Relationships Between Inflammatory Parameters Derived From Complete Blood Count and Quantitative Flow Ratio in Patients With Stable Coronary Artery Disease.

To investigate the relationships between inflammatory parameters, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII), and quantitative flow ratio (QFR) in stable coronary artery disease (CAD) patients (n = 450) enrolled in this cross-sectional study. Logistic regression was performed to evaluate the associations of NLR, PLR, MLR, and SII evaluated as continuous and binary variables with QFR ≤0.80. When treated as continuous variables, lnNLR was associated with QFR ≤0.80 with borderline significance in univariable (odds ratio (OR) = 1.60, p = .05) and multivariable analysis (OR = 1.72, p = .05), while lnMLR was associated with QFR ≤0.80 significantly in univariable analysis (OR = 1.87, p = .03) and with borderline significance in multivariable analysis (OR = 1.91, p = .05). When treated as binary variables, high levels of MLR and SII were significantly associated with QFR ≤0.80 in univariable (MLR: OR = 1.91, p = .02; SII: OR = 2.42, p = .006) and multivariable analysis (MLR: OR = 1.83, p = .04; SII: OR = 2.19, p = .02). NLR, MLR, and SII, but not PLR, were significantly associated with the severity of coronary physiology in stable CAD patients.

Construction of a circRNA-miRNA-mRNA Regulatory Network for Coronary Artery Disease by Bioinformatics Analysis.

BACKGROUND: Circular RNAs (circRNAs) were known to be related to the pathogenesis of many diseases through competing endogenous RNA (ceRNA) regulatory mechanisms. However, the function of circRNA in coronary artery disease (CAD) remains unclear. In this study, we aim to construct a circRNA-related competing endogenous RNA (ceRNA) network in CAD. METHODS: The gene expression profiles of CAD were obtained from Gene Expression Omnibus datasets. Bioinformatics analysis was performed to construct a ceRNA regulatory network, from which the hub genes involved were identified through protein-protein interaction (PPI) networks leading to the identification of the circRNA-miRNA-hub gene subnetwork. In addition, function enrichment analysis was performed to detect the potential biological mechanism in which circRNA might be involved. RESULTS: A total of 115 DEcircRNAs (differentially expressed circRNAs), 17 DEmiRNAs (differentially expressed microRNAs), and 790 DEmRNAs (differentially expressed mRNAs) were identified between CAD and control groups from microarray datasets. Functional enrichment analysis showed that DEmRNAs were significantly involved in inflammation-related pathways and ubiquitin-protein ligase binding. After identifying 20 DEcircRNA-DEmiRNA pairs and 561 DEmiRNA-DEmRNA pairs, we obtained a circRNA-miRNA-mRNA regulatory network. PPI network analysis showed that eight hub genes were closely related to CAD, leading to the identification of a circRNA-miRNA-hub gene subnetwork consisting of nine circRNAs (hsa_circ_0020275, hsa_circ_0020387, hsa_circ_0020417, hsa_circ_0045512, hsa_circ_0047336, hsa_circ_0069094, hsa_circ_0071326, hsa_circ_0071330, and hsa_circ_0085340), four miRNAs (hsa-miR-136-5p, hsa-miR-376c-3p, hsa-miR-411-5p, and hsa-miR-654-5p), and eight mRNAs (MKRN1, UBE2H, UBE2W, UBE2D1, UBE2F, BE2J1, ZNRF1, and SIAH2). In addition, we discovered these hub genes were enriched in the ubiquitin-mediated proteolysis pathway, suggesting circRNAs may be involved in the pathogenesis of CAD through this pathway. CONCLUSIONS: This study may deepen our understanding of the potential role of circRNA-miRNA-mRNA regulation network in CAD and suggest novel diagnostic biomarkers and therapeutic targets for CAD.

RBM4 regulates M1 macrophages polarization through targeting STAT1-mediated glycolysis.

M1/M2 macrophages polarization play important roles in regulating tissue homeostasis. Recently, RNA-binding motif 4 (RBM4) has been reported to modulate the proliferation and expression of inflammatory factors in HeLa cells. However, whether RBM4 is involved in regulating macrophage polarization and inflammatory factor expression are still unknown. In this study, RAW264.7, a mouse macrophage cell line, were stimulated with interferon γ (IFN-γ) or interleukin-4 (IL-4) to induce M1/M2 macrophages polarization. We found that IFN-γ, but not IL-4, stimulation decreased RBM4 expression in macrophages, and RBM4 overexpression inhibits IFN-γ-induced M1 macrophage polarization. Furthermore, RNA-Sequencing, protein immunoprecipitation accompanied with mass spectrometry, and extracellular acidification rate analysis showed that RBM4 suppresses IFN-γ-induced M1 macrophage polarization though inhibiting glycolysis. Moreover, RBM4 knockdown promoted IFN-γ-induced signal transducer and activator of transcription 1 (STAT1) activation via increasing STAT1 mRNA stability, leading to the increase of glycolysis-related gene transcripts regulated by STAT1. Finally, we find that RBM4 interacts with YTH N6-methyladenosine RNA binding protein 2 (YTHDF2) to degrade m6A modified STAT1 mRNA, thereby regulating glycolysis and M1 macrophage polarization. Collectively, the current study firstly reports that RBM4 regulates M1 macrophages polarization through targeting STAT1-mediated glycolysis and shows that RBM4 is a possible candidate for regulating macrophage M1 polarization and inflammatory responses.