FBLN5, a member of the short fibulins in the fibulin family of extracellular matrix/matricellular proteins, is involved in interactions with components of the basement membrane and extracellular matrix proteins. It plays key roles in endothelial tissues in many vascular diseases. In this study, the relationship between FBLN5 and carotid atherosclerotic plaque stability as well as the regulatory roles of miRNAs were evaluated. Differential gene expression analyses and weighted gene co-expression network analysis (WGCNA) based on the GSE163154 dataset (including 16 samples without intraplaque hemorrhage and 27 samples with intraplaque hemorrhage) in GEO revealed that FBLN5 is related to plaque stability and is the most significantly differentially expressed gene. LASSO regression was used to evaluate genes obtained from the intersection of differentially expressed genes and clinically significant modules identified by WGCNA. A prediction model based on eight genes, including FBLN5, was constructed and showed an accuracy of 0.951 based on an ROC analysis. Low FBLN5 expression in plaque tissues was confirmed by immunohistochemistry and western blotting. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses showed that FBLN5 acted mainly by the maintenance of the cellular matrix and reactive oxygen species production. miRNAs upstream of these eight predictive genes, including FBLN5, were identified and used to construct a network diagram. These results revealed that hsa-mir-128 and hsa-mir-532-3p were upstream regulatory factors of FBLN5, as verified by PCR assays of human plaque tissues demonstrating that both miRNAs were significantly up-regulated. Therefore, FBLN5 may play an important role in carotid atherosclerosis via hsa-mir-128 and hsa-mir-532-3p as well as become an essential target for treatment.

Necroptosis is a form of programmed cell death that has recently been shown to be important in the progression of head and neck cancer (HNC). Noncoding RNAs (ncRNAs) are known to function in cell death and tumor formation. In this study, we focused on microRNAs (miRNA) that play roles in necroptosis and the progression of HNC. We collected miRNA expression data, related clinical data of patients with HNC, and miRNA data related to necroptosis. A prognostic multimiRNA molecular marker was generated based on differential expression analysis and univariate and multivariate Cox regression analyses. Target genes of the prognosis-related miRNAs were identified, and their functions were evaluated by Gene Ontology Enrichment Analysis to reveal the processes the miRNAs may be involved in. Eight potentially prognostic miRNAs were identified through differential expression analysis: miR-331-3p, miR-181d-5p, miR-181b-5p, miR-500a-3p, miR-425-5p, miR-181a-5p, miR-141-3p, and miR-200a-5p. Multivariate Cox regression identified the risk score as an independent prognostic factor (univariate Cox regression results: hazard ratio (HR): 2.2028, 95% confidence interval (CI): 1.2640–3.8388, P = 0.0053; multivariate Cox regression results: HR: 2.4168, 95% CI: 1.3743–4.2501, P = 0.0022). Survival curve analysis revealed that patients with a high risk score had a bad prognosis (P = 0.0109). A receiver operating characteristic curve showed that the model has a certain prediction ability. We identified 187 miRNA-related genes, which were enriched in “cell cycle” and “cellular senescence.” In conclusion, this study identified eight novel miRNA markers for predicting the prognosis of patients with HNC and paved the way for future research on necroptosis-related genes.

Atherosclerosis (AS) is a chronic inflammatory disease, which has a complex interplay between altered immune metabolism and oxidative stress. Therefore, we aimed to determine the oxidative stress and immune-related biomarkers in AS. Differential gene expression analyses are based on the GSE100927 dataset in the Gene Expression Omnibus (GEO), and 389 oxidative stress (OS) genes are identified based on gene set enrichment analysis (GSEA). We identified 74 differentially expressed genes related to oxidative stress (DEOSGs). "CIBERSORT" and "WGCNA" R Packages were used to compare the differences in immune infiltration levels between AS and control samples. The DEOSGs (N = 74) were intersected with the key module's genes of WGCNA (N = 972), and 27 differentially expressed immune-related oxidative stress genes (DEIOSGs) were obtained. To identify the pivotal genes, a protein-protein interaction (PPI) network was constructed using the STRING database and the Cytoscape software. MMP9, ALOX5, NCF2, NCF, and NCF4 were identified as diagnostic markers of AS, and we validated them in the GSE57691 dataset. The expression levels of the five diagnostic genes were significantly highly expressed in the AS group. Correlation analysis and single-cell analysis revealed that five diagnostic genes were mainly correlated with macrophages M1. We, respectively, intersected differentially expressed genes (DEGs) with ferroptosis gene set, necroptosis gene set, and pyroptosis gene set. The findings suggested that ALOX5 and NCF2 were differentially expressed genes of ferroptosis. High expression of five hub genes in RAW264.7 macrophages were confirmed by PCR. High ALOX5 and NCF2 expression levels in plaque tissues were confirmed by immunohistochemistry (IHC) and western blotting. Our study identified that MMP9, ALOX5, NCF2, NCF1, and NCF4 were diagnostic genes of AS and associated with oxidative stress. ALOX5 and NCF2 may be involved in the formation of the necrotic core in AS by regulating macrophage ferroptosis.

Objective:To explore the potential mechanism of abdominal aortic aneurysm (AAA) through weighted gene co-expression network analysis.Methods:The transcriptome sequencing data of GSE47472 and GSE57691 were combined. Differential expression genes (DEGs) were obtained by differential gene expression analysis, and hub genes were obtained by weighted gene coexpression network analysis (WGCNA). The intersection of the two genes was conducted to obtain hub DEGs, and the Gene Set Enrichment Analysis was performed. Subsequently, a mouse model of AAA and a sham surgery group were established to verify hub DEGs by quantitative polymerase chain reaction (qPCR). Statistical analysis was performed using GraphPad Prism 8. Kolmogorov-Smirnov test data for normality, independent-sample student′s t test for significance of gene differential expression. Results:745 DEGs were screened out, and 16 gene co-expression modules were established. Hub module includes 119 genes.60 hub DEGs were obtained after intersection. Functional enrichment analysis of hub DEGs showed low expression of genes related to smooth muscle proliferation, differentiation, and adhesion function. The adhesion related genes were N-cadherin 2 (CDH2), N-cadherin 13 (CDH13), FERM domain containing kindlin 2 (FERMT2), Rho associated coiled-coil containing protein kinase 1 (ROCK1), and laminin subunit α5 (LAMA5), and the significance of their expression differences was verified by qPCR. The relative expression times were FERMT2=0.31 ( t=2.454, P<0.05), ROCK1=0.22 ( t=3.686, P<0.05), LAMA5=0.45 ( t=3.168, P<0.05), CDH13=1.36 ( t=0.103, P>0.05), CDH2=1.71 ( t=0.702, P>0.05). Conclusion:Low expression of FERMT2, ROCK1 and LAMA5 were involved in the formation of AAA by mediating the focal adhesion between vascular smooth muscle cells and extracellular matrix.