Dataset Information

Predicting key gene related to immune infiltration and myofibroblast-like valve interstitial cells in patients with calcified aortic valve disease based on bioinformatics analysis.

ABSTRACT:

Background

Calcified aortic valve disease (CAVD) is the most prevalent valvular disease that can be treated only through valve replacement. We aimed to explore potential biomarkers and the role of immune cell infiltration in CAVD progression through bioinformatics analysis.

Methods

Differentially ex-pressed genes (DEGs) were screened out based on three microarray datasets: GSE12644, GSE51472 and GSE83453. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to evaluate gene expression differences. Machine learning algorithms and DEGs were used to screen key gene. We used CIBERSORT to evaluate the immune cell infiltration of CAVD and evaluated the correlation between the biomarkers and infiltrating immune cells. We also compared bioinformatics analysis results with the valve interstitial cells (VICs) gene expression in single-cell RNA sequencing.

Results

Collagen triple helix repeat containing 1 (CTHRC1) was identified as the key gene of CAVD. We identified a cell subtype valve interstitial cells-fibroblast, which was closely associated with fibro-calcific progress of aortic valve. CTHRC1 highly expressed in the VIC subpopulation. Immune infiltration analysis demonstrated that mast cells, B cells, dendritic cells and eosinophils were involved in pathogenesis of CAVD. Correlation analysis demonstrated that CTHRC1 was correlated with mast cells mostly.

Conclusions

In summary, the study suggested that CTHRC1 was a key gene of CAVD and CTHRC1 might participate in the potential molecular pathways involved in the connection between infiltrating immune cells and myofibroblast phenotype VICs.

SUBMITTER: Lu W

PROVIDER: S-EPMC10407485 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Publications

Predicting key gene related to immune infiltration and myofibroblast-like valve interstitial cells in patients with calcified aortic valve disease based on bioinformatics analysis.

Lu Wenyuan W Sun Cheng C Hou Jianfeng J

Journal of thoracic disease 20230718 7

<h4>Background</h4>Calcified aortic valve disease (CAVD) is the most prevalent valvular disease that can be treated only through valve replacement. We aimed to explore potential biomarkers and the role of immune cell infiltration in CAVD progression through bioinformatics analysis.<h4>Methods</h4>Differentially ex-pressed genes (DEGs) were screened out based on three microarray datasets: GSE12644, GSE51472 and GSE83453. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathw ...[more]

PMID: 37559614

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Project description:Background: Valvular heart disease is obtaining growing attention in the cardiovascular field and it is believed that calcific aortic valve disease (CAVD) is the most common valvular heart disease (VHD) in the world. CAVD does not have a fully effective treatment to delay its progression and the specific molecular mechanism of aortic valve calcification remains unclear. Materials and Methods: We obtained the gene expression datasets GSE12644 and GSE51472 from the public comprehensive free database GEO. Then, a series of bioinformatics methods, such as GO and KEGG analysis, STING online tool, Cytoscape software, were used to identify differentially expressed genes in CAVD and healthy controls, construct a PPI network, and then identify key genes. In addition, immune infiltration analysis was used via CIBERSORT to observe the expression of various immune cells in CAVD. Results: A total of 144 differential expression genes were identified in the CAVD samples in comparison with the control samples, including 49 up-regulated genes and 95 down-regulated genes. GO analysis of DEGs were most observably enriched in the immune response, signal transduction, inflammatory response, proteolysis, innate immune response, and apoptotic process. The KEGG analysis revealed that the enrichment of DEGs in CAVD were remarkably observed in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway. Chemokines CXCL13, CCL19, CCL8, CXCL8, CXCL16, MMP9, CCL18, CXCL5, VCAM1, and PPBP were identified as the hub genes of CAVD. It was macrophages that accounted for the maximal proportion among these immune cells. The expression of macrophages M0, B cells memory, and Plasma cells were higher in the CAVD valves than in healthy valves, however, the expression of B cells naïve, NK cells activated, and macrophages M2 were lower. Conclusion: We detected that chemokines CXCL13, CXCL8, CXCL16, and CXCL5, and CCL19, CCL8, and CCL18 are the most important markers of aortic valve disease. The regulatory macrophages M0, plasma cells, B cells memory, B cells naïve, NK cells activated, and macrophages M2 are probably related to the occurrence and the advancement of aortic valve stenosis. These identified chemokines and these immune cells may interact with a subtle adjustment relationship in the development of calcification in CAVD.

Project description:Background: Calcific aortic valve disease (CAVD) is the most common valvular heart disease in the aging population, resulting in a significant health and economic burden worldwide, but its underlying diagnostic biomarkers and pathophysiological mechanisms are not fully understood. Methods: Three publicly available gene expression profiles (GSE12644, GSE51472, and GSE77287) from human Calcific aortic valve disease (CAVD) and normal aortic valve samples were downloaded from the Gene Expression Omnibus database for combined analysis. R software was used to identify differentially expressed genes (DEGs) and conduct functional investigations. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify key feature genes as potential biomarkers for Calcific aortic valve disease (CAVD). Receiver operating characteristic (ROC) curves were used to evaluate the discriminatory ability of key genes. The CIBERSORT deconvolution algorithm was used to determine differential immune cell infiltration and the relationship between key genes and immune cell types. Finally, the Expression level and diagnostic ability of the identified biomarkers were further validated in an external dataset (GSE83453), a single-cell sequencing dataset (SRP222100), and immunohistochemical staining of human clinical tissue samples, respectively. Results: In total, 34 identified DEGs included 21 upregulated and 13 downregulated genes. DEGs were mainly involved in immune-related pathways such as leukocyte migration, granulocyte chemotaxis, cytokine activity, and IL-17 signaling. The machine learning algorithm identified SCG2 and CCL19 as key feature genes [area under the ROC curve (AUC) = 0.940 and 0.913, respectively; validation AUC = 0.917 and 0.903, respectively]. CIBERSORT analysis indicated that the proportion of immune cells in Calcific aortic valve disease (CAVD) was different from that in normal aortic valve tissues, specifically M2 and M0 macrophages. Key genes SCG2 and CCL19 were significantly positively correlated with M0 macrophages. Single-cell sequencing analysis and immunohistochemical staining of human aortic valve tissue samples showed that SCG2 and CCL19 were increased in Calcific aortic valve disease (CAVD) valves. Conclusion: SCG2 and CCL19 are potential novel biomarkers of Calcific aortic valve disease (CAVD) and may play important roles in the biological process of Calcific aortic valve disease (CAVD). Our findings advance understanding of the underlying mechanisms of Calcific aortic valve disease (CAVD) pathogenesis and provide valuable information for future research into novel diagnostic and immunotherapeutic targets for Calcific aortic valve disease (CAVD).

Project description:Background: As the most prevalent valvular heart disease, calcific aortic valve disease (CAVD) has become a primary cause of aortic valve stenosis and insufficiency. We aim to illustrate the roles of immune related genes (IRGs) and immune cells infiltration in the occurrence of CAVD. Methods: Integrative meta-analysis of expression data (INMEX) was adopted to incorporate multiple gene expression datasets of CAVD from Gene Expression Omnibus (GEO) database. By matching the differentially expressed genes (DEGs) to IRGs from "ImmPort" database, differentially expressed immune related genes (DEIRGs) were screened out. We performed enrichment analysis and found that DEIRGs in CAVD were closely related to inflammatory response and immune cells infiltration. We also constructed protein-protein interaction (PPI) network of DEIRGs and identified 5 key DEIRGs in CAVD according to the mixed character calculation results. Moreover, CIBERSORT algorithm was used to explore the profile of infiltrating immune cells in CAVD. Based on Spearman's rank correlation method, correlation analysis between key DEIRGs and infiltrating immune cells was performed. Results: A total of 220 DEIRGs were identified and the enrichment analysis of DEIRGs showed that they were significantly enriched in inflammatory responses. PPI network was constructed and PTPN11, GRB2, SYK, PTPN6 and SHC1 were identified as key DEIRGs. Compared with normal aortic valve tissue samples, the proportion of neutrophils, T cells CD4 memory activated and macrophages M0 was elevated in calcified aortic valves tissue samples, as well as reduced infiltration of macrophages M2 and NK cells activated. Furthermore, key DEIRGs identified in the present study, including PTPN11, GRB2, PTPN6, SYK, and SHC1, were all significantly correlated with infiltration of various immune cells. Conclusion: This meta-analysis suggested that PTPN11, GRB2, PTPN6, SYK, and SHC1 might be key DEIRGs associated with immune cells infiltration, which play a pivotal role in pathogenesis of CAVD.

Project description:The present study aimed to elucidate the role of autophagy-related genes (ARGs) in calcific aortic valve disease (CAVD) and their potential interactions with immune infiltration via experimental verification and bioinformatics analysis. A total of three microarray datasets (GSE12644, GSE51472 and GSE77287) were obtained from the Gene Expression Omnibus database, and gene set enrichment analysis was performed to identify the relationship between autophagy and CAVD. After differentially expressed genes and differentially expressed ARGs (DEARGs) were identified using CAVD samples and normal aortic valve samples, a functional analysis was performed, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, protein-protein interaction network construction, hub gene identification and validation, immune infiltration and drug prediction. The results of the present study indicated a significant relationship between autophagy and CAVD. A total of 46 DEARGs were identified. GO and pathway enrichment analyses revealed the complex roles of DEARGs in regulating CAVD, including multiple gene functions and pathways. A total of 10 hub genes were identified, with three (SPP1, CXCL12 and CXCR4) consistently upregulated in CAVD samples compared with normal aortic valve samples in multiple datasets and experimental validation. Immune infiltration analyses demonstrated significant differences in immune cell proportions between CAVD samples and normal aortic valve samples, thus showing the crucial role of immune infiltration in CAVD development. Furthermore, therapeutic drugs were predicted that could target the identified hub genes, including bisphenol A, resveratrol, progesterone and estradiol. In summary, the present study illuminated the crucial role of autophagy in CAVD development and identified key ARGs as potential therapeutic targets. In addition, the observed immune cell infiltration and predicted autophagy-related drugs suggest promising avenues for future research and novel CAVD treatments.

Project description:BackgroundRenal interstitial fibrosis (RIF) is the common final pathway that mediates almost all progressive renal diseases. However, the underlying mechanisms of RIF have not been fully elucidated. Therefore, the current study aimed to explore the etiology of RIF and identify the key targets and immune infiltration patterns of RIF.MethodsRibonucleic acid (RNA)-seq data of RIF and normal samples were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to screen relevant modules associated with RIF. Differentially expressed genes (DEGs) between the RIF and normal samples were identified using the limma package. Machine learning methods were used to identify hub gene signatures related to RIF. Further biochemical approaches including quantitative polymerase chain reaction (qPCR), immunoblotting and immunohistochemistry experiments were performed to verify the hub signatures in the RIF samples. Single sample gene set enrichment analysis (ssGSEA) was used to analyze the proportions of 28 immune cells in RIF and normal samples.ResultsWGCNA showed 121 RIF-related genes. A total of 523 DEGs were found between the RIF and normal samples. By overlapping these genes, we obtained 78 RIF-related genes, which were mainly enriched in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with immunity and inflammation. Integrative analysis of machine learning methods showed prominin 1 (PROM1), tryptophan aspartate-containing coat protein (CORO1A), interferon-stimulated exonuclease gene 20 (ISG20), and tissue inhibitor matrix metalloproteinase 1 (TIMP1) as hub gene signatures in RIF. Further, receiver operating curve (ROC) curves implied the diagnostic role of ISG20 and CORO1A in RIF. The expression levels of ISG20 and CORO1A were significantly higher in fibrotic tubular cells and renal tissues based on biochemical approaches. The immune microenvironment was found to be markedly altered in the RIF samples, as 21 differentially infiltrated immune cells (DIICs) were found between RIF and normal samples.ConclusionsThis study is the first to find that ISG20 and CORO1A are key biomarkers and to examine the landscape of immune infiltration in RIF. Our findings provide novel insights into the mechanisms and treatment of patients with RIF.

Dataset Information

Predicting key gene related to immune infiltration and myofibroblast-like valve interstitial cells in patients with calcified aortic valve disease based on bioinformatics analysis.

Background

Methods

Results

Conclusions

Publications

Predicting key gene related to immune infiltration and myofibroblast-like valve interstitial cells in patients with calcified aortic valve disease based on bioinformatics analysis.

Similar Datasets

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