RESUMO
ABSTRACT: Aneurysms are localized dilations of blood vessels, which can expand to 50% of the original diameter. They are more common in cardiovascular and cerebrovascular vessels. Rupture is one of the most dangerous complications. The pathophysiology of aneurysms is complex and diverse, often associated with progressive vessel wall dysfunction resulting from vascular smooth muscle cell death and abnormal extracellular matrix synthesis and degradation. Multiple studies have shown that long noncoding RNAs (lncRNAs) play a significant role in the progression of cardiovascular and cerebrovascular diseases. Therefore, it is necessary to find and summarize them. LncRNAs control gene expression and disease progression by regulating target mRNA or miRNA and are biomarkers for the diagnosis and prognosis of aneurysmal cardiovascular and cerebrovascular diseases. This review explores the role, mechanism, and clinical value of lncRNAs in aneurysms, providing new insights for a deeper understanding of the pathogenesis of cardiovascular and cerebrovascular aneurysms.
Assuntos
Aneurisma Intracraniano , Músculo Liso Vascular , Miócitos de Músculo Liso , Fenótipo , RNA Longo não Codificante , Humanos , Músculo Liso Vascular/patologia , Músculo Liso Vascular/metabolismo , RNA Longo não Codificante/genética , RNA Longo não Codificante/metabolismo , Aneurisma Intracraniano/genética , Aneurisma Intracraniano/patologia , Aneurisma Intracraniano/metabolismo , Aneurisma Intracraniano/fisiopatologia , Miócitos de Músculo Liso/metabolismo , Miócitos de Músculo Liso/patologia , Animais , Regulação da Expressão Gênica , Aneurisma/genética , Aneurisma/patologia , Aneurisma/metabolismo , Doenças Cardiovasculares/genética , Doenças Cardiovasculares/patologia , Doenças Cardiovasculares/metabolismo , Doenças Cardiovasculares/fisiopatologia , Transdução de SinaisRESUMO
BACKGROUND: The effects of genes on the development of intracranial aneurysms (IAs) remain to be elucidated, and reliable blood biomarkers for diagnosing IAs are yet to be established. This study aimed to identify genes associated with IAs pathogenesis and explore their diagnostic value by analyzing IAs datasets, conducting vascular smooth muscle cells (VSMC) experiments, and performing blood detection. METHODS: IAs datasets were collected and the differentially expressed genes were analyzed. The selected genes were validated in external datasets. Autophagy was induced in VSMC and the effect of selected genes was determined. The diagnostic value of selected gene on the IAs were explored using area under curve (AUC) analysis using IAs plasma samples. RESULTS: Analysis of 61 samples (32 controls and 29 IAs tissues) revealed a significant increase in expression of ADORA3 compared with normal tissues using empirical Bayes methods of "limma" package; this was further validated by two external datasets. Additionally, induction of autophagy in VSMC lead to upregulation of ADORA3. Conversely, silencing ADORA3 suppressed VSMC proliferation and autophagy. Furthermore, analysis of an IAs blood sample dataset and clinical plasma samples demonstrated increased ADORA3 expression in patients with IA compared with normal subjects. The diagnostic value of blood ADORA3 expression in IAs was moderate when analyzing clinical samples (AUC: 0.756). Combining ADORA3 with IL2RB or CCR7 further enhanced the diagnostic ability for IAs, with the AUC value over 0.83. CONCLUSIONS: High expression of ADORA3 is associated with IAs pathogenesis, likely through its promotion of VSMC autophagy. Furthermore, blood ADORA3 levels have the potential to serve as an auxiliary diagnostic biomarker for IAs.
Assuntos
Aneurisma Intracraniano , Humanos , Aneurisma Intracraniano/diagnóstico , Aneurisma Intracraniano/genética , Aneurisma Intracraniano/patologia , Teorema de Bayes , Perfilação da Expressão Gênica , Transcriptoma , BiomarcadoresRESUMO
Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein-protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.