Regulation of cell–cell adhesion in prostate cancer cells by microRNA-96 through upregulation of E-Cadherin and EpCAM

Voss, Gjendine; Haflidadóttir, Benedikta S.; Järemo, Helena; Persson, Margareta; Ivković, Tina Catela; Wikström, Pernilla; Ceder, Yvonne

doi:10.1093/carcin/bgz191

Cited by 25 publications

(32 citation statements)

References 51 publications

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“…This includes reduced cell-cell adhesion, lower levels of the adhesion molecules E-cadherin and β-catenin, and a greater degree of migratory capabilities as measured by the wound healing assay [37]. Through the AGO2-immunoprecipitation assay, it was discovered that the adhesion molecules, E-Cadherin and EpCAM, were upregulated by miR-96 in the prostate cancer bone metastasis samples [38]. This gives the tumor an advantage during the late stages of metastasis in the Mesenchymal and Epithelial Transition (MET) [38].…”

Section: Discussionmentioning

confidence: 99%

“…Through the AGO2-immunoprecipitation assay, it was discovered that the adhesion molecules, E-Cadherin and EpCAM, were upregulated by miR-96 in the prostate cancer bone metastasis samples [38]. This gives the tumor an advantage during the late stages of metastasis in the Mesenchymal and Epithelial Transition (MET) [38]. Future work should explore the correlation of these adhesion molecules and AGO2 expression level in brain cancer.…”

Section: Discussionmentioning

confidence: 99%

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Effect of AGO2 Depletion on Cell Migration of A172 Brain Cancer Cell

Kim¹,

Kim²,

Kim³

et al. 2020

CRJ

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Effect of AGO2 Depletion on Cell Migration of A172 Brain Cancer Cell

Kim¹,

Kim²,

Kim³

et al. 2020

CRJ

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“…Junctional adhesion molecule A (JAM-A) is a cell–cell adhesion protein and a key regulator of cell migration and invasion [ 198 ]. JAM-A has been identified as a direct target of miR-145, which finds its expression downregulated in cancer [ 66 ].…”

Section: Tumor Cell Migration and Local Invasionmentioning

confidence: 99%

“…Conversely, miR-96 upregulates E-cadherin expression by direct binding, which leads to the enhanced cell-to-cell adhesion [197]. -A) is a cell-cell adhesion protein and a key regulator of cell migration and invasion [198]. JAM-A has been identified as a direct target of miR-145, which finds its expression downregulated in cancer [66].…”

Section: Cadherinsmentioning

confidence: 99%

Regulators at Every Step—How microRNAs Drive Tumor Cell Invasiveness and Metastasis

Grzywa

Klicka

Włodarski

2020

Cancers

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Tumor cell invasiveness and metastasis are the main causes of mortality in cancer. Tumor progression is composed of many steps, including primary tumor growth, local invasion, intravasation, survival in the circulation, pre-metastatic niche formation, and metastasis. All these steps are strictly controlled by microRNAs (miRNAs), small non-coding RNA that regulate gene expression at the post-transcriptional level. miRNAs can act as oncomiRs that promote tumor cell invasion and metastasis or as tumor suppressor miRNAs that inhibit tumor progression. These miRNAs regulate the actin cytoskeleton, the expression of extracellular matrix (ECM) receptors including integrins and ECM-remodeling enzymes comprising matrix metalloproteinases (MMPs), and regulate epithelial–mesenchymal transition (EMT), hence modulating cell migration and invasiveness. Moreover, miRNAs regulate angiogenesis, the formation of a pre-metastatic niche, and metastasis. Thus, miRNAs are biomarkers of metastases as well as promising targets of therapy. In this review, we comprehensively describe the role of various miRNAs in tumor cell migration, invasion, and metastasis.

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“…Firstly, we choose prostate neoplasms for this case, as this is the most common cancer in men in the world. There are more than 100,000 men that die from prostate neoplasms in Europe alone in 2018 [43]. In this case study, we first set all miRNA-disease associations related to prostate neoplasms from HMDD 2.0 to zero.…”

Section: Cases Studiesmentioning

confidence: 99%

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

et al. 2020

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Background Many studies prove that miRNAs have significant roles in diagnosing and treating complex human diseases. However, conventional biological experiments are too costly and time-consuming to identify unconfirmed miRNA-disease associations. Thus, computational models predicting unidentified miRNA-disease pairs in an efficient way are becoming promising research topics. Although existing methods have performed well to reveal unidentified miRNA-disease associations, more work is still needed to improve prediction performance. Results In this work, we present a novel multiple meta-paths fusion graph embedding model to predict unidentified miRNA-disease associations (M2GMDA). Our method takes full advantage of the complex structure and rich semantic information of miRNA-disease interactions in a self-learning way. First, a miRNA-disease heterogeneous network was derived from verified miRNA-disease pairs, miRNA similarity and disease similarity. All meta-path instances connecting miRNAs with diseases were extracted to describe intrinsic information about miRNA-disease interactions. Then, we developed a graph embedding model to predict miRNA-disease associations. The model is composed of linear transformations of miRNAs and diseases, the means encoder of a single meta-path instance, the attention-aware encoder of meta-path type and attention-aware multiple meta-path fusion. We innovatively integrated meta-path instances, meta-path based neighbours, intermediate nodes in meta-paths and more information to strengthen the prediction in our model. In particular, distinct contributions of different meta-path instances and meta-path types were combined with attention mechanisms. The data sets and source code that support the findings of this study are available at https://github.com/dangdangzhang/M2GMDA. Conclusions M2GMDA achieved AUCs of 0.9323 and 0.9182 in global leave-one-out cross validation and fivefold cross validation with HDMM V2.0. The results showed that our method outperforms other prediction methods. Three kinds of case studies with lung neoplasms, breast neoplasms, prostate neoplasms, pancreatic neoplasms, lymphoma and colorectal neoplasms demonstrated that 47, 50, 49, 48, 50 and 50 out of the top 50 candidate miRNAs predicted by M2GMDA were validated by biological experiments. Therefore, it further confirms the prediction performance of our method.

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Regulation of cell–cell adhesion in prostate cancer cells by microRNA-96 through upregulation of E-Cadherin and EpCAM

Cited by 25 publications

References 51 publications

Effect of AGO2 Depletion on Cell Migration of A172 Brain Cancer Cell

Effect of AGO2 Depletion on Cell Migration of A172 Brain Cancer Cell

Regulators at Every Step—How microRNAs Drive Tumor Cell Invasiveness and Metastasis

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

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