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      Cytokine TGFβ Gene Polymorphism in Asthma: TGF-Related SNP Analysis Enhances the Prediction of Disease Diagnosis (A Case-Control Study With Multivariable Data-Mining Model Development)

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          Abstract

          Introduction

          TGF-β and its receptors play a crucial role in asthma pathogenesis and bronchial remodeling in the course of the disease. TGF-β1, TGF-β2, and TGF-β3 isoforms are responsible for chronic inflammation, bronchial hyperreactivity, myofibroblast activation, fibrosis, bronchial remodeling, and change the expression of approximately 1000 genes in asthma. TGF-β SNPs are associated with the elevated plasma level of TGF-β1, an increased level of total IgE, and an increased risk of remodeling of bronchi.

          Methods

          The analysis of selected TGF-β1, TGF-β2, TGF-β3-related single-nucleotide polymorphisms (SNP) was conducted on 652 DNA samples with an application of the MassARRAY ® using the mass spectrometry (MALDI-TOF MS). Dataset was randomly split into training (80%) and validation sets (20%). For both asthma diagnosis and severity prediction, the C5.0 modelling with hyperparameter optimization was conducted on: clinical and SNP data (Clinical+TGF), only clinical (OnlyClinical) and minimum redundancy feature selection set (MRMR). Area under ROC (AUCROC) curves were compared using DeLong’s test.

          Results

          Minor allele carriers (MACs) in SNP rs2009112 [OR=1.85 (95%CI:1.11-3.1), p=0.016], rs2796821 [OR=1.72 (95%CI:1.1-2.69), p=0.017] and rs2796822 [OR=1.71 (95%CI:1.07-2.71), p=0.022] demonstrated an increased odds of severe asthma. Clinical+TGF model presented better diagnostic potential than OnlyClinical model in both training (p=0.0009) and validation (AUCROC=0.87 vs. 0.80,p=0.0052). At the same time, the MRMR model was not worse than the Clinical+TGF model (p=0.3607 on the training set, p=0.1590 on the validation set), while it was better in comparison with the Only Clinical model (p=0.0010 on the training set, p=0.0235 on validation set, AUCROC=0.85 vs. 0.87). On validation set Clinical+TGF model allowed for asthma diagnosis prediction with 88.4% sensitivity and 73.8% specificity.

          Discussion

          Derived predictive models suggest the analysis of selected SNPs in TGF-β genes in combination with clinical factors could predict asthma diagnosis with high sensitivity and specificity, however, the benefit of SNP analysis in severity prediction was not shown.

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          Most cited references26

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          Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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            MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA

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              Signaling Receptors for TGF-β Family Members.

              Transforming growth factor β (TGF-β) family members signal via heterotetrameric complexes of type I and type II dual specificity kinase receptors. The activation and stability of the receptors are controlled by posttranslational modifications, such as phosphorylation, ubiquitylation, sumoylation, and neddylation, as well as by interaction with other proteins at the cell surface and in the cytoplasm. Activation of TGF-β receptors induces signaling via formation of Smad complexes that are translocated to the nucleus where they act as transcription factors, as well as via non-Smad pathways, including the Erk1/2, JNK and p38 MAP kinase pathways, and the Src tyrosine kinase, phosphatidylinositol 3'-kinase, and Rho GTPases.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                14 June 2022
                2022
                : 13
                : 746360
                Affiliations
                [1] 1 Department of Internal Medicine, Asthma and Allergy of The Medical University of Lodz, Medical University of Lodz , Lodz, Poland
                [2] 2 Department of Biostatistics and Translational Medicine of The Medical University of Lodz, Medical University of Lodz , Lodz, Poland
                Author notes

                Edited by: Michael Kirschfink, Heidelberg University, Germany

                Reviewed by: Rocio Teresa Martinez-Nunez, King’s College London, United Kingdom; Heidi Makrinioti, Chelsea and Westminster Hospital NHS Foundation Trust, United Kingdom

                *Correspondence: Michał Panek, michalmp@ 123456poczta.onet.pl

                This article was submitted to Cytokines and Soluble Mediators in Immunity, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2022.746360
                9238410
                35774789
                53a4975e-2a83-40eb-a3b4-a54c2c3f44cf
                Copyright © 2022 Panek, Stawiski, Kaszkowiak and Kuna

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 July 2021
                : 13 May 2022
                Page count
                Figures: 4, Tables: 5, Equations: 0, References: 26, Pages: 13, Words: 6987
                Funding
                Funded by: Polpharma Scientific Foundation , doi 10.13039/501100010408;
                Categories
                Immunology
                Original Research

                Immunology
                tgf — transforming growth factor,asthma,inflammation,prediction,development
                Immunology
                tgf — transforming growth factor, asthma, inflammation, prediction, development

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