Downregulation of NAGLU in VEC Increases Abnormal Accumulation of Lysosomes and Represents a Predictive Biomarker in Early Atherosclerosis

Explore the role of long noncoding RNAs and mRNAs in intracranial atherosclerotic stenosis: From the perspective of neutrophils

CONTEXT

Circulating neutrophils and long noncoding RNAs (lncRNAs) play various roles in intracranial atherosclerotic stenosis (ICAS).

OBJECTIVE

Our study aimed to detect differentially expressed (DE) lncRNAs and mRNAs in circulating neutrophils and explore the pathogenesis of atherosclerosis from the perspective of neutrophils.

METHODS

Nineteen patients with ICAS and 15 healthy controls were enrolled. The peripheral blood of the participants was collected, and neutrophils were separated. The expression profiles of lncRNAs and mRNAs in neutrophils from five patients and five healthy controls were obtained, and DE lncRNAs and mRNAs were selected. Six lncRNAs were selected and validated using quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and ceRNA and lncRNA-RNA binding protein (RBP)-mRNA networks were constructed. Correlation analysis between lncRNAs and mRNAs was performed. Functional enrichment annotations were also performed.

RESULTS

Volcano plots and heat maps displayed the expression profiles and DE lncRNAs and mRNAs, respectively. The qRT-PCR results revealed that the four lncRNAs showed a tendency consistent with the expression profile, with statistical significance. The ceRNA network revealed three pairs of regulatory networks: lncRNA RP3-406A7.3-NAGLU, lncRNA HOTAIRM1-MVK/IL-25/GBF1/CNOT4/ANKK1/PLEKHG6, and lncRNA RP11-701H16.4-ZNF416. The lncRNA-RBP-mRNA network showed five pairs of regulatory networks: lncRNA RP11-701H16.4-TEK, lncRNA RP11-701H16.4-MED17, lncRNA SNHG19-NADH-ubiquinone oxidoreductase core subunit V1, lncRNA RP3-406A7.3-Angel1, and lncRNA HOTAIRM1-CARD16.

CONCLUSIONS

Our study identified and verified four lncRNAs in neutrophils derived from peripheral blood, which may explain the transcriptional alteration of neutrophils during the pathophysiological process of ICAS. Our results provide insights for research related to the pathogenic mechanisms and drug design of ICAS.

Construction and Evaluation of a Risk Score Model for Lymph Node Metastasis-Associated Circadian Clock Genes in Esophageal Squamous Carcinoma

Background: Studies suggested that circadian clock genes (CCGs) in human esophageal squamous carcinoma (ESCC) samples are dysregulated. However, the relevance of CCGs to lymph node metastasis (LNM) and prognosis of ESCC remains unclear. Methods: The differentially expressed genes (DEGs) between normal and ESCC samples in The Cancer Genome Atlas database (TCGA) database were intersected with the genes associated with LNM (LNMGs) in ESCC samples and 300 CCGs to obtain the differentially expressed LNM-associated CCGs (DE-LNM-CCGs). The risk model was constructed by Cox regression analysis in the TCGA-ESCC training set, and the accuracy of the risk model was verified by risk profile and overall survival profile. Furthermore, differences of 23 immune cells, 13 immune functions, and immune checkpoint molecules between the high- and low-risk groups were assessed using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Gene set enrichment analysis (GSEA) was conducted to investigate the functional differences between low- and high-risk groups. Finally, we validated the mRNA expression levels of prognostic model genes by quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of six DE-LNM-CCGs were identified in TCGA-ESCC. TP53 and NAGLU were selected by Cox regression analysis to construct the risk model. Risk profile plots, overall survival plots, and validation results of the risk model in the validation set indicated that the constructed risk model was reliable. The result of ssGSEA showed that the percentages of activated B cells, activated dendritic cells, effector memory CD8 T cells, immune function in neutrophils, plasmacytoid dendritic cells, T cell co-inhibition, and Type 17 T helper cells were different between the high- and low-risk groups. In addition, the expression of CD274, PDCD1, TNFRSF18, and TNFRSF9 was dysregulated between the high- and low-risk groups. GSEA revealed that the high-risk group was associated with cell differentiation, oxidative phosphorylation, and steroid biosynthesis pathways, while the low-risk group was associated with chromosome, ECM–receptor interaction, and other pathways. Finally, qRT-PCR results showed that the mRNA expression levels of two prognostic genes were consistent with TCGA. Conclusion: In conclusion, the risk model constructed based on TP53 and NAGLU could accurately predict the prognosis.