MiR-135a-5p suppresses breast cancer cell proliferation, migration, and invasion by regulating BAG3

Background: MicroRNAs (miRNAs) are involved in the progression of diverse human cancers. This work aimed to delve into how microRNA-135a-5p (miR-135a-5p) affects the biological behaviors of Breast Cancer (BC) cells. Methods: Gene Expression Omnibus (GEO) datasets were used to analyze the expression differences of miR-135a-5p in cancer tissues of BC patients. Quantitative real-time PCR and western blot were conducted to detect miR-135a-5p and Bcl-2 Associated Athanogene (BAG3) expression levels in BC tissues and cells, respectively. The proliferation, migration, invasion, and cell cycle of BC cells were detected by cell counting kit-8 assay, BrdU assay, wound healing assay, transwell assay, and flow cytometry. The targeted relationship between miR-135a-5p and BAG3 mRNA 3′UTR predicted by bioinformatics was further testified by a dual-luciferase reporter gene assay. Pearson's correlation analysis was adopted to analyze the correlation between miR-135a-5p expression and BAG3 expression. The downstream pathways of BAG3 were analyzed by the LinkedOmics database. Results: MiR-135a-5p was significantly down-regulated and BAG3 expression was significantly raised in BC tissues. MiR-135a-5p overexpression repressed the viability, migration and invasion of BC cells, and blocked cell cycle progression in G0/G1 phase while inhibiting miR-135a-5p worked oppositely. BAG3 was verified as a target of miR-135a-5p. Overexpression of BAG3 reversed the impacts of miR-135a-5p on the malignant biological behaviors of BC cells. The high expression of BAG3 was associated with the activation of the cell cycle, mTOR and TGF-β signaling pathways. Conclusion: MiR-135a-5p regulates BAG3 to repress the growth, migration, invasion, and cell cycle progression of BC cells

G-protein-coupled estrogen receptor agonist G-1 inhibits the proliferation of breast cancer cells through induction of apoptosis and cycle arrest

Purpose: To determine the effect of G-1, a G-protein-linked estrogen receptor (GPER) agonist on apoptosis, cell cycle, and proliferative potential of mammary tumor cells, and the associated mechanisms of action. Methods: Three groups of human breast cancer cell line MDA-MB-231 were used: control group, estradiol (E2) group and G-1 group. Control group was not treated. The effects of treatment (10 M G1) on cell proliferation were determined and compared amongst the groups. Cell cycle distribution and apoptosis were determined while expression levels of proteins related to pi3k/AKT/MAPK were assessed by western blotting. Results: Apoptosis was significantly reduced in E2 group relative to control, but was enhanced in G-1 group, when compared to the other 2 groups (p < 0.05). There were marked down-regulations in protein levels of cylinb1, p21, caspase 6, p53, p-ERK in E2 group, relative to the corresponding expression levels in the control group. Conclusion: GPER agonist G-1 suppresses the proliferation of mammary tumor cells and induces apoptotic changes and cycle blockage in the cells via inhibition of pi3k/AKT pathway and activation of MAPKs pathway. Thus, GPER is a potential target in breast tumor treatment, and G-1 is a potential new anti-tumor drug

Quality Prediction of DWT-Based Compression for Remote Sensing Image Using Multiscale and Multilevel Differences Assessment Metric

Accurate assessment and prediction of visual quality are of fundamental importance to lossy compression of remote sensing image, since it is not only a basic indicator of coding performance, but also an important guide to optimize the coding procedure. In the paper, a novel quality prediction model based on multiscale and multilevel distortion (MSMLD) assessment metric is preferred for DWT-based coding of remote sensing image. Firstly, we propose an image quality assessment metric named MSMLD, which assesses quality by calculating distortions in three levels and multiscale sampling between original images and compressed images. The MSMLD method not only has a better consistency with subjective perception values, but also shows the distortion features and visual quality of compressed image well. Secondly, some significant characteristics in spatial and wavelet domain that link well with quality criteria of MSMLD are chosen with multiple linear regression and used to establish a compression quality prediction model of MSMLD. Finally, the quality prediction model is extended to a wider range of compression ratios from 4 : 1 to 20 : 1 and tested with experiment. The experimental results show that the prediction accuracy of the proposed model is up to 98.33%, and its mean prediction error is less than state-of-the-art methods