Evaluation of the association between KIR polymorphisms and systemic sclerosis: a meta-analysis

Karimizadeh, Elham; Mostafaei, Shayan; Aslani, Saeed; Gharibdoost, Farhad; Xavier, Ricardo Machado; Salim, Patrícia Hartstein; Kavosi, Hoda; Farhadi, Elham; Mahmoudi, Mahdi

doi:10.1186/s42358-019-0107-3

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…Recent findings indicate that fever-inducing KIR3DL1+ SSc individuals express the HLA-Bw4 inhibitory ligand compared to KIR3DL1+ controls [41]. This discovery could elucidate the direct interplay between the composite of HLA class I and the function of NK cells, which directly correlates with an increased risk of SSc development, although some studies in the literature contradict this assertion [43,44].…”

Section: Major Histocompatibility Complex Region Hla Genesmentioning

confidence: 96%

Immunogenetics of Systemic Sclerosis

Gumkowska-Sroka,

Kotyla,

Kotyla

2024

Genes

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Systemic sclerosis (SSc) is a rare autoimmune connective tissue disorder characterized by massive fibrosis, vascular damage, and immune imbalance. Advances in rheumatology and immunology over the past two decades have led to a redefinition of systemic sclerosis, shifting from its initial perception as primarily a “hyperfibrotic” state towards a recognition of systemic sclerosis as an immune-mediated disease. Consequently, the search for genetic markers has transitioned from focusing on fibrotic mechanisms to exploring immune regulatory pathways. Immunogenetics, an emerging field at the intersection of immunology, molecular biology, and genetics has provided valuable insights into inherited factors that influence immunity. Data from genetic studies conducted thus far indicate that alterations in genetic messages can significantly impact disease risk and progression. While certain genetic variations may confer protective effects, others may exacerbate disease susceptibility. This paper presents a comprehensive review of the most relevant genetic changes that influence both the risk and course of systemic sclerosis. Special emphasis is placed on factors regulating the immune response, recognizing their pivotal role in the pathogenesis of the disease.

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Section: Major Histocompatibility Complex Region Hla Genesmentioning

confidence: 96%

Immunogenetics of Systemic Sclerosis

Gumkowska-Sroka,

Kotyla,

Kotyla

2024

Genes

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“…In this line, according to a recent meta-analysis, the highly polymorphic KIR gene cluster is not likely to largely contribute to SSc on its own [44]. However, Hanson et al [45 ▪ ] very recently conducted a large analysis of KIR-HLA epistatic interactions in SSc.…”

Section: New Insights Into the Human Leukocyte Antigen Region Reveal ...mentioning

confidence: 99%

Recent advances in elucidating the genetic basis of systemic sclerosis

Villanueva-Martín

Martı́n

Bossini‐Castillo

2022

Current Opinion in Rheumatology

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Purpose of reviewSystemic sclerosis (SSc) is a complex autoimmune disorder that affects the connective tissue and causes severe vascular damage and fibrosis of the skin and internal organs. There are recent advances in the field that apply novel methods to high throughput genotype information of thousands of patients with SSc and provide promising results towards the use of genomic data to help SSc diagnosis and clinical care.Recent findingsThis review addresses the development of the first SSc genomic risk score, which can contribute to differentiating SSc patients from healthy controls and other immune-mediated diseases. Moreover, we explore the implementation of data mining strategies on the results of genome-wide association studies to highlight subtype-specific HLA class II associations and a strong association of the HLA class I locus with SSc for the first time. Finally, the combination of genomic data with transcriptomics informed drug repurposing and genetic association studies in well characterized SSc patient cohorts identified markers of severe complications of the disease.SummaryEarly diagnosis and clinical management of SSc and SSc-related complications are still challenging for rheumatologists. The development of predictive models and tools using genotype data may help to finally deliver personalized clinical care and treatment for patients with SSc in the near future.

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“…Firstly, this approach allows us to directly compare the efficacy of the new ML predictive model with the entropy analysis employed in a previous study ( Melis et al, 2019 ). Secondly, the decision to include the KIR genes in the analysis is supported by recent publications suggesting that NK cell-mediated immunity may actively regulate MS evolution through different combinations of KIR/HLA haplotypes ( Kaur et al, 2013 ; Bettencourt et al, 2014 ; Shahsavar et al, 2016 ; Karimizadeh et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Decision trees to evaluate the risk of developing multiple sclerosis

Pasella,

Pisano,

Cannas

et al. 2023

Front. Neuroinform.

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IntroductionMultiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure for MS, numerous treatments exist to address symptoms, decelerate disease progression, and enhance the quality of life for individuals with MS.MethodsThis paper introduces a novel machine learning (ML) algorithm utilizing decision trees to address a key objective: creating a predictive tool for assessing the likelihood of MS development. It achieves this by combining prevalent demographic risk factors, specifically gender, with crucial immunogenetic risk markers, such as the alleles responsible for human leukocyte antigen (HLA) class I molecules and the killer immunoglobulin-like receptors (KIR) genes responsible for natural killer lymphocyte receptors.ResultsThe study included 619 healthy controls and 299 patients affected by MS, all of whom originated from Sardinia. The gender feature has been disregarded due to its substantial bias in influencing the classification outcomes. By solely considering immunogenetic risk markers, the algorithm demonstrates an ability to accurately identify 73.24% of MS patients and 66.07% of individuals without the disease.DiscussionGiven its notable performance, this system has the potential to support clinicians in monitoring the relatives of MS patients and identifying individuals who are at an increased risk of developing the disease.

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Evaluation of the association between KIR polymorphisms and systemic sclerosis: a meta-analysis

Cited by 3 publications

References 39 publications

Immunogenetics of Systemic Sclerosis

Immunogenetics of Systemic Sclerosis

Recent advances in elucidating the genetic basis of systemic sclerosis

Decision trees to evaluate the risk of developing multiple sclerosis

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