MicroRNA‑138 modulates glioma cell growth, apoptosis and invasion through the suppression of the AKT/mTOR signalling pathway by targeting CREB1

The Kaohsiung J of Med Scie

Han

Lin

et al. 2022

Emerging evidence greatly implicates that microRNA-450a (miR-450a) plays an essential role in cancer pathobiology. While the pathological role of miR-450a in breast carcinogenesis remains enigmatic. Herein, we showed that miR-450a was lowly expressed in breast cancer cell lines compared with normal, and low miR-450a expression was associated with poor survival in patients with breast cancer. We revealed that miR-450a mimic transfected breast cancer cells (T47D and BT474) exhibited attenuated capacities of proliferation, migration, and invasion in vitro, and miR-450a suppressed T47D cell growth in a xenograft tumor model. Mechanistically, cAMP response element-binding protein 1 (CREB1) was negatively targeted by miR-450a, and CREB1 deletion mimicked the effects of miR-450a mimic treatment.Bioinformatics analysis further revealed that elevated expression of CREB1 correlated with poor prognosis in patients with breast cancer and miR-450a level was negatively correlated with CREB1 level in breast cancer. Additionally, miR-450a inhibited the phosphorylation of phosphatidylinositol 3-kinase/V-akt murine thymoma viral oncogene homolog (PI3K/AKT) and the activities of matrix metalloproteinase-2/9 (MMP-2/9). The following rescue assay indicated that CREB1 was implicated in the anti-tumoral effect of mR-450a in breast carcinoma. All these observations disclosed that miR-450a negatively regulates the growth and metastatic property of breast carcinoma cells.

Section: Resultsmentioning

confidence: 55%

miR‐450a exerts oncosuppressive effects in breast carcinoma by targeting CREB1

The Kaohsiung J of Med Scie

Han

Lin

et al. 2022

“…One potential target BTRC (F-box/WD repeat-containing protein 1A) is a hub for two miRNAs, namely miR-150 and miR-132. Curiously, CREB1 appeared to be not only the shared target among miR-22 64 , miR-138 65 , miR-141 66 , and miR-150 61 , but also acts as one of the most interacting genes targeted by miR-138 65 . In total, 11 out of 19 shared targets made no direct links, but the other eight targets could be directly combined into the hub-based network (Supplemental Fig.…”

Section: Resultsmentioning

confidence: 99%

High-content analysis of microRNAs involved in the phenotype regulation of vascular smooth muscle cells

Starkuviene

Erfle

et al. 2022

Sci Rep

In response to vascular injury vascular smooth muscle cells (VSMCs) alternate between a differentiated (contractile) and a dedifferentiated (synthetic) state or phenotype. Although parts of the signaling cascade regulating the phenotypic switch have been described, the role of miRNAs is still incompletely understood. To systematically address this issue, we have established a microscopy-based quantitative assay and identified 23 miRNAs that induced contractile phenotypes when over-expressed. These were then correlated to miRNAs identified from RNA-sequencing when comparing cells in the contractile and synthetic states. Using both approaches, six miRNAs (miR-132-3p, miR-138-5p, miR-141-3p, miR-145-5p, miR-150-5p, and miR-22-3p) were filtered as candidates that induce the phenotypic switch from synthetic to contractile. To identify potentially common regulatory mechanisms of these six miRNAs, their predicted targets were compared with five miRNAs sharing ZBTB20, ZNF704, and EIF4EBP2 as common potential targets and four miRNAs sharing 16 common potential targets. The interaction network consisting of these 19 targets and additional 18 hub targets were created to facilitate validation of miRNA-mRNA interactions by suggesting the most plausible pairs. Furthermore, the information on drug candidates was integrated into the network to predict novel combinatorial therapies that encompass the complexity of miRNAs-mediated regulation. This is the first study that combines a phenotypic screening approach with RNA sequencing and bioinformatics to systematically identify miRNA-mediated pathways and to detect potential drug candidates to positively influence the phenotypic switch of VSMCs.

“…One potential target BTRC (F-box/WD repeat-containing protein 1A) is a hub for two miRNAs, namely miR-150 and miR-132. Curiously, CREB1 appeared to be not only the shared target among miR-22 60 , miR-138 61 , miR-141 62 , and miR-150 57 , but also acts as one of the most interacting genes targeted by miR-138 61 . In total, 11 out of 19 shared targets made no direct links, but the other eight targets could be directly combined into the hub-based network (Supplemental Figure 3); for instance, MAP3K3 kinase by interaction to epidermal growth factor receptor EGFR.…”

Section: Mirna-mediated Regulatory Network Inducing the Contractile Phenotypementioning

confidence: 99%

High-Content Analysis of MicroRNAs Facilitates the Development of Combinatorial Therapies for Vascular Diseases

Starkuviene

Erfle

et al. 2021

Preprint

In response to vascular injury vascular smooth muscle cells (VSMCs) alternate between a differentiated (contractile) and a dedifferentiated (synthetic) state or phenotype. Although parts of the signaling cascade regulating the phenotypic switch have been described, little is known on the role of miRNAs involved. To systematically address this issue, we have established a microscopy-based quantitative assay and identified 23 miRNAs that induced contractile phenotypes when over-expressed. These were then correlated to miRNAs identified from RNA-sequencing when comparing cells in the contractile and synthetic states. Using both approaches, six miRNAs (miR-132-3p, miR-138-5p, miR-141-3p, miR-145-5p, miR-150-5p, and miR-22-3p) were filtered as candidates that induce the phenotypic switch from synthetic to contractile. To identify potentially common regulatory mechanisms of these six miRNAs, their predicted targets were compared with five miRNAs sharing ZBTB20, ZNF704, and EIF4EBP2 as common potential targets and four miRNAs sharing 16 common potential targets. The interaction network consisting of these 19 targets and additional 18 hub targets were created to facilitate validation of miRNA-mRNA interactions by suggesting the most plausible pairs. Furthermore, the information on drug candidates was integrated into the network to predict novel combinatorial therapies that encompass the complexity of miRNAs-mediated regulation. This is the first study that combines phenotypic screening approach with RNA sequencing and bioinformatics to systematically identify miRNAs-mediated pathways and to identify potential drug candidates to positively influence the phenotypic switch of VSMCs.