RESUMEN
Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD.
Asunto(s)
Trastorno Bipolar , Humanos , Trastorno Bipolar/genética , Trastorno Bipolar/diagnóstico , Trastorno Bipolar/psicología , Encéfalo/metabolismo , Biología Computacional , Biomarcadores/metabolismo , Expresión GénicaRESUMEN
The functional significance of the zinc-finger of the cerebellum (ZIC) gene family in gliomas remains to be elucidated. Clinical data from patients with gliomas, containing expression levels of ZIC genes, were extracted from CCLE, GEPIA2 and The Human Protein Atlas (HPA). Univariate survival analysis adjusted by Cox regression via OncoLnc was used to determine the prognostic significance of ZIC expression. We used cBioPortal to explore the correlation between gene mutations and overall survival (OS). ZIC expression was found to be related to immune cell infiltration in gliomas via TIMER analysis. GO term and KEGG pathway enrichment analyzes were performed with Metascape. PPI networks were constructed using STRING. The expression levels of ZIC1/3/4/5 in gliomas were significantly different from those in normal samples. High expression levels of ZIC1/5 were associated with poor OS in brain low-grade glioma (LGG) patients, while low ZIC3 expression combined was related to favorable OS in glioblastoma multiforme (GBM). ZIC alterations were associated with poor prognosis in LGG patients and related to favorable prognosis in GBM patients. We observed that the expression of ZICs was related to immune cell infiltration in glioma patients. ZICs were enriched in several pathways and biological processes involving Neuroactive ligand-receptor interaction (hsa04080). The PPI network revealed that some proteins coexpressed with ZICs played a role in the pathogenesis of gliomas. Differences in the expression levels of ZIC genes could provide a significant marker for predicting prognosis in gliomas.
Asunto(s)
Biomarcadores de Tumor/metabolismo , Neoplasias Encefálicas/mortalidad , Proteínas de Unión al ADN/metabolismo , Glioblastoma/mortalidad , Glioma/mortalidad , Proteínas del Tejido Nervioso/metabolismo , Factores de Transcripción/metabolismo , Biomarcadores de Tumor/genética , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/metabolismo , Neoplasias Encefálicas/patología , Biología Computacional/métodos , Proteínas de Unión al ADN/genética , Bases de Datos Genéticas , Bases de Datos de Proteínas , Regulación Neoplásica de la Expresión Génica , Glioblastoma/genética , Glioblastoma/metabolismo , Glioblastoma/patología , Glioma/genética , Glioma/metabolismo , Glioma/patología , Proteínas de Homeodominio/genética , Proteínas de Homeodominio/metabolismo , Humanos , Proteínas del Tejido Nervioso/genética , Proteínas Nucleares/genética , Proteínas Nucleares/metabolismo , Pronóstico , Tasa de Supervivencia , Factores de Transcripción/genéticaRESUMEN
Background: Postprandial hypotension (PPH) is an independent predictive factor of all-cause mortality in older people. Drug management has not achieved a satisfactory effect yet. In recent years, many studies have found that acarbose may be effective in the treatment of PPH with glucose metabolism disorders. Objective: To assess the efficacy and safety of acarbose on PPH with glucose metabolism disorders. Methods: PubMed (MEDLINE), Cochrane, EMBASE, Web of Science, Clinical Trials, and relevant Chinese databases were searched from inception to October 1, 2020. Randomized controlled studies of acarbose in the treatment of PPH with glucose metabolism disorders were included. Review Manager 5.3 software was used for quality evaluation and meta-analysis. GRADEpro GDT software was used to GRADE the evidence for the research objectives. Results: A total of 4 randomized controlled studies including 202 participants were identified after screening. The meta-analysis showed that acarbose significantly attenuated the decrease in postprandial systolic blood pressure [weighted mean difference (MD): -9.84, 95% CI: -13.34 to -6.33], diastolic blood pressure (MD: -6.86, 95% CI: -12.89 to -0.83), and mean arterial pressure (MD: -8.10, 95% CI: -12.40 to -3.49) compared with the control group. One study reported a case of adverse reactions that included mild abdominal distension in the acarbose group (4.8%, 1/21). No adverse reactions were reported in the other three studies. Conclusion: Acarbose may attenuate the decrease in postprandial blood pressure and avoid the occurrence of PPH in patients with PPH and abnormal glucose metabolism disorders. More clinical trials are needed to make a clear conclusion. Registration: PROSPERO CRD42020171335.
