<i>CCR7</i> and its related molecules may be potential biomarkers of pulmonary arterial hypertension

Cai, Mengsi; Li, Xiuchun; Dong, Haoru; Wang, Ying; Huang, Xiaoying

doi:10.1002/2211-5463.13130

Cited by 6 publications

(2 citation statements)

References 26 publications

(28 reference statements)

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“…CEACAM1 [340], ACSL1 [341], TLR4 [342], ABCA1 [343], TLR5 [344], CYP2D6 [345], JAK2 [346], NOTCH2 [347], DDX3X [348], NCOA4 [349], EGR1 [350], IQGAP2 [351], GCLC (glutamate-cysteine ligase catalytic subunit) [352], VEGFA (vascular endothelial growth factor A) [353], ITGB1 [354], LDLR (low density lipoprotein receptor) [355], TLR6 [316], SIRT1 [356], FGL2 [357], TET2 [358], PHF2 [328], VEGFB (vascular endothelial growth factor B) [359], SELENOM (selenoprotein M) [360], TRPM4 [361], OLFM2 [362] and ATAD3A [363] are thought to be involved in non-alcoholic fatty liver disease. Altered expression of ATOH8 [364], STAT1 [365], ARG1 [366], TLR4 [367], VNN1 [368], ABCA1 [369], IFIH1 [370], PTGS2 [371], F2RL1 [289], CYP2D6 [372], PDK4 [373], RNF213 [374], JAK2 [375], NOTCH2 [376], PDGFC (platelet derived growth factor C) [377], TLR2 [378], CYP1B1 [379], IL1RN [380], GCH1 [381], EGR1 [382], HIF1A [383], PLA2G7 [384], CCR2 [385], GAB1 [386], VEGFA (vascular endothelial growth factor A) [387], OGT (O-linked N-acetylglucosamine (GlcNAc) transferase) [388], OXR1 [389], IRF9 [390], FMR1 [391], LDLR (low density lipoprotein receptor) [392], SIRT1 [393], NOD2 [394], ATP13A3 [395], VCAN (versican) [396], FGL2 [397], TET2 [398], KDM6A [399], KLHL2 [400], CAVIN1 [401], TNFRSF4 [402], PF4 [403], VEGFB (vascular endothelial growth factor B) [330], CCR7 [404], PRDX2 [405], HSPB1 [406], TCF4 […”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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“…In this study, it has also been found that the hsa_circ_0081968/hsa-let-7f-5p/CCR7 regulatory axis may be an important target for the treatment of AAAs. Cai et al have also revealed that a lncRNA/miRNA/mRNA interaction network may be involved in the pathogenesis of pulmonary arterial hypertension (PAH) and suggested hsalet-7e-5p and CCR7 can be regarded as potential biomarkers for PAH [52].…”

Section: Discussionmentioning

confidence: 99%

Construction of the circRNA-miRNA-mRNA Regulatory Network of an Abdominal Aortic Aneurysm to Explore Its Potential Pathogenesis

Zhang

Bian

et al. 2021

Disease Markers

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Background. Abdominal aortic aneurysm (AAA) is a progressive cardiovascular disease, which is a permanent and localized dilatation of the abdominal aorta with potentially fatal consequence of aortic rupture. Dysregulation of circRNAs is correlated with the development of various pathological events in cardiovascular diseases. However, the function of circRNAs in abdominal aortic aneurysm (AAA) is unknown and remains to be explored. This study is aimed at determining the regulatory mechanisms of circRNAs in AAAs. This study was aimed at exploring the underlying molecular mechanisms of abdominal aortic aneurysms based on the competing endogenous RNA (ceRNA) regulatory hypothesis of circRNA, miRNA, and mRNA. Methods. The expression profiles of circRNAs (GSE144431), miRNAs (GSE62179), and mRNAs (GSE7084, GSE57691, and GSE47472) in human tissue sample from the aneurysm group and normal group were obtained from the Gene Expression Omnibus database, respectively. The circRNA-miRNA-mRNA network was constructed by using Cytoscape 3.7.2 software; then, the protein-protein interaction (PPI) network was constructed by using the STRING database, and the hub genes were identified by using the cytoHubba plug-in. The circRNA-miRNA-hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in AAAs. Results. The present study identified 40 differentially expressed circRNAs (DECs) in the GSE144431, 90 differentially expressed miRNAs (DEmiRs) in the GSE62179, and 168 differentially expressed mRNAs (DEGs) with the same direction regulation (130 downregulated and 38 upregulated) in the GSE7084, GSE57691, and GSE47472 datasets identified regarding AAAs. The miRNA response elements (MREs) of three DECs were then predicted. Four overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 17 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of 4 miRNAs with 168 DEGs. Furthermore, the circRNA-miRNA-mRNA network was constructed through 3 circRNAs, 4 miRNAs, and 17 mRNAs, and three hub genes (SOD2, CCR7, and PGRMC1) were identified. Simultaneously, functional enrichment and pathway analysis were performed within genes in the circRNA-miRNA-mRNA network. Three of them (SOD2, CCR7, and PGRMC1) were suggested to be crucial based on functional enrichment, protein-protein interaction, and ceRNA network analysis. Furthermore, the expression of SOD2 and CCR7 may be regulated by hsa_circ_0011449/hsa_circ_0081968/hsa-let-7f-5p; the expression of PGRMC1 may be regulated by hsa_circ_0011449/hsa_circ_0081968-hsa-let-7f-5p/hsa-let-7e-5p. Conclusion. In conclusion, the ceRNA interaction axis we identified may be an important target for the treatment of abdominal aortic aneurysms. This study provided further understanding of the potential pathogenesis from the perspective of the circRNA-related competitive endogenous RNA network in AAAs.

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Noncoding RNAs in atherosclerosis: regulation and therapeutic potential

Xing

Yuan

et al. 2023

Mol Cell Biochem

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Atherosclerosis, a chronic disease of arteries, results in high mortality worldwide as the leading cause of cardiovascular disease. The development of clinically relevant atherosclerosis involves the dysfunction of endothelial cells and vascular smooth muscle cells. A large amount of evidence indicates that noncoding RNAs, such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), are involved in various physiological and pathological processes. Recently, noncoding RNAs were identified as key regulators in the development of atherosclerosis, including the dysfunction of endothelial cells, and vascular smooth muscle cells and it is pertinent to understand the potential function of noncoding RNAs in atherosclerosis development. In this review, the latest available research relates to the regulatory role of noncoding RNAs in the progression of atherosclerosis and the therapeutic potential for atherosclerosis is summarized. This review aims to provide a comprehensive overview of the regulatory and interventional roles of ncRNAs in atherosclerosis and to inspire new insights for the prevention and treatment of this disease.

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CCR7 and its related molecules may be potential biomarkers of pulmonary arterial hypertension

Cited by 6 publications

References 26 publications

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Construction of the circRNA-miRNA-mRNA Regulatory Network of an Abdominal Aortic Aneurysm to Explore Its Potential Pathogenesis

Noncoding RNAs in atherosclerosis: regulation and therapeutic potential

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