Dataset Information

Association of gene polymorphisms in FBN1 and TGF-β signaling with the susceptibility and prognostic outcomes of Stanford type B aortic dissection.

ABSTRACT:

Background

This study is aimed at investigating the association of Fibrillin-1 (FBN1) and transforming growth factor β (TGF-β) signaling-related gene polymorphisms with the susceptibility of Stanford type B aortic dissection (AD) and its clinical prognostic outcomes.

Methods

Five single-nucleotide polymorphism (SNPs) (FBN1rs 145233125, rs201170905, rs11070646, TGFB1rs1800469, and TGFB2rs900) were analyzed in patients with Stanford type B AD (164) and healthy controls (317). Gene-gene and gene-environment interactions were assessed by generalized multifactor dimensionality reduction. A 4-year follow-up was performed for all AD patients.

Results

G carriers of FBN1 rs201170905 and TGFB1 rs1800469 have an increased risk of Stanford type B AD. The interaction of FBN1, TGFB1, TGFB2 and environmental promoted to the increased risk of type B AD (cross-validation consistency = 10/10, P = 0.001). Dominant models of FBN1rs145233125 TC + CC genotype (P = 0.028), FBN1 rs201170905 AG + GG (P = 0.047) and TGFB1 rs1800469 AG + GG (P = 0.052) were associated with an increased risk of death of Stanford type B AD. The recessive model of FBN1 rs145233125 CC genotype (P < 0.001), FBN1rs201170905 GG (P < 0.001), TGFB1 rs1800469 AG + GG genotype (P = 0.011) was associated with an increased risk of recurrence of chest pain in Stanford type B AD.

Conclusions

The interactions of gene-gene and gene-environment are related with the risk of Stanford type B AD. C carriers of rs145233125, G carriers of rs201170905 and G carriers of rs1800469 may be the poor clinical outcome indicators of mortality and recurrent chest pain in Stanford type B AD.

SUBMITTER: Sun L

PROVIDER: S-EPMC8935688 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Association of gene polymorphisms in FBN1 and TGF-β signaling with the susceptibility and prognostic outcomes of Stanford type B aortic dissection.

Sun Ling L Chang Yafei Y Jiang Peipei P Ma Yitong Y Yuan Qinghua Q Ma Xiang X

BMC medical genomics 20220320 1

<h4>Background</h4>This study is aimed at investigating the association of Fibrillin-1 (FBN1) and transforming growth factor β (TGF-β) signaling-related gene polymorphisms with the susceptibility of Stanford type B aortic dissection (AD) and its clinical prognostic outcomes.<h4>Methods</h4>Five single-nucleotide polymorphism (SNPs) (FBN1rs 145233125, rs201170905, rs11070646, TGFB1rs1800469, and TGFB2rs900) were analyzed in patients with Stanford type B AD (164) and healthy controls (317). Gene-g ...[more]

PMID: 35307021

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Project description:ObjectiveThis study aims to investigate the association of lysine methyltransferase 2 C (MLL3) and transforming growth factor β (TGF-β) signaling-related gene polymorphisms with the susceptibility of Stanford type B aortic dissection (AD) and its clinical prognostic outcomes. The methods involved investigating the MLL3 (rs10244604, rs6963460, rs1137721), TGFβ1 (rs1800469), TGFβ2 (rs900), TGFR1 (rs1626340) and TGFR2 (rs4522809) gene polymorphisms. Logistic regression was performed to investigate the association between 7 single nucleotide gene polymorphisms (SNPs) and Stanford type B aortic dissection. The GMDR software was used to analyze gene-gene and gene-environment interactions. The odds ratio (OR) with a 95% confidence interval (CI) was employed to evaluate the association of genes and Stanford type B AD risk.ResultsGenotypes and allele distributions in the case and control groups showed significant differences (P < 0.05). Logistic regression has shown that the Stanford Type B AD risk was highest in individuals with the rs1137721 CT genotype (OR = 4.33, 95% CI = 1.51-12.40). Additionally, WBC, drinking, hypertension, triglycerides (TG), and low-density lipoprotein (LDL-C) were independent risk factors for Stanford Type B AD. Logistic regression showed that the Stanford Type B AD risk was highest in individuals with the MLL3 (rs1137721)-TT + CT and TGFβ1 (rs4522809)-AA genotype (OR = 6.72, 95% CI = 1.56-29.84), and lowest in those with the MLL3 (rs1137721)-CC and TGFβ1 (rs4522809)-AA + GG genotype (OR = 4.38, 95% CI = 0.92-20.83). However, the 55-month median long-term follow-up did not show statistical significance.ConclusionCarriers of both TT + CT of MLL3 (rs1137721) and AA of TGFβ1 (rs4522809) polymorphisms may be closely related to the development of Stanford type B AD. MLL3 (rs1137721), WBC, and TG/TC were found to be associated with the morbidity of Stanford type B AD. MLL3 (KMT2C) is associated with the TGF-β signaling pathway protein. The risk of Stanford type B AD is related to the interactions of gene-gene and gene-environment.

Project description:BackgroundThe expression levels of long noncoding RNAs (lncRNAs) and mRNAs in human acute Stanford type A aortic dissecting aneurysm and normal active vascular tissues were compared using the array lncRNA/mRNA expression profile chip technology.MethodsThe tissue samples of 5 patients who presented with Stanford type A aortic dissections and the normal ascending aorta tissues from 5 donor heart transplantation patients receiving surgical treatment in Ganzhou People's Hospital were collected. Hematoxylin and eosin (HE) staining were performed to investigate the structural features of the ascending aortic vascular tissue. Nanodropnd-100 was used to detect the surface level of RNA in 10 samples included in the experiment, to ensure that the quality of the standard was consistent with the core plate detection. NanoDrop ND-1000 was used to detect the RNA expression levels in 10 specimens included in the experiment to ensure that the quality of specimens satisfied the requirements of the microarray detection experiment. The Arraystar Human LncRNA/mRNAV3.0 expression profile chip (8×60K, Arraystar) was used to detect the expression levels of lncRNAs and mRNAs in the tissue samples.ResultsA total of 29,198 lncRNAs and 22,959 mRNA target genes could be detected in the above tissue samples after the initial data were standardized and low-expression information was filtered. The data in the middle of the range of 50% value consistency was higher. The scatterplot results preliminarily suggested that there were large numbers of lncRNAs with increased and decreased expression in Stanford type A aortic dissection tissues compared with normal aortic tissues. The differentially expressed lncRNAs were enriched in BPs including apoptosis, nitric oxide synthesis, estradiol response, angiogenesis, inflammatory response, oxidative stress, and acute response; cell components (CCs) including cytoplasm, nucleus, cytoplasmic matrix, extracellular space, protein complex, and platelet α granule lumen; and MFs including protease binding, zinc ion binding, steroid compound binding, steroid hormone receptor activity, heme binding, protein kinase, cytokine, superoxide dismutase, and nitric oxide synthase activities.ConclusionsGene ontology analysis demonstrated that many genes in Stanford type A aortic dissection were involved in cell biological functions, cell components, and molecular functions through upregulating and downregulating the levels of expression.

Project description:Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear. Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein-protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm. Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell-cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group. Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

Dataset Information

Association of gene polymorphisms in FBN1 and TGF-β signaling with the susceptibility and prognostic outcomes of Stanford type B aortic dissection.

Background

Methods

Results

Conclusions

Publications

Association of gene polymorphisms in FBN1 and TGF-β signaling with the susceptibility and prognostic outcomes of Stanford type B aortic dissection.

Similar Datasets

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