BackgroundMetastasis associated in lung adenocarcinoma transcript-1 (MALAT-1) is overexpressed during cancer progression and promotes cell migration and invasion in many solid tumors. However, its role in ovarian cancer remains poorly understood.MethodsExpressions of MALAT-1 were detected in 37 normal ovarian tissues and 45 ovarian cancer tissues by reverse transcription polymerase chain reaction (RT-PCR). Cell proliferation was observed by CCK-8 assay; Flow cytometry was used to measure cell cycle and apoptosis; Cell migration was detected by transwell migration and invasion assay. In order to evaluate the function of MALAT-1, shRNA combined with DNA microarray and Functional enrichment analysis were performed to determine the transcriptional effects of MALAT-1 silencing in OVCAR3 cells. RNA and protein expression were measured by qRT-PCR and Western blotting, respectively.ResultsWe found that upregulation of MALAT-1 mRNA in ovarian cancer tissues and enhanced MALAT-1 expression was associated with FIGO stage. Knockdown of MALAT-1 expression in OVCAR3 cells inhibited cell proliferation, migration, and invasion, leading to G0/G1 cell cycle arrest and apoptosis. Overexpressed MALAT-1 expression in SKOV3 cells promoted cell proliferation, migration and invasion. Downregulation of MALAT-1 resulted in significant change of gene expression (at least 2-fold) in 449 genes, which regulate proliferation, cell cycle, and adhesion. As a consequence of MALAT-1 knockdown, MMP13 protein expression decreased, while the expression of MMP19 and ADAMTS1 was increased.ConclusionsThe present study found that MALAT-1 is highly expressed in ovarian tumors. MALAT-1 promotes the growth and migration of ovarian cancer cells, suggesting that MALAT-1 may be an important contributor to ovarian cancer development.
NEAT1 is an important tumor oncogenic gene in various tumors. Nevertheless, its involvement remains poorly studied in cervical cancer. Our study explored the functional mechanism of NEAT1 in cervical cancer. NEAT1 level in several cervical cancer cells was quantified and we found NEAT1 was greatly upregulated in vitro. NEAT1 knockdown inhibited cervical cancer development through repressing cell proliferation, colony formation, capacity of migration, and invasion and also inducing the apoptosis. For another, microRNA (miR)‐133a was downregulated in cervical cancer cells and NEAT1 negatively modulated miR‐133a expression. Subsequently, we validated that miR‐133a functioned as a potential target of NEAT1. Meanwhile, SOX4 is abnormally expressed in various cancers. SOX4 was able to act as a downstream target of miR‐133a and silencing of SOX4 can restrain cervical cancer progression. In addition, in vivo assays were conducted to prove the role of NEAT1/miR‐133a/SOX4 axis in cervical cancer. These findings implied that NEAT1 served as a competing endogenous RNA to sponge miR‐133a and regulate SOX4 in cervical cancer pathogenesis. To sum up, it was implied that NEAT1/miR‐133a/SOX4 axis was involved in cervical cancer development.
Cervical cancer (CC) is one of the most common malignancies in females, with high prevalence and mortality globally. Despite advances in diagnosis and therapeutic strategies developed in recent years, CC is still a major health burden worldwide. The molecular mechanisms underlying the development of CC need to be understood. In this study, we aimed to demonstrate the role of lncRNA SNHG15 in CC progression. Using qRT-PCR, we determined that lncRNA SNHG15 is highly expressed in CC tumor tissues and cells. lncRNA SNHG15 knockdown also reduces the tumorigenic properties of CC in vitro, as determined using the MTT, EdU, flow cytometry, and transwell assays. Using bioinformatics analysis, RNA pull-down, ChIP, and luciferase reporter assays, we verified the molecular mechanisms of lncRNA SNHG15 in CC progression and found that lncRNA SNHG15 expression in CC cells is transcriptionally regulated by SOX12; moreover, lncRNA SNHG15 promotes CC progression via the miR-4735-3p/HIF1a axis. This study can provide a potential target for CC diagnosis or therapeutic strategies in the future.
This study aimed to propose a deep transfer learning framework for histopathological image analysis by using convolutional neural networks (CNNs) with visualization schemes, and to evaluate its usage for automated and interpretable diagnosis of cervical cancer. First, in order to examine
the potential of the transfer learning for classifying cervix histopathological images, we pre-trained three state-of-the-art CNN architectures on large-size natural image datasets and then fine-tuned them on small-size histopathological datasets. Second, we investigated the impact of three
learning strategies on classification accuracy. Third, we visualized both the multiple-layer convolutional kernels of CNNs and the regions of interest so as to increase the clinical interpretability of the networks. Our method was evaluated on a database of 4993 cervical histological images
(2503 benign and 2490 malignant). The experimental results demonstrated that our method achieved 95.88% sensitivity, 98.93% specificity, 97.42% accuracy, 94.81% Youden's index and 99.71% area under the receiver operating characteristic curve. Our method can reduce the cognitive burden on pathologists
for cervical disease classification and improve their diagnostic efficiency and accuracy. It may be potentially used in clinical routine for histopathological diagnosis of cervical cancer.
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