Pyroptosis-Related Gene Signature and Expression Patterns in the Deterioration of Atherosclerosis

Wu, Yan; Ma, Qi; Wang, Xudong; Wei, Tianci; Tian, Jiawei; Zhang, Wenjing

doi:10.1155/2022/1356618

Cited by 3 publications

(4 citation statements)

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“…With the development of biology and informatics, bioinformatics was an essential tool to identify the potential diagnostic and prognosis biomarkers in numerous diseases. For example, Wu et al found a potential pyroptosis‐related gene signature in atherosclerosis 11 . The present study utilised bioinformatics analysis and identify pyroptosis‐related genes and explore the diagnostic value in ICM.…”

Section: Introductionmentioning

confidence: 97%

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An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy

Jin,

Liu,

Zhang

et al. 2023

J Cellular Molecular Medi

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Pyroptosis is involved in ischemic cardiomyopathy (ICM). The study aimed to investigate the pyroptosis‐related genes and clarify their diagnostic value in ICM. The bioinformatics method identified the differential pyroptosis genes between the normal control and ICM samples from online datasets. Then, protein–protein interaction (PPI) and function analysis were carried out to explore the function of these genes. Following, subtype analysis was performed using ConsensusClusterPlus, functions, immune score, stromal score, immune cell proportion and human leukocyte antigen (HLA) genes between subtypes were investigated. Moreover, optimal pyroptosis genes were selected using the least absolute shrinkage and selection operator (LASSO) analysis to construct a diagnostic model and evaluate its effectiveness using receiver operator characteristic (ROC) analysis. Twenty‐one differential expressed pyroptosis genes were identified, and these genes were related to immune and pyroptosis. Subtype analysis identified two obvious subtypes: sub‐1 and sub‐2. And LASSO identified 13 optimal genes used to construct the diagnostic model. The diagnostic model in ICM diagnosis with the area under ROC (AUC) was 0.965. Our results suggested that pyroptosis was tightly associated with ICM.

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Section: Introductionmentioning

confidence: 97%

“…For example, Wu et al found a potential pyroptosis‐related gene signature in atherosclerosis. 11 The present study utilised bioinformatics analysis and identify pyroptosis‐related genes and explore the diagnostic value in ICM.…”

Section: Introductionmentioning

confidence: 99%

An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy

Jin,

Liu,

Zhang

et al. 2023

J Cellular Molecular Medi

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“…17 In general, emerging assays and new approaches for studying complex diseases have enabled more extensive systems biology characterization of cell death. [18][19][20][21] While recent work has characterized disease-specific transcriptome differences [22][23][24] , pyroptosis has not been characterized via other systems biology views including high-content imaging and secretome.…”

Section: Introductionmentioning

confidence: 99%

A morphology and secretome map of pyroptosis

Lippincott,

Tomkinson,

Bunten

et al. 2024

Preprint

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Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1β processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and healthy cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.

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“…Patients with SSc and apparent cardiovascular symptoms are at increased risk of deterioration and early death from cardiovascular causes, accounting for approximately 20%–30% of all SSc deaths ( Mok and Lau, 2010 ). Atherosclerosis (AS) is the dominant pathological cause of CVD and can significantly affect the morbidity and mortality of SSc patients ( Wu et al, 2022 ). The incidence of AS in patients with SSc was reported to be 56% ( Pagkopoulou et al, 2023 ) as SSc is an independent risk factor that can accelerate AS ( Mok et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Relationships between systemic sclerosis and atherosclerosis: screening for mitochondria-related biomarkers

Wang,

Lyu,

Qin

et al. 2024

Front. Genet.

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BackgroundPatients with systemic sclerosis (SSc) are known to have higher incidence of atherosclerosis (AS). Mitochondrial injuries in SSc can cause endothelial dysfunction, leading to AS; thus, mitochondria appear to be hubs linking SSc to AS. This study aimed to identify the mitochondria-related biomarkers of SSc and AS.MethodsWe identified common differentially expressed genes (DEGs) in the SSc (GSE58095) and AS (GSE100927) datasets of the Gene Expression Omnibus (GEO) database. Considering the intersection between genes with identical expression trends and mitochondrial genes, we used the least absolute shrinkage and selection operator (LASSO) as well as random forest (RF) algorithms to identify four mitochondria-related hub genes. Diagnostic nomograms were then constructed to predict the likelihood of SSc and AS. Next, we used the CIBERSORT algorithm to evaluate immune infiltration in both disorders, predicted the transcription factors for the hub genes, and validated these genes for the two datasets.ResultsA total of 112 genes and 13 mitochondria-related genes were identified; these genes were then significantly enriched for macrophage differentiation, collagen-containing extracellular matrix, collagen binding, antigen processing and presentation, leukocyte transendothelial migration, and apoptosis. Four mitochondria-related hub DEGs (IFI6, FSCN1, GAL, and SGCA) were also identified. The nomograms showed good diagnostic values for GSE58095 (area under the curve (AUC) = 0.903) and GSE100927 (AUC = 0.904). Further, memory B cells, γδT cells, M0 macrophages, and activated mast cells were significantly higher in AS, while the resting memory CD4+ T cells were lower and M1 macrophages were higher in SSc; all of these were closely linked to multiple immune cells. Gene set enrichment analysis (GSEA) showed that IFI6 and FSCN1 were involved in immune-related pathways in both AS and SSc; GAL and SGCA are related to mitochondrial metabolism pathways in both SSc and AS. Twenty transcription factors (TFs) were predicted, where two TFs, namely BRCA1 and PPARγ, were highly expressed in both SSc and AS.ConclusionFour mitochondria-related biomarkers were identified in both SSc and AS, which have high diagnostic value and are associated with immune cell infiltration in both disorders. Hence, this study provides new insights into the pathological mechanisms underlying SSc and AS. The specific roles and action mechanisms of these genes require further clinical validation in SSc patients with AS.

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Pyroptosis-Related Gene Signature and Expression Patterns in the Deterioration of Atherosclerosis

Cited by 3 publications

References 54 publications

An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy

An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy

A morphology and secretome map of pyroptosis

Relationships between systemic sclerosis and atherosclerosis: screening for mitochondria-related biomarkers

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