Long noncoding RNAs (lncRNAs) play important roles in the development of vascular diseases. However, the effect of lncRNA NORAD on atherosclerosis remains unknown. This study aimed to investigate the effect NORAD on endothelial cell injury and atherosclerosis. Ox-LDL-treated human umbilical vein endothelial cells (HUVECs) and high-fat-diet (HFD)-fed ApoE-/- mice were used as in vitro and in vivo models. Results showed that NORAD-knockdown induced cell cycle arrest in G0/G1 phase, aggravated ox-LDL-induced cell viability reduction, cell apoptosis, and cell senescence along with the increased expression of Bax, P53, P21 and cleaved caspase-3 and the decreased expression of Bcl-2. The effect of NORAD on cell viability was further verified via NORAD-overexpression. NORAD- knockdown increased ox-LDL-induced reactive oxygen species, malondialdehyde, p-IKBα expression levels and NF-κB nuclear translocation. Proinflammatory molecules ICAM, VCAM, and IL-8 were also increased by NORAD- knockdown. Additionally, we identified the strong interaction of NORAD and IL-8 transcription repressor SFPQ in HUVECs. In ApoE-/- mice, NORAD-knockdown increased the lipid disorder and atherosclerotic lesions. The results have suggested that lncRNA NORAD attenuates endothelial cell senescence, endothelial cell apoptosis, and atherosclerosis via NF-κB and p53-p21 signaling pathways and IL-8, in which NORAD-mediated effect on IL-8 might through the direct interaction with SFPQ.

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to self-antigen, autoantibody production, and abnormal immune response. Cuproptosis is a recently reported cell death form correlated with the initiation and development of multiple diseases. This study intended to probe cuproptosis-related molecular clusters in SLE and constructed a predictive model. Methods: We analyzed the expression profile and immune features of cuproptosis-related genes (CRGs) in SLE based on GSE61635 and GSE50772 datasets and identified core module genes associated with SLE occurrence using the weighted correlation network analysis (WGCNA). We selected the optimal machine-learning model by comparing the random forest (RF) model, support vector machine (SVM) model, generalized linear model (GLM), and the extreme gradient boosting (XGB) model. The predictive performance of the model was validated by nomogram, calibration curve, decision curve analysis (DCA), and external dataset GSE72326. Subsequently, a CeRNA network based on 5 core diagnostic markers was established. Drugs targeting core diagnostic markers were acquired using the CTD database, and Autodock vina software was employed to perform molecular docking.Results: Blue module genes identified using WGCNA were highly related to SLE initiation. Among the four machine-learning models, the SVM model presented the best discriminative performance with relatively low residual and root-mean-square error (RMSE) and high area under the curve (AUC = 0.998). An SVM model was constructed based on 5 genes and performed favorably in the GSE72326 dataset for validation (AUC = 0.943). The nomogram, calibration curve, and DCA validated the predictive accuracy of the model for SLE as well. The CeRNA regulatory network includes 166 nodes (5 core diagnostic markers, 61 miRNAs, and 100 lncRNAs) and 175 lines. Drug detection showed that D00156 (Benzo (a) pyrene), D016604 (Aflatoxin B1), D014212 (Tretinoin), and D009532 (Nickel) could simultaneously act on the 5 core diagnostic markers.Conclusion: We revealed the correlation between CRGs and immune cell infiltration in SLE patients. The SVM model using 5 genes was selected as the optimal machine learning model to accurately evaluate SLE patients. A CeRNA network based on 5 core diagnostic markers was constructed. Drugs targeting core diagnostic markers were retrieved with molecular docking performed.

Background: Ischaemic stroke and other cardiovascular illnesses are characterised by abnormalities in the processes of thrombosis and haemostasis, which rely on platelet activity. In platelets, a wide variety of microRNAs (long non-coding RNA, lncRNAs) is found. Due to the absence of nuclear DNA in platelets, lncRNAs may serve as critical post-transcriptional regulators of platelet activities. However, research into the roles of lncRNAs in platelets is limited. Objective: The purpose of this study is to learn more about the molecular mechanism by which MALAT1 affects platelet activity and thrombus formation. Methods/results: The CD34⁺ megakaryocytes used in this research as an in vitro model for human megakaryocytes and platelets. Cell adhesion and spreading are enhanced in the absence and presence of agonists in CD34⁺ megakaryocytes subjected to MALAT1 knockdown (KD). The adhesion and activity of platelet-like particles produced by MALAT1 KD cells are significantly enhanced at rest and after thrombin activation. Thrombus development on a collagen matrix is also greatly enhanced in the microfluidic whole-blood perfusion model: platelets lacking MALAT1 exhibit elevated accumulation, distributing area and activity. In addition, MALAT1-deficient mice bleed less and form a stable occlusive thrombus more quickly than wild-type mice. PTEN and PDK1 regulated the activity of MALAT1 in platelets to carry out its PI3k/Akt/GSK-3β signalling pathway-related function. Conclusion: The suppression of MALAT1 expression significantly increases platelet adhesion, spreading, platelet activity, and thrombus formation. lncRNAs may constitute a unique class of platelet function modulators.

Noncoding RNAs (ncRNAs) are a group of RNA molecules that cannot encode proteins, and a better understanding of the complex interaction networks coordinated by ncRNAs will provide a theoretical basis for the development of therapeutics targeting the regulatory effects of ncRNAs. Platelets are produced upon the differentiation of hematopoietic stem cells into megakaryocytes, 10¹¹ per day, and are renewed every 8–9 days. The process of thrombopoiesis is affected by multiple factors, in which ncRNAs also exert a significant regulatory role. This article reviewed the regulatory roles of ncRNAs, mainly microRNAs (miRNAs), circRNAs (circular RNAs), and long non-coding RNAs (lncRNAs), in thrombopoiesis in recent years as well as their roles in primary immune thrombocytopenia (ITP). What is the context? What is new? What is the impact? Changes in ncRNA expression are associated with changes in the production of MKs, thrombopoiesis, and platelet function, which allows a new understanding of the pathogenesis of many congenital or acquired platelet-related diseases.