Galectin-2 3279TT variant protects against the lymphotoxin-α 252GG genotype associated ischaemic stroke

Szolnoki, Zoltán; Maász, Anita; Magyari, Lili; Horvatovich, Katalin; Faragó, Bernadett; Kondacs, András; Bodor, Anita; Hadarits, Ferenc; Orosz, P.; Ille, Alexandru; Melegh, Béla

doi:10.1016/j.clineuro.2008.09.027

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Such interactions between genes are known to play an important role in the development of human diseases, and recently a number of gene–gene interactions have been identified as determinants of several different diseases, including prostate cancer (Cooper et al 2008), age-related macular degeneration (Seitsonen et al 2008), ischemic stroke (Szolnoki et al 2009), and Alzheimer’s disease (Infante et al 2010). …”

Section: Discussionmentioning

confidence: 99%

Partial correlation network analyses to detect altered gene interactions in human disease: using preeclampsia as a model

et al. 2010

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Differences in gene expression between cases and controls have been identified for a number of human diseases. However, the underlying mechanisms of transcriptional regulation remain largely unknown. Beyond comparisons of absolute or relative expression levels, disease states may be associated with alterations in the observed correlational patterns among sets of genes. Here we use partial correlation networks aiming to compare the transcriptional co-regulation for 222 genes that are differentially expressed in decidual tissues between preeclampsia (PE) cases and non-PE controls. Partial correlation coefficients (PCCs) have been calculated in cases (N = 37) and controls (N = 58) separately. For all PCCs, we tested if they were significant non-zero in the cases and controls separately. In addition, to examine if a given PCC is different between the cases and controls, we tested if the difference between two PCCs were significant non-zero. In the group with PE cases, only five PCCs were significant (FDR p value ≤ 0.05), of which none were significantly different from the PCCs in the controls. However, in the controls we identified a total of 56 statistically significant PCCs (FDR p value ≤ 0.05), of which 31 were also significantly different (FDR p value ≤ 0.05) from the PCCs in the PE cases. The identified partial correlation networks included genes that are potentially relevant for developing PE, including both known susceptibility genes (EGFL7, HES1) and novel candidate genes (CFH, NADSYN1, DBP, FIGLA). Our results might suggest that disturbed interactions, or higher order relationships between these genes play an important role in developing the disease.

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Section: Discussionmentioning

confidence: 99%

Partial correlation network analyses to detect altered gene interactions in human disease: using preeclampsia as a model

et al. 2010

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Overview of Animal Galectins: Proto-Type Subfamily

Gupta

2012

Animal Lectins: Form, Function and Clinical Applications

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Genetic association between inflammatory genes (IL-1α, CD14, LGALS2, PSMA6) and risk of ischemic stroke: A meta-analysis

Misra

Kumar

et al. 2016

Meta Gene

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BackgroundSequence variations in genes involved in inflammatory system are known to contribute to the risk of cerebrovascular diseases (CVD) including stroke. Very few number of studies have been published in the context of the association between Interleukin-1α(IL-1α), CD14 cell surface glycoprotein (CD14), Galectin-2-encoding gene (LGALS2)and proteasome subunit type 6 (PSMA6) gene polymorphisms with susceptibility to ischemic stroke (IS).ObjectiveThe present meta-analysis aimed to provide a comprehensive account of the association between IL-1α (-C889T and -C511T), CD14 (-C159T), LGALS2 (-C3279T) and PSMA6 (-C8G) gene polymorphisms and susceptibility to IS.MethodsA literature search for eligible genetic studies published before August 31, 2015 was conducted in the PubMed, Medline, EMBASE, OVID, and Google Scholar databases. Fixed or random effects models were used to estimate the Pooled Odds ratio (OR) and 95% confidence interval (CI) using RevMan 5.3 software.ResultsTotal 21 studies were included in our meta-analysis. No significant association was observed between IL-1α (-C889T) [OR = 1.18, 95% CI: 0.67–2.08, P = 0.58], IL-1α (-C511T) [OR = 0.95, 95% CI: 0.66–1.37, P = 0.77], LGALS2(-C2379T) [OR = 0.29, 95% CI: 0.02–4.26, P = 0.37] and CD14 (-C260T) [OR = 0.93, 95% CI: 0.77–1.11, P = 0. 42] gene polymorphisms and risk of IS. However, protective level of association was observed between PSMA6 (-C8G) gene polymorphism and susceptibility to IS under the recessive model [OR = 0.25, 95% CI: 0.08–0.72, P = 0.01].ConclusionOur meta-analysis shows that IL-1α (-C889T and -C511T), CD14 (-C159T), LGALS2 (-C3279T) and gene polymorphisms are not significantly associated with the risk of IS while PSMA6 (-C8G) gene polymorphism may play a protective role with the susceptibility of IS. Further prospective large epidemiological studies are needed to confirm these findings in different populations.

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Galectin-2 3279TT variant protects against the lymphotoxin-α 252GG genotype associated ischaemic stroke

Cited by 5 publications

References 18 publications

Partial correlation network analyses to detect altered gene interactions in human disease: using preeclampsia as a model

Partial correlation network analyses to detect altered gene interactions in human disease: using preeclampsia as a model

Overview of Animal Galectins: Proto-Type Subfamily

Genetic association between inflammatory genes (IL-1α, CD14, LGALS2, PSMA6) and risk of ischemic stroke: A meta-analysis

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