METTL16 regulates the mRNA stability of FBXO5 via m6A modification to facilitate the malignant behavior of breast cancer.

BACKGROUND: N6-methyladenosine (m6A) regulates the progression of breast cancer (BC). We aimed to investigate the action and mechanism involved of methyltransferase-like protein 16 (METTL16) in BC growth and metastasis. METHODS: RT-qPCR, immunoblotting, and IHC were performed to test the levels of gene expression. CCK-8, clone formation, wound healing, and transwell assays were applied to measure the cell proliferation, migration, and invasion. m6A RNA methylation and MeRIP assay were utilized to confirm the m6A level of total RNA and FBXO5 mRNA. RIP was utilized to ascertain the interaction between METTL16 and FBXO5 mRNA. The in vivo murine subcutaneous tumor and metastasis model were constructed to further confirm the action of METTL16. RESULTS: METTL16 was overexpression in BC cells and tissues. Inhibition of METTL16 restrained the growth and metastasis of BC. Furthermore, the METTL16 level and FBXO5 level was positively correlated in BC tissues, and METTL16 aggrandized the stability of FBXO5 mRNA depending on the m6A modification. Overexpression of FBXO5 antagonized the restrained function of METTL16 knockdown on BC cells' proliferation, migration, invasion, and EMT. CONCLUSION: METTL16 boosts the mRNA stability of FBXO5 via m6A modification to facilitate the malignant action of BC in vitro and in vivo, offering new latent targets for cure of BC.

The interplay between H19 and HIF-1α in mitochondrial dysfunction in myocardial infarction.

Myocardial infarction(MI) causes prolonged ischemia of infarcted myocardial tissue, which triggers a wide range of hypoxia cellular responses in cardiomyocytes. Emerging evidence has indicated the critical roles of long non-coding RNAs(lncRNAs) in cardiovascular diseases, including MI. The purpose of this study was to investigate the roles of lncRNA H19 and H19/HIF-1α pathway during MI. Results showed that cell injury and mitochondrial dysfunction were induced in hypoxia-treated H9c2 cells, accompanied by an increase in the expression of H19. H19 silencing remarkably diminishes cell injury, inhibits the dysfunctional degree of mitochondria, and decreases the injury of MI rats. Bioinformatics analysis and dual-luciferase assays revealed that H19 was the hypoxia-responsive lncRNA, and HIF-1α induced H19 transcription through direct binding to the H19 promoter. Moreover, H19 participates in the HIF-1α pathway by stabilizing the HIF-1α protein. These results indicated that H19 might be a potential biomarker and therapeutic target for myocardial infarction.

Association Between Perivascular Adipose Tissue Density and Atherosclerosis in the Descending Thoracic Aorta.

Radiodensity measured by computed tomography (CT) in Hounsfield Units (HU) is emerging as a clinical tool for detecting perivascular adipose tissue (PVAT) inflammation. In the present study, we hypothesized that PVAT radiodensity might predict the risk of descending thoracic aorta atherosclerosis. A total of 73 subjects who underwent CT angiography to investigate aortic disease were retrospectively analyzed. PVAT radiodensity, aortic complex plaque (ACP), mean plaque-burden score (MPBS), and plaque density were measured, and the association between them was analyzed. Perivascular adipose tissue radiodensity (HU) in patients with different aortic plaques grades (grade 1, 2, 3, and 4) were -93.71 ± 2.50, -93.63 ± 3.93, -90.24 ± 4.49, and -89.90 ± 5.18, respectively, and the difference was significant (P = .010). In the regression analysis, PVAT radiodensity was an independent predictor of ACP, with an OR of 1.263. In the linear analysis, PVAT radiodensity was an independent predictor of MPBS, with a ß-coefficient of .073. In the univariate analysis, only the PVAT radiodensity was significantly associated with plaque density, with a ß-coefficient of -1.666. In conclusion, PVAT density was independently related to descending thoracic aorta atherosclerosis.

Magnetic Resonance Imaging Assessment of Abdominal Ectopic Fat Deposition in Correlation With Cardiometabolic Risk Factors.

Purpose: Ectopic fat accumulation and abdominal fat distribution may have different cardiometabolic risk profiles. This study aimed to assess the associations between various magnetic resonance imaging (MRI)-acquired fat depots and cardiometabolic risk factors. Methods: A total of 320 subjects with median age of 59 years, 148 men and 172 women, were enrolled in the study. Visceral adipose tissue (VAT) area and fat fraction (FF), subcutaneous adipose tissue (SAT) area and FF at the L1-L2 levels, preperitoneal adipose tissue (pPAT) area and FF, hepatic FF, pancreatic FF, and intramuscular FF were assessed by MRI FF maps. The associations of various MRI-acquired fat depots with blood pressure, glucose, and lipid were examined using sex-stratified linear regression. Logistic regression stratified by sex was used to analyze the association of various MRI-acquired fat depots with the risk of hypertension, T2DM, and dyslipidemia. Results: The intraclass correlation coefficient (ICC) values were >0.9, which suggested good interobserver and intraobserver agreement. VAT area, V/S, hepatic fat, pancreatic fat, and pPAT rather than SAT area were significantly associated with multiple cardiometabolic risk factors (all p < 0.05). However, the patterns of these correlations varied by sex and specific risk factors. Also, VAT and SAT FF were only significantly associated with multiple cardiometabolic risk factors in women (all p < 0.05). Conclusions: VAT, hepatic fat, pancreatic fat, and pPAT were associated with cardiovascular metabolic risk factors independent of BMI. The patterns of these correlations were related to gender. These findings further the understanding of the association between ectopic fat deposition and cardiometabolic risk factors and help to better understand the obesity heterogeneity.

The Roles of lncRNA in Myocardial Infarction: Molecular Mechanisms, Diagnosis Biomarkers, and Therapeutic Perspectives.

In recent years, long non-coding RNAs (lncRNAs) have been demonstrated to be associated with many physiological and pathological processes in cardiac. Recent studies have shown that lncRNAs are expressed dynamically in cardiovascular diseases and participate in regulation through a variety of molecular mechanisms, which have become a critical part of the epigenetic and transcriptional regulatory pathways in heart development, as well as the initiation and progress of myocardial infarction. In this review, we summarized some current research about the roles of lncRNAs in heart development and myocardial infarction, with the emphasis on molecular mechanisms of pathological responses, and highlighted their functions in the secondary changes of myocardial infarction. We also discussed the possibility of lncRNAs as novel diagnostic biomarkers and potential therapeutic targets for myocardial infarction.

Hepatic fat quantification of magnetic resonance imaging whole-liver segmentation for assessing the severity of nonalcoholic fatty liver disease: comparison with a region of interest sampling method.

BACKGROUND: Accurate and early assessment of the hepatic fat content is crucial for patients with nonalcoholic fatty liver disease (NAFLD). For years, magnetic resonance imaging (MRI) has been considered the optimal noninvasive method for the assessment of fat accumulation. To avoid time-consuming manual placement of multiple regions of interest (ROI), the use of whole-liver segmentation has been proposed to measure liver fat, mainly for heterogeneous fat deposition. However, it remains uncertain whether the hepatic mean fat fraction (FF) obtained by whole-liver segmentation with the inclusion of intrahepatic vasculature is consistent with the traditional ROI sampling method. In this study, we assessed the accuracy of hepatic mean FF obtained by whole-liver segmentation in patients of NAFLD with different severities using the ROI sampling method as a reference standard. METHODS: Hepatic FFs were measured by whole-liver segmentation and the ROI sampling method (reference standard) using MRI scanning with the iterative decomposition of water and fat with echo an asymmetry at least-square estimation-iron quantification (IDEAL-IQ) sequence. SPSS version 25.0 software was used to analyze the correlation and consistency of data between the two methods. RESULTS: There was a strong correlation in hepatic FF between whole-liver segmentation and the ROI sampling method in healthy, mild, and moderate steatosis patients (r = 0.943, 0.990, and 0.961, respectively). Bland-Altman analysis showed a small bias of +0.50±0.27 and +0.05±0.30, which indicated a small overestimation when using whole-liver segmentation in healthy subjects and mild NAFLD patients. The 95% limits of agreement ranged from +1.02 to -0.03, and from +0.65 to -0.55, respectively. However, a small bias of -0.96±0.77 was also evident, which indicated a small underestimation when using whole-liver segmentation in moderate NAFLD patients. The 95% limits of agreement ranged from +0.56 to -2.48. CONCLUSIONS: Due to inclusion of the intrahepatic vasculature, whole-liver segmentation has some effects on hepatic FF assessment in patients with different NAFLD severities; yet, it does not significantly affect the assessment of whole-liver FF in MRI FF maps.

Quantification of Hepatic Fat Fraction in Patients With Nonalcoholic Fatty Liver Disease: Comparison of Multimaterial Decomposition Algorithm and Fat (Water)-Based Material Decomposition Algorithm Using Single-Source Dual-Energy Computed Tomography.

METHODS: Hepatic fat fractions were quantified by noncontrast (HFFnon-CE) and contrast-enhanced single-source dual-energy computed tomography in arterial phase (HFFAP), portal venous phase (HFFPVP) and equilibrium phase (HFFEP) using MMD in 19 nonalcoholic fatty liver disease patients. The fat concentration was measured on fat (water)-based images. As the standard of reference, magnetic resonance iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron quantification images were reconstructed to obtain HFF (HFFIDEAL-IQ). RESULTS: There was a strong correlation between HFFnon-CE, HFFAP, HFFPVP, HFFEP, fat concentration and HFFIDEAL-IQ (r = 0.943, 0.923, 0.942, 0.952, and 0.726) with HFFs having better correlation with HFFIDEAL-IQ. Hepatic fat fractions did not significantly differ across scanning phases. The HFFs of 3-phase contrast-enhanced computed tomography had a good consistency with HFFnon-CE. CONCLUSIONS: Hepatic fat fraction using MMD has excellent correlation with that of magnetic resonance imaging, is independent of the computed tomography scanning phases, and may be used as a routine technique for quantitative assessment of HFF.