Asporin is a Potential Promising Biomarker for Common Heart Failure

Zhang, Kai; Wu, Min; Qin, Xianyu; Wen, Pengju; Wu, Yueheng; Zhuang, Jian

doi:10.1089/dna.2020.5995

Cited by 16 publications

(12 citation statements)

References 40 publications

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“…However, these studies did not conduct link between gene expression and DCM clinical characteristics. This study integrated 4 qualified DCM datasets from GEO into the RRA method to identify DCM-associated genes and develop expression-based molecular signatures to predict DCM, some of which, such as NPPB ( 29 ) and ASPN ( 30 ), have been reported to be biomarkers of DCM or play an important role in its pathogenesis. In addition, associations of developed signatures with the clinicopathological characteristics of DCM were also evaluated.…”

Section: Discussionmentioning

confidence: 99%

Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Huang

et al. 2021

Front. Cardiovasc. Med.

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Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary.Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS).Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen–containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients.Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.

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Section: Discussionmentioning

confidence: 99%

Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Huang

et al. 2021

Front. Cardiovasc. Med.

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“…Interestingly, ASPN increased H9C2 cardiomyocyte apoptosis with downregulation of Bcl-2, upregulation of transforming growth factor-β1, Bax, collagen III, fibronectin, and phosphorylation of smad2 and smad3 (24). It indicates that the role of ASPN in cardiomyocyte has been preliminarily confirmed, and ASPN may be a potential promising biomarker for heart failure (25). Excitingly, interleukin (IL)-10 in cardiovascular disease has been widely studied.…”

Section: Discussionmentioning

confidence: 95%

Integrated Bioinformatics Algorithms and Experimental Validation to Explore Robust Biomarkers and Landscape of Immune Cell Infiltration in Dilated Cardiomyopathy

Zhang

Fan

Cao

et al. 2022

Front. Cardiovasc. Med.

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BackgroundThe etiology of dilated cardiomyopathy (DCM) is unclear. Bioinformatics algorithms may help to explore the underlying mechanisms. Therefore, we aimed to screen diagnostic biomarkers and identify the landscape of immune infiltration in DCM.MethodsFirst, the CIBERSORT algorithm was used to excavate the proportion of immune-infiltration cells in DCM and normal myocardial tissues. Meanwhile, the Pearson analysis and principal component analysis (PCA) were used to identify immune heterogeneity in different tissues. The Wilcoxon test, LASSO regression, and machine learning method were conducted to identify the hub immune cells. In addition, the differentially expressed genes (DEGs) were screened by the limma package, and DEGs were analyzed for functional enrichment. In the protein–protein interaction (PPI) network, multiple algorithms were used to calculate the score of each DEG for screening the hub genes. Subsequently, external datasets were used to further validate the expression of hub genes, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy. Finally, we examined the expression of hub biomarkers in animal models.ResultsA total of 108 DEGs were screened, and these genes may be related to biological processes such as cytolysis, positive regulation of cytokine secretion, etc. Two types of hub immune cells [activated natural killer (NK) cells and eosinophils] and four hub genes (ASPN, CD163, IL10, and LUM) were identified in DCM myocardial tissues. CD163 was verified to have the capability to diagnose DCM with the most excellent specificity and sensitivity. It is worth mentioning that the combined CD163 and eosinophils may have better diagnostic efficacy. Moreover, the correlation analysis showed CD163 was negatively correlated with activated NK cells. Finally, the results of the mice model also indicated that CD163 might be involved in the occurrence of DCM.ConclusionASPN, CD163, IL10, and LUM may have a potential predictive ability for DCM, and especially CD163 showed the most robust efficacy. Furthermore, activated NK cells and eosinophils may relate to the occurrence of DCM.

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“…In contrast, FCN3, CNN1, PCNT, and HOPX expressions were declined in ICM than controls. Previously, ASPN protein displayed increased expressions in ICM left ventricular tissues that are highly correlated to left ventricular ejection fraction ( Zhang et al, 2021 ). Moreover, ASPN expressions are upregulated in cardiac remodeling mouse models ( Wang et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Integrated Strategies of Diverse Feature Selection Methods Identify Aging-Based Reliable Gene Signatures for Ischemic Cardiomyopathy

Song

Chen

Zhang

et al. 2022

Front. Mol. Biosci.

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Objective: Ischemic cardiomyopathy (ICM) is a major cardiovascular state associated with prominently increased morbidity and mortality. Our purpose was to detect reliable gene signatures for ICM through integrated feature selection strategies.Methods: Transcriptome profiles of ICM were curated from the GEO project. Classification models, including least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest, were adopted for identifying candidate ICM-specific genes for ICM. Immune cell infiltrates were estimated using the CIBERSORT method. Expressions of candidate genes were verified in ICM and healthy myocardial tissues via Western blotting. JC-1 staining, flow cytometry, and TUNEL staining were presented in hypoxia/reoxygenation (H/R)-stimulated H9C2 cells with TRMT5 deficiency.Results: Following the integration of three feature selection methods, we identified seven candidate ICM-specific genes including ASPN, TRMT5, LUM, FCN3, CNN1, PCNT, and HOPX. ROC curves confirmed the excellent diagnostic efficacy of this combination of previous candidate genes in ICM. Most of them presented prominent interactions with immune cell infiltrates. Their deregulations were confirmed in ICM than healthy myocardial tissues. TRMT5 expressions were remarkedly upregulated in H/R-stimulated H9C2 cells. TRMT5 deficiency enhanced mitochondrial membrane potential and reduced apoptosis in H/R-exposed H9C2 cells.Conclusion: Collectively, our findings identified reliable gene signatures through combination strategies of diverse feature selection methods, which facilitated the early detection of ICM and revealed the underlying mechanisms.

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Asporin is a Potential Promising Biomarker for Common Heart Failure

Cited by 16 publications

References 40 publications

Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Robust Rank Aggregation and Least Absolute Shrinkage and Selection Operator Analysis of Novel Gene Signatures in Dilated Cardiomyopathy

Integrated Bioinformatics Algorithms and Experimental Validation to Explore Robust Biomarkers and Landscape of Immune Cell Infiltration in Dilated Cardiomyopathy

Integrated Strategies of Diverse Feature Selection Methods Identify Aging-Based Reliable Gene Signatures for Ischemic Cardiomyopathy

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