Structural and functional changes of fibrinogen due to aging

Gligorijević, Nevenka; Križáková, Martina; Penezić, Ana; Katrlı́k, Jaroslav; Nedić, Olgica

doi:10.1016/j.ijbiomac.2017.11.016

Cited by 20 publications

(17 citation statements)

References 47 publications

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“…These genes are FGG (ENSG00000171557), FGA (ENSG00000171560), and FGB (ENSG00000171564), the three fibrinogen chain coding genes involved in the polymerization of a fibrin matrix. This finding is consistent with previous studies about the increasing fibrinogen content in the elderly skeletal muscle leading to persistent fibrin deposition preventing myofiber repair [ 68 , 69 ]. They also support the hypothesis of an inflammatory response triggered by a fibrin accumulation.…”

Section: Resultssupporting

confidence: 93%

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

et al. 2021

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Background Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. Results Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. Conclusion GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

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Section: Resultssupporting

confidence: 93%

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

et al. 2021

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“…Furthermore, we even observed a linear correlation between preoperative plasma fibrinogen levels and tumour size, which indirectly indicates Fib's prognostic significance. Previous studies have reported that the plasma concentrations of some coagulation factors, such as Fib, increase progressively with age [35]. We also found that patients in the H-Fib group were older than patients in the L-Fib group, which was consistent with previous studies [23,35].…”

Section: Discussionsupporting

confidence: 93%

“…Previous studies have reported that the plasma concentrations of some coagulation factors, such as Fib, increase progressively with age [35]. We also found that patients in the H-Fib group were older than patients in the L-Fib group, which was consistent with previous studies [23,35]. However, in our study, for patients with GISTs, the correlation between age and the preoperative plasma Fib levels was not strong (Spearman correlation coefficient [r] = 0.198, P = 0.005).…”

Section: Discussionmentioning

confidence: 99%

Prognostic significance of preoperative plasma fibrinogen levels in primary gastrointestinal stromal tumours: a retrospective cohort study

Song

Cao

Pan

et al. 2020

Preprint

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Background Improved prediction of prognosis for gastrointestinal stromal tumours (GISTs) has become increasingly important since the introduction of small molecule tyrosine kinase inhibitors. Here, we aimed to evaluate the prognostic significance of preoperative plasma fibrinogen (Fib) levels in patients with primary GISTs and to analyse their correlations with clinicopathological characteristics. Methods A total of 201 previously untreated patients with primary GISTs who had undergone radical surgery at our institution between October 2004 and July 2018 were enrolled. Patient demographics, clinicopathological characteristics, preoperative plasma Fib levels and recurrence-free survival (RFS) were analysed. The optimal cut-off value for Fib levels was calculated using time-dependent receiver operating characteristic curve analysis. RFS, the primary endpoint, was calculated by the Kaplan–Meier method and compared by the log-rank test. Univariate and multivariate Cox regression models were calculated. Results Patients in the high Fib group had a shorter RFS than those in the low Fib group (P < 0.001). In multivariate analysis, high preoperative plasma Fib levels were detected as an independent adverse prognostic factor (P = 0.008, hazard ratio 3.136, 95% CI 1.356‒7.256). Furthermore, high preoperative plasma Fib levels also indicated a poor prognosis within the modified National Institutes of Health (mNIH) high-risk subgroup (P = 0.041). In addition, preoperative plasma Fib levels showed a positive correlation with several prognostic factors and even a linear relationship with tumour size (Spearman correlation coefficient [ r ] = 0.411, P < 0.001). Conclusions High preoperative plasma Fib levels may indicate a poor prognosis in patients with primary GISTs. As a cost-effective biomarker, preoperative assessment of plasma Fib levels may help to further risk stratify patients with mNIH high-risk GISTs and instruct the application of targeted therapy.

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“…These genes are FGG (ENSG00000171557), FGA (ENSG00000171560), and FGB (ENSG00000171564), the three fibrinogen chain coding genes involved in the polymerization of a fibrin matrix. This finding is consistent with previous studies about the increasing fibrinogen content in the elderly skeletal muscle leading to persistent fibrin deposition preventing myofiber repair [63,64]. They also support their hypothesis of an inflammatory response triggered by a fibrin accumulation.…”

Section: Multiple Conditions Modules Comparison and Analysissupporting

confidence: 93%

GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package

Lemoine

Scott‐Boyer

Ambroise

et al. 2020

Preprint

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Background: Network-based analysis of gene expression through co-expression networks can be used to investigate modular interactions occurring between genes toward diﬀerent biological functions. An extended description of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.Results: Here we present GWENA, a new R package that integrates gene co-expression network construction and a whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and diﬀerential co-expression. To demonstrate its performances, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. We successfully prioritized a gene whose involvement was unknown in the muscle development and growth. We also gave new insight about the variations in patterns of co-expression as the already known age-dependent loss of connectivity was found coupled to a genes interactions reorganization leading to the expression of other functions involved in aging. Conclusion: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as conditions comparison, it eases the understanding of genes interactions involved in diseases or phenotypes. Going beyond actual packages to perform co-expression analysis, GWENA includes new tools to fully characterize modules, such as diﬀerential co-expression, additional enrichment databases, and network visualization.

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Structural and functional changes of fibrinogen due to aging

Cited by 20 publications

References 47 publications

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

Prognostic significance of preoperative plasma fibrinogen levels in primary gastrointestinal stromal tumours: a retrospective cohort study

GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package

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