Genetic variants in <i>LGALS3</i> are related to lower galectin-3 serum levels and clinical outcomes in systemic sclerosis patients: A case-control study

Cunha, Eudes Gustavo Constantino; Lima, Camilla Albertina Dantas de; Vilar, Kamila de Melo; Nóbrega, Marcelo Francisco de; Almeida, Anderson Rodrigues de; Pereira, Michelly Cristiny; Dantas, Andréa Tavares; Gonçalves, Rafaela Silva Guimarães; Rêgo, Moacyr Jesus Barreto de Melo; Duarte, Ângela Luzia Branco Pinto; Pitta, Maíra Galdino da Rocha

doi:10.1080/08916934.2021.1919881

Cited by 7 publications

(4 citation statements)

References 55 publications

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“…Taken together, downregulating the expression of HDAC1 may contribute to the amelioration of skin disorders in AD. Galectin-3(Gal-3), also known as LGALS3, plays a critical role in self-tolerance, inflammation, and fibrosis [ 48 ]. It has reported that the level of Gal-3 was decreased in AD epidermis compared with control samples.…”

Section: Discussionmentioning

confidence: 99%

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

et al. 2023

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Objective The aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning. Method The pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets. Results Nine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result. Conclusions The XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients.

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Section: Discussionmentioning

confidence: 99%

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

et al. 2023

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“…One of the most investigated LGALS-3 variants, the non-synonymous rs4652, was previously associated with Gal-3 levels [10] and is in an almost absolute LD with the variant investigated in the present study, rs11628437 (D = 1, r 2 = 0.98). The variant rs4652 has previously been associated with CAD in diabetics [31] and systemic sclerosis, a disease characterized by chronic inflammation, like atherosclerosis [32]. To date, variants rs4040064 and rs11628437 have not been analyzed in association with any cardiovascular phenotype and are not present on microarray chips according to the Ensembl database [33].…”

Section: Discussionmentioning

confidence: 99%

Variants Tagging LGALS-3 Haplotype Block in Association with First Myocardial Infarction and Plasma Galectin-3 Six Months after the Acute Event

Djordjevic

Živković

Bošković

et al. 2022

Genes

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Galectin-3 is encoded by LGALS-3, located in a unique haplotype block in Caucasians. According to the Tagger server, rs4040064, rs11628437, and rs7159490 cover 82% (r2 > 0.8) of the genetic variance of this HapBlock. Our aims were to examine the association of their haplotypes with first myocardial infarction (MI), changes in left ventricular echocardiographic parameters over time, and impact on plasma galectin-3 and LGALS-3 mRNA in peripheral blood mononuclear cells, both 6 months post-MI. The study group consisted of 546 MI patients and 323 controls. Gene expression was assessed in 92 patients and plasma galectin-3 in 189 patients. Rs4040064, rs11628437, rs7159490, and LGALS-3 mRNA expression were detected using TaqMan® technology. Plasma galectin-3 concentrations were determined by the ELISA method. We found that the TGC haplotype could have a protective effect against MI (adjusted OR 0.19 [0.05–0.72], p = 0.015) and that the GAC haplotype had significantly higher galectin-3 concentrations (48.3 [37.3–59.4] ng/mL vs. 18.9 [14.5–23.4] ng/mL], p < 0.0001), both in males and compared to the referent haplotype GGC. Higher plasma Gal-3 was also associated with higher NYHA class and systolic dysfunction. Our results suggest that variants tagging LGALS-3 HapBlock could reflect plasma Gal-3 levels 6 months post-MI and may have a potential protective effect against MI in men. Further replication, validation, and functional studies are needed.

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“…In future clinical trials, observations of disease processes are needed to approve these competitive biomarker proteins as evident biomarkers of ALS [ 39 ]. In addition, LGALS3 genetic variation encoding Gal-3 have been associated with systemic sclerosis patients and cognitive functions at old age [ 42 , 43 ]. The role of Gal-3 in different neurodegenerative disorders is shown in Figure 2 .…”

Section: Physiological Role Of Galsmentioning

confidence: 99%

Galectins—Potential Therapeutic Targets for Neurodegenerative Disorders

Chaudhary¹,

Chaudhary²,

Rawat³

et al. 2022

IJMS

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Advancements in medicine have increased the longevity of humans, resulting in a higher incidence of chronic diseases. Due to the rise in the elderly population, age-dependent neurodegenerative disorders are becoming increasingly prevalent. The available treatment options only provide symptomatic relief and do not cure the underlying cause of the disease. Therefore, it has become imperative to discover new markers and therapies to modulate the course of disease progression and develop better treatment options for the affected individuals. Growing evidence indicates that neuroinflammation is a common factor and one of the main inducers of neuronal damage and degeneration. Galectins (Gals) are a class of β-galactoside-binding proteins (lectins) ubiquitously expressed in almost all vital organs. Gals modulate various cellular responses and regulate significant biological functions, including immune response, proliferation, differentiation, migration, and cell growth, through their interaction with glycoproteins and glycolipids. In recent years, extensive research has been conducted on the Gal superfamily, with Gal-1, Gal-3, and Gal-9 in prime focus. Their roles have been described in modulating neuroinflammation and neurodegenerative processes. In this review, we discuss the role of Gals in the causation and progression of neurodegenerative disorders. We describe the role of Gals in microglia and astrocyte modulation, along with their pro- and anti-inflammatory functions. In addition, we discuss the potential use of Gals as a novel therapeutic target for neuroinflammation and restoring tissue damage in neurodegenerative diseases.

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Genetic variants in LGALS3 are related to lower galectin-3 serum levels and clinical outcomes in systemic sclerosis patients: A case-control study

Cited by 7 publications

References 55 publications

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

Variants Tagging LGALS-3 Haplotype Block in Association with First Myocardial Infarction and Plasma Galectin-3 Six Months after the Acute Event

Galectins—Potential Therapeutic Targets for Neurodegenerative Disorders

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