the growth of human ovarian epithelial carcinoma through PI3K/AKT signaling pathways by targeting Runx2.
Objective We aimed to identify key susceptibility gene targets in multiple datasets generated from postmortem brains and blood of Parkinson’s disease (PD) patients and healthy controls (HC). Methods We performed a multitiered analysis to integrate the gene expression data using multiple-gene chips from 244 human postmortem tissues. We identified hub node genes in the highly PD-related consensus module by constructing protein–protein interaction (PPI) networks. Next, we validated the top four interacting genes in 238 subjects (90 sporadic PD, 125 HC and 23 Parkinson’s Plus Syndrome (PPS)). Utilizing multinomial logistic regression analysis (MLRA) and receiver operating characteristic (ROC), we analyzed the risk factors and diagnostic power for discriminating PD from HC and PPS. Results We identified 1333 genes that were significantly different between PD and HCs based on seven microarray datasets. The identified MEturquoise module is related to synaptic vesicle trafficking (SVT) dysfunction in PD (P < 0.05), and PPI analysis revealed that SVT genes PPP2CA, SYNJ1, NSF and PPP3CB were the top four hub node genes in MEturquoise (P < 0.001). The levels of these four genes in PD postmortem brains were lower than those in HC brains. We found lower blood levels of PPP2CA, SYNJ1 and NSF in PD compared with HC, and lower SYNJ1 in PD compared with PPS (P < 0.05). SYNJ1, negatively correlated to PD severity, displayed an excellent power to discriminating PD from HC and PPS. Conclusions This study highlights that SVT genes, especially SYNJ1, may be promising markers in discriminating PD from HCs and PPS.
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