ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
<p><b>BACKGROUND</b>White matter lesions (WMLs) are common findings in brain magnetic resonance imaging (MRI) and are strongly associated with stroke incidence, recurrence, and prognosis. However, the relationship between WMLs and transient ischemic attacks (TIAs) is not well established. This study aimed to determine the clinical significance of WMLs in patients with TIA.</p><p><b>METHODS</b>A total of 181 consecutive inpatients with first-ever TIA were enrolled. Brain MRIs within 2 days of symptom onset were used to measure WML volumes. Recurrent vascular events within 1 year of TIA onset were assessed. The relationship between WMLs and recurrent risk of vascular events was determined by a multivariate logistic regression.</p><p><b>RESULTS</b>WMLs were identified in 104 patients (57.5%). Age and ratio of hypertension were significantly different between patients with and without WMLs. The incidence of vascular events in patients with WMLs significantly increased in comparison to those without WMLs (21.15% vs. 5.19%, 95% confidence interval [CI]: 1.18-15.20, P = 0.027) after controlling for confounders. Furthermore, distributions of WML loads were found to be different between patients who developed vascular events and those who did not. WML volumes were demonstrated to be correlated with recurrent risks, and the fourth quartile of WML volumes led to an 8.5-fold elevation of recurrent risk of vascular events compared with the first quartile (95% CI: 1.52-47.65, P = 0.015) after adjusting for hyperlipidemia.</p><p><b>CONCLUSION</b>WMLs occur frequently in patients with TIA and are associated with the high risk of recurrent vascular events, suggesting a predictive neuroimaging marker for TIA outcomes.</p>
ABSTRACT
<p><b>BACKGROUND</b>The long-term effects of bone marrow-derived cells (BMC) transplantation in patients with acute myocardial infarction (AMI) have not been established. The present meta-analysis of randomized controlled trials with follow-up ≥ 2 years was performed to investigate the long-term effects of BMC therapy in patients after AMI.</p><p><b>METHODS</b>Specific terms were used to conduct a systematic literature search of MEDLINE, EMBASE, the Cochrane Library and the Cochrane Central Register of Controlled Trials, and the China Biological Medicine Disk database from their inception to March 2012. A standardized protocol was used to extract information, and random effect model was used to analyze all data except major adverse events.</p><p><b>RESULTS</b>Five trials comprising 510 patients were included. Compared with controls, BMC therapy significantly improved left ventricular ejection fraction (LVEF) (4.18%, 95%CI: 2.02% to 6.35%, P = 0.0002), while mildly but not significantly reduced left ventricular end-systolic volume (-4.47 ml, 95%CI: -10.92 to 1.99, P = 0.17) and left ventricular end-diastolic volume (-2.29 ml, 95%CI: -9.96 to 5.39, P = 0.56). Subgroup analysis revealed that significant improvement of LVEF induced by BMC therapy could be observed in patients with baseline LVEF ≤ 42%, but disappeared in those with baseline LVEF > 42%. There were trends in favor of BMC therapy for most major clinical adverse events, though most differences were not significant.</p><p><b>CONCLUSIONS</b>Intracoronary BMC infusion in patients with AMI seems to be safe and may further improve LVEF on top of standard therapy; especially the beneficial effects could last for long term. The findings need to be validated in the future.</p>
Subject(s)
Female , Humans , Male , Middle Aged , Acute Disease , Bone Marrow Transplantation , Myocardial Infarction , General Surgery , Randomized Controlled Trials as Topic , Ventricular Function, LeftABSTRACT
An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.
Subject(s)
Humans , Algorithms , Cluster Analysis , Data Interpretation, Statistical , Electromyography , Methods , Entropy , Fractals , Models, Statistical , Nonlinear Dynamics , Pattern Recognition, Automated , Signal Processing, Computer-AssistedABSTRACT
Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal dimension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can represent different patterns of surface EMG signals.