CD14, CD163, and CCR1 are involved in heart and blood communication in ischemic cardiac diseases

Chen, Chengcong; Hong, Peng; Zeng, Yanwu; Dong, Guoqing

doi:10.1177/0300060520951649

Cited by 16 publications

(10 citation statements)

References 38 publications

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“…CTSG (cathepsin G) is a protein coding gene plays important roles in aortic aneurysms [ 166 ]. Evidence from Safa et al [ 167 ], Chen et al [ 168 ], Zhou et al [ 169 ], Hu et al [ 170 ], Lou et al [ 171 ], Zhang et al [ 172 ] and Chen et al [ 173 ] study indicated that the expression of CCL22, CCR1, FPR1, KNG1, CRISPLD2, CD38 and GPRC5A were linked with progression of ischemic heart disease. Li et al [ 174 ] showed that STEAP3 expression can be associated with cardiac hypertrophy progression.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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Introduction Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. Methods The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein–protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene—miRNA regulatory network and target gene—TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. Results A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene—miRNA regulatory network and target gene—TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. Conclusion The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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show abstract

“…To clarify the dynamic progression of HF, heart samples is the best straightforward to monitor this progression [4]. Herein, we con rmed our computational analysis using qRT-PCR in left ventricle and peripheral blood specimens to follow probable dynamic mRNA expression being consistent with ICM progression from CAD and MI phenotypes.…”

Section: Discussionmentioning

confidence: 65%

“…M2 macrophages inhibit tumor apoptosis and activate NK-κB to triggers cell proliferation releasing proangiogenic factors such as VEGF, FGF2 and TNFα and immunomodulatory factors such as IL-10 and TGF-β [28]. The immune and in ammatory responses are critical biochemical processes in ischemic heart failure [4]. From the above, there are reasonable hypothesis that disrupted in ammatory responses relevant to CD163 is strongly nominated to be key biomarker in ischemic heart failure progression.…”

Section: Discussionmentioning

confidence: 99%

“…Chen et al (2020) analyzed microarray data GSE42955 and GSE9128 as study datasets including blood samples of ischaemic cardiomyopathy (ICM) and non-ICM patients, and GSE16499, GSE57338, and GSE59867 as validation groups, the authors pointed that CD163 and CEBPD were overlapped between GSE42955 and GSE9128 and CD163 represented as one of the hub downregulated genes [4]. Chen et al…”

Section: Discussionmentioning

confidence: 99%

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STAT3-CD163 crosstalk exhibits promising biomarkers for a progressive Ischemic cardiomyopathy; integrative computational and gene expression profiling based on GEO datasets

Mohareb¹,

Kariem²,

Tohamy³

et al. 2023

Preprint

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Background:Ischemic heart diseaseis one of the heaviest health-related burdens worldwide.We aimed to identify the common hub mRNA and pathways that are involved in pathological progression of ischemic cardiomyopathy(ICM). Methods:To explore potential differentially expressed genes (DEGs) of all ischemic heart disease stages, we used chipster and GEO2R tools to analyze of retrieved eight high throughput RNA datasets obtained from GEO database. Gene Ontology functional annotation and Pathways enrichment analyses were used to obtain the common functional enriched DEGs which were visualized in protein–protein interactions (PPI) network to explore the hub mRNA according to the interaction scores. Validation qRT-PCR was carried out for blood and cardiac biopsies compared with controls to validate the determined four hub mRNAs and subsequently reviewed inside comprehensive published meta-analysis database. The validated mRNAs were visualized in two interaction modules. Finally screening of approved drugs was applied. Results: 15 common DEGs with p value ≤ 0.01 were identified and carbohydrate &amino acids metabolism and inflammatory responses were significantly enriched. STAT3, CEBPD, GLUL and CD163 were hub enriched mRNAs with interaction score ≥ 0.50. Our qRT-PCR analysis showed increased expression of STAT3 over all patients groups and CD163 mainly in cardiac samples with remarked ascending manner. Interaction modules showed co-regulators supporting high STAT3-CD163 connectivity providing potential role of STAT3-CD163 crosstalk mediated inflammatory responses in ICM progression. We determined two reported drugs targeting STAT3. Conclusion:Post analysis of the used GEO datasets and qRT-PCR data pointed that STAT3-CD163 crosstalk was potential biomarkers for ICM progression. Clinical trial registration: www.clinicaltrials.gov, Identifier: NCT05508269

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“…[158], Chen et al [159], Zhou et al [160], Hu et al [161], Lou et al [162], Zhang et al [163] and Chen et al [164] study indicated that the expression of CCL22, CCR1, FPR1, KNG1, CRISPLD2, CD38 and GPRC5A were linked with progression of ischemic heart disease. Li et al [165] showed that STEAP3 expression can be associated with cardiac hypertrophy progression.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Vastrad¹,

Tengli

Vastrad³

2021

Preprint

View full text Add to dashboard Cite

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene - miRNA regulatory network and target gene - TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

show abstract

CD14, CD163, and CCR1 are involved in heart and blood communication in ischemic cardiac diseases

Cited by 16 publications

References 38 publications

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

STAT3-CD163 crosstalk exhibits promising biomarkers for a progressive Ischemic cardiomyopathy; integrative computational and gene expression profiling based on GEO datasets

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

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