E-selectin gene polymorphism (A561C) and essential hypertension

Ouyang, Yaofu; Wu, Hexing; Yang, Haijun; Gao, Yong; Li, Huihui; Lu, Shou‐En; Hu, Yanling; Tang, Xian Fa; Zhang, H.

doi:10.1007/s00059-014-4122-1

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Genes such as COMP (cartilage oligomeric matrix protein) [ 131 ], CHI3L1 [ 132 ], PLA2G2A [ 133 ], P2RY12 [ 134 ], CR1 [ 135 ], HPSE (heparanase) [ 136 ], PTX3 [ 137 ] and SERPINE1 [ 138 ] were related to atherosclerosis. CCDC80 [ 139 ], CMA1 [ 140 ], MDK (midkine) [ 141 ], GNA14 [ 142 ], SCG2 [ 143 ], NPPB (natriuretic peptide B) [ 144 ], FGF10 [ 145 ], ARNTL (aryl hydrocarbon receptor nuclear translocator like) [ 146 ], WNK3 [ 147 ], EDNRB (endothelin receptor type B) [ 148 ], THBS1 [ 149 ], SELE (selectin E) [ 150 ], SLC4A7 [ 151 ], AQP4 [ 152 ] and KCNK3 [ 153 ] are thought to be responsible for progression of hypertension, but these genes might to be associated with progression of HF. CNTNAP2 [ 154 ], GLI2 [ 155 ], DPT (dermatopontin) [ 156 ], AEBP1 [ 157 ], ITIH5 [ 158 ], CXCL11 [ 159 ], GDNF (glial cell derived neurotrophic factor) [ 160 ], MCHR1 [ 161 ], FLT3 [ 162 ], ELANE (elastase, neutrophil expressed) [ 163 ], OSMR (oncostatin M receptor) [ 164 ] and IL15RA [ 165 ] are involved in development of obesity, but these genes might be key for progression of HF.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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Introduction Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. Methods The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein–protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene—miRNA regulatory network and target gene—TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. Results A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene—miRNA regulatory network and target gene—TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. Conclusion The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

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Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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“…These findings are biologically plausible, as single nucleotide polymorphisms at the SELE locus affect normal functions of the gene by upregulating the gene expression levels. 53 The observation of upregulated expression levels of the SELE gene due to polymorphisms is supported by Bannan et al, who found significantly higher levels associated with the C allele at the A561C polymorphism. 54 A more recent study by Mlekusch et al lend further support for the significant association between increased plasma SELE levels and the SELE gene polymorphisms.…”

Section: Discussionmentioning

confidence: 89%

Relationship of SELE A561C and G98T Variants With the Susceptibility to CAD

Liao

Chen

Wang³

et al. 2016

Medicine

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Published genetic association studies have produced controversial results regarding the association of SELE gene polymorphisms (A516C and G98T) and CAD susceptibility. We therefore chose to perform a meta-analysis to determine the association.Twenty-seven eligible articles were identified through electronic databases, providing 5170 CAD cases and 4996 controls. Fixed-effects or random-effects summary ORs were calculated to estimate the risk of CAD in relation to A516C and G98T. Forest plots and funnel plots were constructed by Stata software 12.0.A strong association was observed between A516C and susceptibility of CAD among 4757 cases and 4272 controls. The summary OR was greatest in individuals carrying the CC genotype (OR = 1.91, 95% CI, 1.12–3.25). A significantly increased risk was indicated in both Caucasians and Asians. The analyses by disease type showed a significant increase in the risk of AP and MI. We also noted a strong association in population-based studies. In the analyses of G98T, data were available for 1422 cases and 1625 controls. We saw a markedly increased risk of CAD associated with G98T. The highest risk was indicated in individuals with the TT genotype (OR = 2.82, 95% CI, 1.15–6.89). A similar trend was seen in Asians and population-based studies.These findings provide consistent evidence that A516C and G98T polymorphisms of the SELE gene may be associated with increased susceptibility of CAD.

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“…Many studies have also confirmed that subjects carrying the 677CC genotype are at significantly increased risk of developing hypertension, while the C677T gene polymorphism leads to lower blood pressure ( Ghogomu et al, 2016 ). Meta-analysis suggests that carriers of the C allele of the A561C polymorphism of the SELE gene may contribute to an increased risk of hypertension in the Chinese Han population ( Ouyang et al, 2015 ). In addition, a significant association between G98T polymorphism and hypertension has also been found ( Chen et al, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Effect of interaction between occupational stress and polymorphisms of MTHFR gene and SELE gene on hypertension

Yang

Qiu

Abudoubari

et al. 2022

PeerJ

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Background Gene-environment interaction is related to the prevalence of hypertension, but the impact of genetic polymorphisms on hypertension may vary due to different geography and population. Objective To explore the impact of the interaction among occupational stress and MTHFR gene and SELE gene polymorphism on the prevalence of hypertension in Xinjiang oil workers. Methods A case-control study was conducted on 310 oil workers. In an oilfield base in Karamay City, Xinjiang, 155 hypertensive patients aged 18~60 years old with more than one year of service were selected as the case group, and 155 oil workers without hypertension were selected as the control group according to the 1:1 matching principle (matching conditions: the gender and shift were the same. The age is around 2 years old). The Occupational Stress Scale was used to evaluate the degree of occupational stress, PCR technique was used to detect MTHFR and SELE gene polymorphism, Logistic regression analysis was used to analyze the effects of gene and occupational stress on hypertension, and gene-gene and gene-environment interactions were analyzed by generalized multi-factor dimension reduction method. Results The G98T polymorphism of SELE gene (χ2 = 6.776, P = 0.034), the C677T (χ2 = 7.130, P = 0.028) and A1298C (χ2 = 12.036, P = 0.002) loci of MTHFR gene and the degree of occupational stress (χ2 = 11.921, P = 0.003) were significantly different between the case group and the control group. The genotypes GT at the G98T polymorphism of the SELE gene (OR = 2.151, 95% CI [1.227–3.375]), and the dominant model (AC/CC vs AA, OR = 1.925, 95% CI [1.613–3.816]); AC and CC at the A1298C polymorphism of the MTHFR gene (ORAC = 1.917, 95% CI [1.064–3.453]; ORCC = 2.233, 95% CI [1.082–4.609]), the additive model (CC vs AA, OR = 2.497, 95% CI [1.277–4.883]) and the dominant model (AC/CC vs AA, OR = 2.012, 95% CI [1.200–3.373]); at the C677T polymorphism of the MTHFR gene CT and TT (ORCT = 1.913, 95% CI [1.085–3.375]; ORTT = 3.117, 95% CI [1.430–6.795]), the additive model (CC vs AA, OR = 1.913, 95% CI [1.085–3.375]) and the dominant model (AC/CC vs AA, OR = 2.012, 95% CI [1.200–3.373]), which could increase hypertension risk (P < 0.05). The gene-gene interaction showed that there was a positive interaction between the A1298C and C677T sites of the MTHFR gene, and the gene-occupational stress interaction showed that there was a positive interaction between the A1298C and C677T sites of the MTHFR gene and the occupational stress. Conclusion The interaction of gene mutation and occupational stress in Xinjiang oil workers maybe increase the risk of hypertension.

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E-selectin gene polymorphism (A561C) and essential hypertension

Abstract: The findings suggest that C allele carriers of the SELE gene polymorphism (A561C) might be predisposed to EH in the Chinese population. Further investigations in other ethnic populations should be conducted to verify these findings.

Cited by 6 publications

References 32 publications

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Relationship of SELE A561C and G98T Variants With the Susceptibility to CAD

Effect of interaction between occupational stress and polymorphisms of MTHFR gene and SELE gene on hypertension

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