MicroRNA-561-3p indirectly regulates the PD-L1 expression by targeting ZEB1, HIF1A, and MYC genes in breast cancer.

Globally, breast cancer is the second most common cause of cancer-related deaths among women. In breast cancer, microRNAs (miRNAs) are essential for both the initiation and development of tumors. It has been suggested that the tumor suppressor microRNA-561-3p (miR-561-3p) is crucial in arresting the growth of cancer cells. Further research is necessary to fully understand the role and molecular mechanism of miR-561 in human BC. The aim of this study was to investigate the inhibitory effect of miR-561-3p on ZEB1, HIF1A, and MYC expression as oncogenes that have the most impact on PD-L1 overexpression and cellular processes such as proliferation, apoptosis, and cell cycle in breast cancer (BC) cell lines. The expression of ZEB1, HIF1A, and MYC genes and miR-561-3p were measured in BC clinical samples and cell lines via qRT-PCR. The luciferase assay, MTT, Annexin-PI staining, and cell cycle experiments were used to assess the effect of miR-561-3p on candidate gene expression, proliferation, apoptosis, and cell cycle progression. Flow cytometry was used to investigate the effects of miR-561 on PD-L1 suppression in the BC cell line. The luciferase assay showed that miRNA-561-3p targets the 3'-UTRs of ZEB1, HIF1A and MYC genes significantly. In BC tissues, the qRT-PCR results demonstrated that miR-561-3p expression was downregulated and the expression of ZEB1, HIF1A and MYC genes was up-regulated. It was shown that overexpression of miR-561-3p decreased PD-L1 expression and BC cell proliferation, and induced apoptosis and cell cycle arrest through downregulation of candidate oncogenes. Furthermore, inhibition of candidate genes by miR-561-3p reduced PD-L1 at both mRNA and protein levels. Our research investigated the impact of miR-561-3p on the expression of ZEB1, HIF1A and MYC in breast cancer cells for the first time. Our findings may help clarify the role of miR-561-3p in PD-L1 regulation and point to this miR as a potential biomarker and novel therapeutic target for cancer immunotherapy.

Evaluating the effects of vegetation and land management on runoff control using field plots and machine learning models.

Excess surface water after heavy rainfalls leads to soil erosion and flash floods, resulting in human and financial losses. Reducing runoff is an essential management tool to protect water and soil resources. This study aimed to evaluate the effects of vegetation and land management methods on runoff control and to provide a model to predict runoff values. Filed plot data and three machine learning (ML) methods, including artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIS), and extreme gradient boosting (EGB), were used in a test site in the north of Iran. In this regard, plots with various vegetation and land management treatments including bare soil treatment, rangeland cover treatment, forest litter treatment, rangeland litter treatment, tillage treatment in the direction of slope, tillage treatment perpendicular to the slope, and repetition of treatments under forest canopy were constructed on a hillslope. After each rainfall event, the amount of rainfall and corresponding runoff generated in each plot was recorded. Three ML models (ANN, CANFIS, and EGB) were used to establish relationships between amounts of recorded runoff and its controlling factors (rainfall, antecedent soil moisture (A.M.C), shrub canopy percentage and height, tree canopy percentage and height, soil texture (clay, silt, and sand percent), slope degree, leaf litter percentage of soil, and tillage interval). These data were normalized, randomized, and divided into training and testing subsets. Results showed that the ANN performed better than the other two models in predicting runoff in training (R2 = 0.98; MSE = 0.004) and the test stages (R2 = 0.90; MSE = 0.95). Statistical analysis and sensitivity analysis of inputs factors showed that rainfall, rangeland cover, and A.M.C are the three most important factors controlling runoff generation. The adopted method can be used to predict the effect of different vegetation and land management scenarios on runoff generation in the study area and the areas with similar settings elsewhere.