Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics

You, Hongjun; Dong, Mengya

doi:10.2147/jir.s421196

Cited by 5 publications

(5 citation statements)

References 98 publications

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“…Our initial observations reveal that overexpression of CBLB significantly curtailed the degree of myocardial infarction in AMI mice, which is consistent with our hypothesis. This observation aligns with the research by Hongjun You et al [ 9 ], which identifies CBLB as an essential immune-inflammation-related biomarker of AMI, reporting a significant decrease in CBLB levels in AMI patients. Both findings mutually reinforce the role of CBLB in AMI, underscoring its significance.…”

Section: Discussionsupporting

confidence: 92%

“…Research has suggested that Cbl-b may serve as a potential biomarker for AMI, which provides evidence that Cbl-b expression is significantly downregulated in AMI patients compared to the control group, as demonstrated by both public database analysis and analysis of 20 peripheral blood samples [ 9 ]. These findings further underscore the potential role of Cbl-b in the pathogenesis and progression of AMI.…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has revealed specific changes in the gut microbiota and in vivo metabolites in AMI patients [ 9 – 11 ], providing new perspectives on AMI diagnosis and treatment. However, the role of Cbl-b in these alterations remains largely unexplored.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the role of CBLB in acute myocardial infarction: transcriptomic, microbiomic, and metabolomic analyses

You,

Chang,

Chen

et al. 2024

J Transl Med

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Background Specific alterations in gut microbiota and metabolites have been linked to AMI, with CBLB potentially playing an essential role. However, the precise interactions remain understudied, creating a significant gap in our understanding. This study aims to address this by exploring these interactions in CBLB-intervened AMI mice using transcriptome sequencing, 16 S rDNA, and non-targeted metabolite analysis. Methods To probe the therapeutic potential and mechanistic underpinnings of CBLB overexpression in AMI, we utilized an integrative multi-omics strategy encompassing transcriptomics, metabolomics, and 16s rDNA sequencing. We selected these particular methods as they facilitate a holistic comprehension of the intricate interplay between the host and its microbiota, and the potential effects on the host’s metabolic and gene expression profiles. The uniqueness of our investigation stems from utilizing a multi-omics approach to illuminate the role of CBLB in AMI, an approach yet unreported to the best of our knowledge. Our experimental protocol encompassed transfection of CBLB lentivirus-packaged vectors into 293T cells, followed by subsequent intervention in AMI mice. Subsequently, we conducted pathological staining, fecal 16s rDNA sequencing, and serum non-targeted metabolome sequencing. We applied differential expression analysis to discern differentially expressed genes (DEGs), differential metabolites, and differential microbiota. We performed protein-protein interaction analysis to identify core genes, and conducted correlation studies to clarify the relationships amongst these core genes, paramount metabolites, and key microbiota. Results Following the intervention of CBLB in AMI, we observed a significant decrease in inflammatory cell infiltration and collagen fiber formation in the infarcted region of mice hearts. We identified key changes in microbiota, metabolites, and DEGs that were associated with this intervention. The findings revealed that CBLB has a significant correlation with DEGs, differential metabolites and microbiota, respectively. This suggests it could play a pivotal role in the regulation of AMI. Conclusion This study confirmed the potential of differentially expressed genes, metabolites, and microbiota in AMI regulation post-CBLB intervention. Our findings lay groundwork for future exploration of CBLB’s role in AMI, suggesting potential therapeutic applications and novel research directions in AMI treatment strategies.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Exploring the role of CBLB in acute myocardial infarction: transcriptomic, microbiomic, and metabolomic analyses

You,

Chang,

Chen

et al. 2024

J Transl Med

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“…The LASSO algorithm, a regression analysis model capable of feature selection, has been used to identify biomarkers for various diseases. 24 , 25 The LASSO regression algorithm was implemented using the glmnet package in R software to identify candidate diagnostic biomarkers for distinguishing HCM and control samples. SVM-RFE is a supervised machine learning algorithm that combines a support vector machine approach with recursive feature elimination.…”

Section: Methodsmentioning

confidence: 99%

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

You,

Dong

2023

J Int Med Res

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Objectives Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM. Methods Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein–protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy. Results Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA–VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM. Conclusions RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.

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“…They observed that inflammation-related genes such as NR4A2 , IRAK3 , NFIL3 , IL1R2 , CLEC4E and BCL3AMI were highly up-regulated, indicating that inflammation is an important feature of MI. Another study employed a bioinformatic analysis to pinpoint eight key immuno-inflammation-related genes, namely SH2D1B , ADM , PI3 , MMP9 , NRG1 , CBLB , RORA , and FASLG , which have been identified as potential biomarkers for AMI ( 100 ). Zhuo et al ( 101 ) constructed an MI-related lncRNA-miRNA-mRNA network using RNA sequencing data and identified lncRNA SNHG8, hsa-miR-411-5p, SOCS3 and ICAM1 were key nodes in the network.…”

Section: Single-omics Approachesmentioning

confidence: 99%

From multi-omics approaches to personalized medicine in myocardial infarction

Zhan,

Tang,

et al. 2023

Front. Cardiovasc. Med.

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Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

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Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics

Cited by 5 publications

References 98 publications

Exploring the role of CBLB in acute myocardial infarction: transcriptomic, microbiomic, and metabolomic analyses

Exploring the role of CBLB in acute myocardial infarction: transcriptomic, microbiomic, and metabolomic analyses

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

From multi-omics approaches to personalized medicine in myocardial infarction

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