Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Wu, Li-Da; Li, Feng; Chen, Jiayi; Zhang, Jie; Qian, Lingling; Wang, Ruxing

doi:10.1186/s12920-022-01212-0

Cited by 13 publications

(12 citation statements)

References 72 publications

(62 reference statements)

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“…And atrial electrophysiology and structural substrates can be altered by mediators of the inflammatory response, thus increasing the risk of atrial fibrillation [29]. Furthermore, several clinical studies have demonstrated increased infiltration of proinflammatory immune cells in the atrial myocardium of AF patients [30]. Bhat et al demonstrated that the neutrophil-to-lymphocyte (NLR) ratio was an independent predictor of outcomes in patients with stable coronary artery disease and an independent predictor of prognosis in patients with acute coronary syndromes [31].…”

Section: Discussionmentioning

confidence: 99%

The Nine RNA Methylation Regulatory Gene Signature Is Associated with the Pathogenesis of Atrial Fibrillation by Modulating the Immune Microenvironment in the Atrial Tissues

Wang

Zhang

et al. 2023

Disease Markers

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Background. Atrial fibrillation (AF) is the most common type of cardiac arrhythmias and a major cause of cardiovascular disease (CVD)-related deaths globally. RNA methylation is the most frequent posttranscriptional modification in the eukaryotic RNAs. Previous studies have demonstrated close associations between the status of RNA methylation and CVD. Methods. We comprehensively evaluated the relationship between RNA methylation and AF. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to establish a risk score model in AF. Biological functional analysis was used to explore the relationship between RNA methylation related signatures and immune microenvironment characteristics. Machine learning was used to recognize the outstanding RNA methylation regulators in AF. Results. There was a significant variant of the mRNA expression of RNA methylation regulators in AF. RNA methylation related risk score could predict the onset of AF and closely associated with immune microenvironment features. XG-Boost algorithm and SHAP recognized that NSUN3 and DCPS might play a key role in the development of AF. Meanwhile, NSUN3 and DCPS had potential diagnostic value in AF. Conclusion. RNA methylation regulatory genes are associated with the onset of AF by modulating the immune microenvironment. The nine AF risk-related RNA methylation regulatory gene signature is a potential diagnostic biomarker and therapeutic target for AF.

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Section: Discussionmentioning

confidence: 99%

The Nine RNA Methylation Regulatory Gene Signature Is Associated with the Pathogenesis of Atrial Fibrillation by Modulating the Immune Microenvironment in the Atrial Tissues

Wang

Zhang

et al. 2023

Disease Markers

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“…40 An example of this is quantifying biomarkers for disease states in tissues. [41][42][43] In the aforementioned diagnostics context, each of the masked pixels taken from the 4 × 4 MM is treated as an instance in our dataset. Each instance has 16 features, with each feature corresponding to one of the 16 channels of the MM.…”

Section: Artificial Intelligence and Machine Learning Techniquesmentioning

confidence: 99%

“…Another common task is regression, in which the model typically predicts continuous, numerical values instead of discrete labels 40 . An example of this is quantifying biomarkers for disease states in tissues 41 – 43 …”

Section: Background and Related Workmentioning

confidence: 99%

Polarimetric imaging for cervical pre-cancer screening aided by machine learning: ex vivo studies

Robinson,

Hoong,

Kleijn

et al. 2023

J. Biomed. Opt.

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.SignificanceWide-field imaging Mueller polarimetry is an optical imaging technique that has great potential to become a reliable, fast, non-contact in vivo imaging modality for the early detection of, e.g., deceases and tissue structural malformations, such as cervical intraepithelial neoplasia, in both clinical and low-resource settings. On the other hand, machine learning methods have established themselves as a superior solution in image classification and regression tasks. We combine Mueller polarimetry and machine learning, critically assess the data/classification pipeline, investigate the bias arising from training strategies, and demonstrate how higher levels of detection accuracy can be achieved.AimWe aim to automate/assist with diagnostic segmentation of polarimetric images of uterine cervix specimens.ApproachA comprehensive capture-to-classification pipeline is developed in house. Specimens are acquired and measured with imaging Mueller polarimeter and undergo histopathological classification. Subsequently, a labeled dataset is created within tagged regions of either healthy or neoplastic cervical tissues. Several machine learning methods are trained utilizing different training-test-set-split strategies, and their corresponding accuracies are compared.ResultsOur results include robust measurements of model performance with two approaches: a 90:10 training–test-set-split and leave-one-out cross-validation. By comparing the classifier’s accuracy directly with the ground truth obtained during histology analysis, we demonstrate how conventionally used shuffled split leads to an over-estimate of true classifier performance ( 0.964 ± 0.00 ) . The leave-one-out cross-validation, however, leads to more accurate performance ( 0.812 ± 0.21 ) with respect to newly obtained samples that were not used to train the models.ConclusionsCombination of Mueller polarimetry and machine learning is a powerful tool for the task of screening for pre-cancerous conditions in cervical tissue sections. Nevertheless, there is a inherent bias with conventional processes that can be addressed using more conservative classifier training approaches. This results in overall improvements of the sensitivity and specificity of the developed techniques for “unseen” images.

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“…Medicine is one of the earliest applications of AI, including disease diagnosis and the selection of the best surgical procedures ( Goyal et al, 2022 ). Machine learning is an important branch of artificial intelligence and has been widely used in screening characteristic genes and risk factors of diseases ( Dai et al, 2022 ; Liu et al, 2022 ; Wu et al, 2022 ). We also used machine learning methods to screen characteristic genes in subgroups in an attempt to correlate gene expression profiles with clinical features in patients with DCM.…”

Section: Introductionmentioning

confidence: 99%

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

Weng

2023

Front. Genet.

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Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM.Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with “limma” package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the “Consensus Cluster Plus” package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup‐specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve.Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness.Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

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Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation

Cited by 13 publications

References 72 publications

The Nine RNA Methylation Regulatory Gene Signature Is Associated with the Pathogenesis of Atrial Fibrillation by Modulating the Immune Microenvironment in the Atrial Tissues

The Nine RNA Methylation Regulatory Gene Signature Is Associated with the Pathogenesis of Atrial Fibrillation by Modulating the Immune Microenvironment in the Atrial Tissues

Polarimetric imaging for cervical pre-cancer screening aided by machine learning: ex vivo studies

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

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