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Objective This study was designed to determine the methylation profile of four CpGs and the genotypes of two CpG-SNPs located in promoter region of DIO2 in patients with Kashin-Beck disease (KBD). We also analyzed the interaction between the CpGs methylations and CpG-SNPs. Methods Whole blood specimens were collected from 16 KBD patients and 16 healthy subjects. Four CpGs and two CpG-SNPs in the promoter regions of DIO2 were detected using matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). The CpGs methylation levels were compared between samples from KBD patients and healthy subjects. The methylation levels were also analyzed in KBD patients with different CpG-SNP genotypes. Results The mRNA expression of DIO2 in whole blood of KBD patients was significnatly lower than in healthy controls (P <0.05). The methylation levels of DIO2-1_CpG_3 in KBD patients were significantly higher than those in healthy controls (P <0.05). The methylation levels of four CpGs were not significantly different between KBD patients and healthy controls. The methylation level of DIO2-1_CpG_3 in the promoter region of DIO2 in KBD patients with GA/AA genotype was significantly higher than that of KBD patients with GG genotype (P <0.05). Conclusion The methylation level of DIO2 increases in KBD patients. Similar trends exist in KBD carriers of variant genotypes of CpG-SNPs DIO2 rs955849187.
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Humans , Case-Control Studies , Iodide Peroxidase/genetics , Kashin-Beck Disease/genetics , Methylation , Polymorphism, Single Nucleotide , Promoter Regions, GeneticABSTRACT
Objective To investigate the relationship between the expression of glutathione peroxidase(GPX)genes and the clinical prognosis in glioma patients,and to construct and evaluate the model for predicting the prognosis of glioma. Methods The clinical information and GPX expression of 663 patients,including 153 patients of glioblastoma(GBM)and 510 patients of low-grade glioma(LGG),were obtained from The Cancer Genome Atlas(TCGA)database.The relationship between GPX expression and patient survival was analyzed.The key GPX affecting the prognosis of glioma was screened out by single- and multi-factor Cox's proportional-hazards regression models and validated by least absolute shrinkage and selection operator(Lasso)regression.Finally,we constructed the model for predicting the prognosis of glioma with the screening results and then used concordance index and calibration curve respectively to evaluate the discrimination and calibration of model. Results Compared with those in the control group,the expression levels of GPX1,GPX3,GPX4,GPX7,and GPX8 were up-regulated in glioma patients(all P<0.001).Moreover,the expression levels of other GPX except GPX3 were higher in GBM patients than in LGG patients(all P<0.001).The Kaplan-Meier curves showed that the progression-free survival of GBM with high expression of GPX1(P=0.013)and GPX4(P=0.040),as well as the overall survival,disease-specific survival,and progression-free survival of LGG with high expression of GPX1,GPX7,and GPX8,was shortened(all P<0.001).GPX7 and GPX8 were screened out as the key factors affecting the prognosis of LGG.The results were further used to construct a nomogram model,which suggested GPX7 was the most important variable.The concordance index of the model was 0.843(95%CI=0.809-0.853),and the calibration curve showed that the predicted and actual results had good consistency. Conclusion GPX7 is an independent risk factor affecting the prognosis of LGG,and the nomogram model constructed with it can be used to predict the survival rate of LGG.
Subject(s)
Humans , Brain Neoplasms , Glioblastoma , Glioma/diagnosis , Glutathione Peroxidase/metabolism , Peroxidases , Prognosis , Proportional Hazards ModelsABSTRACT
To study the gene expression of cardiac mesenchymal cells in patients with type 2 diabetes mellitus (T2DM)based on a whole-genome high-throughput sequencing dataset,screen differentially expressed genes,analyze the genetics signature of cardiac mesenchymal cells in T2DM patients by bioinformatics analysis,and explore the environmental chemicals related to the key differentially expressed genes. The dataset GSE106177 was obtained from Gene Expression Omnibus (GEO) database.The dataset was pre-processed and analyzed by Network Analyst,Cytoscape 3.7.1,String11.0,CTD,and HMDD for screening for differentially expressed genes,enrichment analysis,establishment of protein-protein interaction (PPI) networks,and screening for relevant environmental chemicals. The gene expression pattern of cardiac mesenchymal cells in T2DM patients was significantly different from that in the control group.There were 135 differentially expressed genes,of which 58 (42.96%) were up-regulated and 77 (57.04%) were down-regulated.The differentially expressed genes mainly participated in biological processes such as multicellular organism development,anatomical structure development,and system development and were mainly involved in hepatocellular carcinoma,Cushing's syndrome,and cholesterol metabolism.PPI network showed that UBC was the core protein node.The microRNA-Gene interaction network showed that seven microRNAs,represented by hsa-mir-8485,interacted with the differentially expressed genes.Key T2DM related genes such as UBC,DNER,and CNTN1 interacted with bisphenol A. The gene expression profile of cardiac mesenchymal cells markedly changes in T2DM patients,during which UBC may play an important biological role.Bisphenol A exposure may also affect the development and normal function of cardiac cells in T2DM patients.
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Objective To provide the basis for early diagnosis and clinical treatment for diabetic kidney disease(DKD)and cardiovascular disease by analyzing the causes of DKD and the relationship between them. Methods Microarray gene chip data from the glomerulus in DKD were downloaded from GEO Database.The gene expression profile,gene function,protein-protein interaction network,gene and signal pathway co-expression network of control and DKD groups were analyzed and the key node genes between the two groups were selected by R,QOE,GCBI,STRING and Genclip.Results The gene expression profile of glomerular tissue in DKD patients was significantly altered.Compared with control group,there were 844 differentially expressed genes in DKD patients.One protein-protein interaction network with the core of TNNT 2 occurred among top 20 differentially expressed genes.Differentially expressed genes were mainly related to inflammatory and stress responses,as well as to MAPK signal pathway.Conclusion The cause of DKD is mainly related to inflammatory and stress responses, which are also related to cardiovascular disease.
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Objective To analyze the differentially expressed genes and key proteins in T cells between acute and chronic idiopathic thrombocytopenic purpura (ITP) in children and provide the basis for the prevention and therapies of this disease. Methods Microarray gene chip data from T cells of children with acute or chronic ITP were downloaded from the GEO Database. The gene expression profiles,gene function,and protein interaction network were analyzed by R,QOE,Networkanalyst,GCBI,and GenClip. Results The gene expression profiles between these two groups were significantly different. Among the 54 675 genes analyzed,there were 457 (0.84%) differentially expressed genes between these two groups. In the protein interaction networks among top 20 differentially expressed genes,the core was JUN(down-regulated) and ITCH(up-regulated),which were both related to the tumor necrosis factor signaling pathway;differentially expressed genes were mainly related to the activation and tolerance of T cell. Conclusions The gene expression profiles differ between acute and chronic ITP patients,suggesting that the gene transcription profile plays a regulatory role in the different stages of ITP. JUN and ITCH may play a role in predict the progression of ITP and may exert their biological functions by regulating the tumor necrosis factor signaling pathway.
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Objective To construct a model of Seasonal Autoregressive Integrated Moving Average (SARIMA) for forecasting the epidemic of Japanese encephalitis (JE) in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention. Methods Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015. Results SARIMA (1, 1, 1) (2, 1, 1)was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R, and a lower Mean Absolute Percent Error. SARIMA (1, 1, 1) (2, 1, 1)was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA (1, 1, 1) (2, 1, 1)appeared to be reliable and accurate and could be applied to incidence prediction. Conclusions The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally (≥0.4/100 000). According to the predicted Results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.