Expression of TLR4-PTGE2 signaling genes in atherosclerotic carotid plaques and peripheral blood

Oxidative Stress and Pyroptosis in Doxorubicin-Induced Heart Failure and Atrial Fibrillation

Patients undergoing doxorubicin (Dox) chemotherapy often develop new-onset atrial fibrillation and heart failure. Recent studies indicate that the TLR4/MyD88/NLRP3 pyroptosis signaling pathway plays a key role in the occurrence and development of cancer, heart failure, and atherosclerosis. However, few studies investigated the role of oxidative stress and pyroptosis in doxorubicin-induced heart failure and new-onset atrial fibrillation. In this study, we recruited 84 healthy subjects, 112 patients undergoing Dox chemotherapy showing heart failure (HF), and 62 patients undergoing Dox treatment who manifested atrial fibrillation (AF). The mRNA and protein levels of TLR4 expression, several downstream pyroptosis-associated proteins (cleaved caspase-1, NLRP3, GSDMD-N, and HMGB-1), serum inflammatory factors, and oxidative stress were detected at the beginning of chemotherapy and after 3 months of Dox chemotherapy. Oxidative stress and downstream pyroptosis-associated proteins tended to increase in the Dox-baseline group to the Dox-HF group. However, virtually no change in the expression of either oxidative stress or pyroptosis-associated proteins was detected in patients after three months of Dox chemotherapy compared with those at baseline. This study suggests that the prolonged oxidative stress and high levels of pyroptosis-associated proteins contribute to cardiac systolic dysfunction, suggesting TLR4 as a novel biomarker and a potential treatment target for doxorubicin-induced heart failure.

Atherosclerosis: The Culprit and Co-victim of Vascular Dementia

Vascular dementia (VD), a cerebrovascular disease which causes cognitive impairment, is one of the significant factors that affects the quality of senectitude. Atherosclerosis (AS) is a chronic inflammatory syndrome and closely associated with VD. Analyzing the role of AS in VD contribute greatly to its early detection and prevention, but their relationship has not been integrated into a complete network. This review summarizes AS biomarkers as VD predictors for the first time and describes the direct mechanisms of AS causing VD from five aspects: vascular morphogenesis, hemodynamic change, neurovascular unit damage (NVU), oxidative stress, and microRNA (miRNA). Finally, it discriminates the relationship between AS and VD in common risk factors which can be disease or some molecules. In particular, these data imply that the role of AS in VD is not only a pathogenic factor but also a comorbidity in VD. This review aims to bring new ideas for the prediction and treatment of VD.

Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein-protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.