Cardiac-cerebral vascular disease (CCVD), is primarily induced by atherosclerosis, and is a leading cause of mortality. Numerous studies have investigated and attempted to clarify the molecular mechanisms of atherosclerosis; however, its pathogenesis has yet to be completely elucidated. Two expression profiling datasets, GSE43292 and GSE57691, were obtained from the Gene Expression Omnibus (GEO) database. The present study then identified the differentially expressed genes (DEGs), and functional annotation of the DEGs was performed. Finally, an atherosclerosis animal model and neural network prediction model was constructed to verify the relationship between hub gene and atherosclerosis. The results identified a total of 234 DEGs between the normal and atherosclerosis samples. The DEGs were mainly enriched in actin filament, actin binding, smooth muscle cells, and cytokine-cytokine receptor interactions. A total of 13 genes were identified as hub genes. Following verification of animal model, the common DEG, Tropomyosin 2 (TPM2), was found, which were displayed at lower levels in the atherosclerosis models and samples. In summary, DEGs identified in the present study may assist clinicians in understanding the pathogenesis governing the occurrence and development of atherosclerosis, and TPM2 exhibits potential as a promising diagnostic and therapeutic biomarker for atherosclerosis.

Based on χ, Spearman's rho test and multiple linear regression, NEU is related with intimal thickness (P < 0.05). Furthermore, NEU can predict the intimal thickness through the ROC curve. What's more, N/L is a risk factor of carotid media thickness (P < 0.05) by the Spearman's rho test, and is also correlated with poor NDT (P < 0.05) based on univariate Cox proportional regression analysis. Through ROC curve, N/L can predict the carotid media thickness. The carotid atherosclerotic endarterium is richest in macrophagocytes, and the arrangement of endotheliocytes is disordered. In summary, the increased NEU and N/L respectively have a strong correlation and precise predictability for carotid intimal and media thickness of atherosclerosis.

Keloids, characterized by skin fibrosis and excessive accumulation of extracellular matrix, remain a therapeutic challenge. In this study, we systematically capture the cellular composition of keloids by the single-cell RNA sequencing technique. Our results indicated that there are significant differences in most cell types present between 12 pairs of keloid and adjacent normal tissue. We found that fibroblasts, endothelial cells, mast cells, mural cells, and Schwann cells increased significantly in keloid. The proportion of mesenchymal fibroblast subpopulations in keloids was markedly higher than those in the surrounding normal skin tissue. Furthermore, we found that the immune profiles between two groups varied significantly. The proportion of macrophages in the keloid was significantly elevated compared to the surrounding normal tissue, while cDC2 cells significantly decreased. Hotspot and pseudotime trajectory analysis indicated two modules of macrophage cells (Module2: highly expresses RNASE1, C1QA, CD163, CD14, C1QC, FCGRT, MS4A7; Module10: highly expresses APOC1, CTSB, CTSL, TYROBP), which exhibited the characteristics of tumor-associated macrophages, were upregulated in more-advanced keloid cells. Subsequently, the analysis of cellular communication networks suggested that a macrophage-centered communication regulatory network may exist in keloids and that fibroblasts in keloids may facilitate the transition and proliferation of M2 macrophages, which contributes to further comprehension of the immunological features of keloids. Overall, we delineate the immunology landscape of keloids and present new insights into the mechanisms involved in its formation in this study.

Background Atherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages. Methods Two expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein–protein interaction network and analyzing hub genes. Results A total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3 , CD55 , and DYNC2H1 . Conclusion Hub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.