Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model

Xie, Yaxue; Shi, Hongshuo; Han, Bo

doi:10.1186/s12887-023-03896-4

Cited by 18 publications

(8 citation statements)

References 87 publications

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“…The RF method was applied in several diseases in previous studies ( 54 , 55 ). Other machine learning methods, such as LASSO and SVM algorithm were also used to construct diagnosis or prognosis models ( 56 ). The translation of clinical applications is vital.…”

Section: Discussionmentioning

confidence: 99%

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Yu,

Zhang,

Yang

et al. 2024

Front. Cardiovasc. Med.

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BackgroundCalcific aortic valve disease (CAVD) is one of the most prevalent valvular diseases and is the second most common cause for cardiac surgery. However, the mechanism of CAVD remains unclear. This study aimed to investigate the role of pyroptosis-related genes in CAVD by performing comprehensive bioinformatics analysis.MethodsThree microarray datasets (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the Gene Expression Omnibus (GEO) database. Pyroptosis-related differentially expressed genes (DEGs) were identified between the calcified and the normal valve samples. LASSO regression and random forest (RF) machine learning analyses were performed to identify pyroptosis-related DEGs with diagnostic value. A diagnostic model was constructed with the diagnostic candidate pyroptosis-related DEGs. Receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic performances of the diagnostic model and the individual diagnostic candidate genes in the training and validation cohorts. CIBERSORT analysis was performed to estimate the differences in the infiltration of the immune cell types. Pearson correlation analysis was used to investigate associations between the diagnostic biomarkers and the immune cell types. Immunohistochemistry was used to validate protein concentration.ResultsWe identified 805 DEGs, including 319 down-regulated genes and 486 up-regulated genes. These DEGs were mainly enriched in pathways related to the inflammatory responses. Subsequently, we identified 17 pyroptosis-related DEGs by comparing the 805 DEGs with the 223 pyroptosis-related genes. LASSO regression and RF algorithm analyses identified three CAVD diagnostic candidate genes (TREM1, TNFRSF11B, and PGF), which were significantly upregulated in the CAVD tissue samples. A diagnostic model was constructed with these 3 diagnostic candidate genes. The diagnostic model and the 3 diagnostic candidate genes showed good diagnostic performances with AUC values >0.75 in both the training and the validation cohorts based on the ROC curve analyses. CIBERSORT analyses demonstrated positive correlation between the proportion of M0 macrophages in the valve tissues and the expression levels of TREM1, TNFRSF11B, and PGF.ConclusionThree pyroptosis-related genes (TREM1, TNFRSF11B and PGF) were identified as diagnostic biomarkers for CAVD. These pyroptosis genes and the pro-inflammatory microenvironment in the calcified valve tissues are potential therapeutic targets for alleviating CAVD.

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Section: Discussionmentioning

confidence: 99%

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Yu,

Zhang,

Yang

et al. 2024

Front. Cardiovasc. Med.

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“…Thicker edges indicated stronger correlation, while the sign represented the direction of correlation, with green indicating positive correlations and red indicating negative correlations. The Least Absolute Shrinkage and Selection Operator was employed to regularize the network, which shrinks some very small correlation coefficients to exactly zero, providing a more specific interpretation of correlations between nodes (Xie et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Network analysis of symptoms, physiological, psychological and environmental risk factors based on unpleasant symptom theory in patients with chronic heart failure

Wang,

Xu,

et al. 2024

Int J of Nursing Practice

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BackgroundSomatic symptoms and related factors in patients with chronic heart failure have been extensively researched. However, more insight into the complex interconnections among these constructs is needed, as most studies focus on them independently from each other.AimsThe aim of this study is to gain a comprehensive understanding of how somatic symptoms and related factors are interconnected among patients with chronic heart failure.MethodsA total of 379 patients were enrolled. Network analysis was used to explore the interconnections among the somatic symptoms and related risk factors.ResultsThe four core symptoms of chronic heart failure were daytime dyspnea, dyspnea when lying down, fatigue and difficulty sleeping. Within the network, the edge weights of depression–anxiety, subjective social support–objective social support, and subjective social support–social support availability were more significant than others. Among physiological, psychological and environmental factors, the edge weights of NYHA–dyspnea, depression–difficulty sleeping, and social support availability–dyspnea when lying down were more significant than others. Depression and anxiety had the highest centrality, indicating stronger and closer connections with other nodes.ConclusionsPsychological and environmental factors stood out in the network, suggesting the potential value of interventions targeting these factors to improve overall health.

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“…In order to optimize our model more comprehensively and systematically, we have also tried to consider elastic net. Elastic net can combine L1 and L2 penalties and avoid some of the instability issues seen on Lasso for correlated predictors ( Li et al, 2021 , 2022 ; Xie et al, 2023 ). In addition, we used the GSE28146 dataset for external data validation of the model.…”

Section: Discussionmentioning

confidence: 99%

Constructing a prognostic risk model for Alzheimer’s disease based on ferroptosis

Wang¹,

Zhai²,

Li³

et al. 2023

Front. Aging Neurosci.

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IntroductionThe aim of this study is to establish a prognostic risk model based on ferroptosis to prognosticate the severity of Alzheimer’s disease (AD) through gene expression changes.MethodsThe GSE138260 dataset was initially downloaded from the Gene expression Omnibus database. The ssGSEA algorithm was used to evaluate the immune infiltration of 28 kinds of immune cells in 36 samples. The up-regulated immune cells were divided into Cluster 1 group and Cluster 2 group, and the differences were analyzed. The LASSO regression analysis was used to establish the optimal scoring model. Cell Counting Kit-8 and Real Time Quantitative PCR were used to verify the effect of different concentrations of Aβ1–42 on the expression profile of representative genes in vitro.ResultsBased on the differential expression analysis, there were 14 up-regulated genes and 18 down-regulated genes between the control group and Cluster 1 group. Cluster 1 and Cluster 2 groups were differentially analyzed, and 50 up-regulated genes and 101 down-regulated genes were obtained. Finally, nine common differential genes were selected to establish the optimal scoring model. In vitro, CCK-8 experiments showed that the survival rate of cells decreased significantly with the increase of Aβ1–42 concentration compared with the control group. Moreover, RT-qPCR showed that with the increase of Aβ1–42 concentration, the expression of POR decreased first and then increased; RUFY3 was firstly increased and then decreased.DiscussionThe establishment of this research model can help clinicians make decisions on the severity of AD, thus providing better guidance for the clinical treatment of Alzheimer’s disease.

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Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model

Cited by 18 publications

References 87 publications

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Network analysis of symptoms, physiological, psychological and environmental risk factors based on unpleasant symptom theory in patients with chronic heart failure

Constructing a prognostic risk model for Alzheimer’s disease based on ferroptosis

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