Dataset Information

Molecular Markers Distinguishing T Cell Subtypes With TSDR Strand-Bias Methylation.

ABSTRACT: Human regulatory CD4⁺CD25⁺FOXP3⁺ T cells (Treg) play important roles in the maintenance of self-tolerance and immune homeostasis in various disease settings and are also involved in the suppression of effective immune responses. These cells are heterogeneous in phenotype and function, and the ability to reliably distinguish between various FOXP3-expressing subpopulations can affect the development of successful therapies. This study demonstrates that hypomethylated CpG sites, present in four regions of the FOXP3 locus, CAMTA1 and FUT7 gene regions, can be used to distinguish several subsets of Treg from conventional CD4⁺ T lymphocytes (Tcon) in donors of both genders. We describe a previously unreported strand-bias hemimethylation pattern in FOXP3 promoter and TSDR in donors of both genders, with the coding strand being demethylated within promoter and methylated within TSDR in all CD4⁺ lymphocyte subtypes, whereas the template strand follows the previously described pattern of methylation with both regions being more demethylated in Treg subtypes and mostly methylated in Tcon. This strand-specific approach within the TSDR may prove to be instrumental in correctly defining Treg subsets in health and in disease.

SUBMITTER: Minskaia E

PROVIDER: S-EPMC6230625 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Molecular Markers Distinguishing T Cell Subtypes With TSDR Strand-Bias Methylation.

Minskaia Ekaterina E Saraiva Barbara C BC Soares Maria M V MMV Azevedo Rita I RI Ribeiro Ruy M RM Kumar Saumya D SD Vieira Ana I S AIS Lacerda João F JF

Frontiers in immunology 20181105

Human regulatory CD4+CD25+FOXP3+ T cells (Treg) play important roles in the maintenance of self-tolerance and immune homeostasis in various disease settings and are also involved in the suppression of effective immune responses. These cells are heterogeneous in phenotype and function, and the ability to reliably distinguish between various FOXP3-expressing subpopulations can affect the development of successful therapies. This study demonstrates that hypomethylat ...[more]

PMID: 30455694

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Project description:Purpose Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features. Methods One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters—four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features—were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance. Results Boruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. Conclusions The proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.

Dataset Information

Molecular Markers Distinguishing T Cell Subtypes With TSDR Strand-Bias Methylation.

Publications

Molecular Markers Distinguishing T Cell Subtypes With TSDR Strand-Bias Methylation.

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