PITX2 and NEURL1 SNP polymorphisms in Hungarian atrial fibrillation patients determined by quantitative real-time PCR and melting curve analysis

Szirák, Krisztina; Soltész, Beáta; Hajas, Orsolya; Urbancsek, Réka; Nagy‐Baló, Edina; Penyige, András; Csanádi, Zoltán; Nagy, Bálint

doi:10.1016/j.jbiotec.2019.04.022

Cited by 4 publications

(7 citation statements)

References 38 publications

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“…When assessing the occurrence of AF in our study population by examining the case and control groups, we found that the T risk allele rs2200733 (PITX2) was significantly associated with an increased risk of occurrence. Our finding is in line with the majority of similar studies [7,[9][10][11][12][13][29][30][31][32][33][34]. The seven other SNVs described in GWAS were not found to be associated with a greater risk of AF development.…”

Section: Discussion Of Resultssupporting

confidence: 92%

“…There was a difference between the case and control groups with regards various variables (sex, age, BMI, congestive heart failure, coronary heart disease and diabetes) to occurrence, which hints at a selection bias. Since the results of the regression analysis only confirmed the result that rs2200733 (PITX2) was associated with a higher risk for AF occurrence, this result simply echoes previous studies [7,[9][10][11][12][13][29][30][31][32][33][34]. Additionally, we found that as the number of cases included in the multiple regression analysis decreased, the odds ratio and confidence interval increased from OR = 4.174 (95% CI = 1.454-11.980) for the 97 patients treated with DCC for whom follow-up data were available to OR = 38.766 (95% CI = 2.085-720.952) for the 50 patients for whom transthoracic echocardiography data were also available.…”

Section: Limitationssupporting

confidence: 75%

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A Higher Polygenic Risk Score Is Associated with a Higher Recurrence Rate of Atrial Fibrillation in Direct Current Cardioversion-Treated Patients

et al. 2021

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Background and Objectives: Recurrence of atrial fibrillation (AF) within six months after sinus rhythm restoration with direct current cardioversion (DCC) is a significant treatment challenge. Currently, the factors influencing outcome are mostly unknown. Studies have found a link between genetics and the risk of AF and efficacy of rhythm control. The aim of this study was to examine the association between eight single-nucleotide variants (SNVs) and the risk of AF development and recurrence after DCC. Materials and Methods: Regarding the occurrence of AF, 259 AF cases and 108 controls were studied. Genotypes for the eight SNVs located in the genes CAV1, MYH7, SOX5, KCNN3, ZFHX3, KCNJ5 and PITX2 were determined using high-resolution melting analysis and confirmed with Sanger sequencing. Six months after DCC, a telephone interview was conducted to determine whether AF had recurred. A polygenic risk score (PRS) was calculated as the unweighted sum of risk alleles. Multivariate regression analyses were performed to assess SNV and PRS association with AF occurrence and recurrence after DCC. Results: The risk allele of rs2200733 (PITX2) was significantly associated with the development of AF (p = 0.012, OR = 2.31, 95% CI = 1.206–4.423). AF recurred in 60% of patients and the allele generally associated with a decreased risk of AF of rs11047543 (SOX5) was associated with a greater risk of AF recurrence (p = 0.014, OR = 0.223, 95% CI = 0.067–0.738). A PRS of greater than 7 was significantly associated (p = 0.008) with a higher likelihood of developing AF after DCC (OR = 4.174, 95% CI = 1.454–11.980). Conclusions: A higher PRS is associated with increased odds of AF recurrence after treatment with DCC. PITX2 (rs2200733) is significantly associated with an increased risk of AF. The protective allele of rs11047543 (SOX5) is associated with a greater risk of AF recurrence. Further studies are needed to predict the success of rhythm control and guide patient selection towards the most efficacious treatment.

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Section: Discussion Of Resultssupporting

confidence: 92%

Section: Limitationssupporting

confidence: 75%

A Higher Polygenic Risk Score Is Associated with a Higher Recurrence Rate of Atrial Fibrillation in Direct Current Cardioversion-Treated Patients

et al. 2021

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“…Thus, Chinese scientists have obtained data that C allele of AGTR1 gene significantly increases the risk of AF developing. As a result of the study of PITX2 and NEURL1 SNP in Hungarian patients with AF, determined by quantitative PCR, no significant association was found between SNP rs2595104 and rs6584555 and AF [37]. The obtained results may be due to genetic characteristics of the population, depending on climatic conditions, geography of residence, and confirm that AF is a heterogeneous disease.…”

Section: Introductionmentioning

confidence: 79%

Association of gene polymorphism at atrial fibrillation: A literature review

et al. 2021

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Atrial fibrillation is the most common persistent heart rhythm disorder, the prevalence of which increases with age. The progressive prevalence of atrial fibrillation is becoming a serious threat to public health throughout the world, as it is associated with high mortality rates. In the review, we analyzed the literature data on the state of knowledge of the genetic nature of atrial fibrillation. To achieve this goal, a systematic search and subsequent analysis of publications and online resources was carried out. All publications are indexed in PubMed, Medline, e-library, Google Scholar. To date, at least 30 loci associated with atrial fibrillation have been identified. At the same time, the research results depend on such factors as ethnicity, concomitant cardiac and extracardiac diseases, inheritance mechanisms, and intergenic interactions. The complexity of etiopathogenesis, the heterogeneity of atrial fibrillation poses the task of researchers to further search for factors that play a leading role in the development of the disease. Significant progress in understanding the genetic basis of atrial fibrillation was achieved with the advent of large-scale genomic GWAS studies, which include genotyping up to a million common variants or single nucleotide polymorphisms. The currently available data on the association of candidate genes with atrial fibrillation in case-control studies are controversial and necessitate more detailed studies on various ethnic groups.

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“…Signaling pathways include cytokine signaling in immune system [ [87], CES1 [88], CYP19A1 [89], PLAC8 [90], CD36 [91], GIPR (gastric inhibitory polypeptide receptor) [92], ARG1 [93], MSR1 [94], DOCK2 [95], S1PR1 [96], OXTR (oxytocin receptor) [97], F11R [98], LEPR (leptin receptor) [99], AR (androgen receptor) [100], DKK3 [101], FOXO1 [102], PIK3CB [103], WWP1 [104], PHIP (pleckstrin homology domain interacting protein) [105], MPZL3 [106], FBXO2 [107], GUCY2C [108], ADRA2A [109], CHRNA5 [110], GLP2R [111], SDC3 [112], NFAT5 [113], PON2 [114], PRNP (prion protein) [115], DGKE (diacylglycerol kinase epsilon) [116], ARHGAP21 [117], COQ2 [118], EPHX2 [119] and FAM3C [120] were observed to be associated with the progression of obesity. Vargas-Alarcón et al [121], Frantz et al [122], Bosè et al [123], Gu et al [124], Liu et al [125], Szirák et al [126], Ye et al [127], Alikhah et al [128], Chen et al [129], Mao et al [130], Meyer et al [131], Zhang et al...…”

Section: Discussionmentioning

confidence: 99%

“…Altered expression of PLA2G5 [55], CASP1 [56], EDNRA (endothelin receptor type A) [57], F2RL1 [58], FOXP3 [59], DRD4 [60], COL6A3 [61], TIMP4 [62], SOCS1 [63], CD74 [64], TGFB1 [65], ATF5 [66], IRF7 [67], IRX3 [68], FOXC2 [69], STX1A [70], IL1RL1 [71], HHIP (hedgehog interacting protein) [72], ELOVL2 [73], BGN (biglycan) [74], POMC (proopiomelanocortin) [75], DOK5 [76], COL1A1 [77], POSTN (periostin) [78], SOD3 [79], ZNF423 [80], FABP5 [81], DDIT4 [82], KCTD15 [83], COL1A2 [84], MGAT2 [85], ENDOG (endonuclease G) [86], HSPA5 [87], CES1 [88], CYP19A1 [89], PLAC8 [90], CD36 [91], GIPR (gastric inhibitory polypeptide receptor) [92], ARG1 [93], MSR1 [94], DOCK2 [95], S1PR1 [96], OXTR (oxytocin receptor) [97], F11R [98], LEPR (leptin receptor) [99], AR (androgen receptor) [100], DKK3 [101], FOXO1 [102], PIK3CB [103], WWP1 [104], PHIP (pleckstrin homology domain interacting protein) [105], MPZL3 [106], FBXO2 [107], GUCY2C [108], ADRA2A [109], CHRNA5 [110], GLP2R [111], SDC3 [112], NFAT5 [113], PON2 [114], PRNP (prion protein) [115], DGKE (diacylglycerol kinase epsilon) [116], ARHGAP21 [117], COQ2 [118], EPHX2 [119] and FAM3C [120] were observed to be associated with the progression of obesity. Vargas-Alarcón et al [121], Frantz et al [122], Bosè et al [123], Gu et al [124], Liu et al [125], Szirák et al [126], Ye et al [127], Alikhah et al […”

Section: Discussionmentioning

confidence: 99%

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Polycystic ovary syndrome (PCOS) is is associated with infertility, obesity, insulin resistance, hyperinsulinemia, type 2 diabetes mellitus, hypertension, cardiovascular problems, neurological and psychological problems and cancer. The specific mechanism of PCOS and its complications remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of PCOS and its complications. The next generation squancing (NGS) datset GSE199225 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. A protein-protein interaction (PPI) network was constructed and module analysis was performed using HiPPIE and cytoscape. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed for research. The expression of the hub genes was validated using receiver operating characteristic (ROC) curve analysis. We have identified 957 DEGs in total, including 478 up regulated genes and 479 down regulated gene. GO and REACTOME illustrated that DEGs in PCOS were significantly enriched in regulation of molecular function, developmental process, interferon signaling and platelet activation, signaling and aggregation. Finally, through analyzing the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes HSPA5, PLK1, RIN3, DBN1, CCDC85B, DISC1, AR, MTUS2, LYN and TCF4 by the Cytoscape software. This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to PCOS and its complications. These analysis results provide us with novel ideas for finding biomarkers and treatment methods for PCOS and its complications.

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PITX2 and NEURL1 SNP polymorphisms in Hungarian atrial fibrillation patients determined by quantitative real-time PCR and melting curve analysis

Cited by 4 publications

References 38 publications

A Higher Polygenic Risk Score Is Associated with a Higher Recurrence Rate of Atrial Fibrillation in Direct Current Cardioversion-Treated Patients

A Higher Polygenic Risk Score Is Associated with a Higher Recurrence Rate of Atrial Fibrillation in Direct Current Cardioversion-Treated Patients

Association of gene polymorphism at atrial fibrillation: A literature review

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

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