Genetic Polymorphisms of <i>CYP2C9</i>/<i>CYP2C19</i> in Chronic Obstructive Pulmonary Disease

Lu, Hui; Xu, Dongchuan; Yang, Yixiu; Feng, Qi; Sun, Juan; Li, Quanni; Zhao, Jie; Zhou, Xiaoli; Niu, Huan; Liu, Jianfang; He, Ping; Ding, Yipeng

doi:10.1080/15412555.2020.1780577

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…Signaling pathways include immune system [109], signal transduction [110], extracellular matrix organization [111], adaptive immune system [112], neutrophil degranulation [113], innate immune system [114], metabolism [115], diseases of metabolism [116] and Hemostasis [117] were responsible for advancement of COPD. PTGS2 [118], IL6 [119], CSF3 [120], CXCL8 [121], CXCL1 [122], SOCS3 [123], CCL3 [124], CCL5 [125], VCAM1 [126], CXCL13 [127], CXCL5 [128], CXCL12 [129], FGG (fibrinogen gamma chain) [130], IL1B [131], AREG (amphiregulin) [132], SERPINE1 [133], OSM (oncostatin M) [134], CCL11 [135], MFAP4 [136], APLN (apelin) [137], IL1RN [138], IFNG (interferon gamma) [139], CX3CL1 [140], CDKN1A [141], ICAM1 [142], SNAI1 [143], CD34 [144], IL33 [145], FASLG (Fas ligand) [146], FGF7 [147], TNF (tumor necrosis factor) [148], GDF15 [149], CCL22 [150], IL2 [151], CXCR6 [152], KLF5 [153], MCL1 [154], SRF (serum response factor) [155], FABP4 [156], CYP2C19 [157], CXCR4 [158], FOXC1 [159], EPHX1 [160], ANXA1 [161], DUSP6 [162], CPA3 [163], MYH10 [164], ICOS (inducible T cell costimulator) [165], CD1C [166], CD96 [167], S100A9 [168], FKBP5 [169], HMOX1 [170], PRTN3 [171], MARCO (macrophage receptor with collagenous structure) [172], STAB1 [173], SIGLEC9 [174], SLC11A1 [175], BPI (bactericidal permeability increasing protein) [176], PGF (placental growth factor) [177], FASN (fatty acid synthase) [178], S100A4 […”

Section: Discussionmentioning

confidence: 99%

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Identification of accurate biomarkers is still particularly urgent for improving the poor survival of chronic obstructive pulmonary disease (COPD) patients. In this investigation, we aimed to identity the potential biomarkers in COPD via bioinformatics and next generation sequencing (NGS) data analysis. In this investigation, the differentially expressed genes (DEGs) in COPD were identified using NGS dataset (GSE239897) from Gene Expression Omnibus (GEO) database. Subsequently, gene ontology (GO) and pathway enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of COPD. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to determine the hub genes, miRNAs and TFs. The receiver operating characteristic (ROC) analysis was performed to determine the diagnostic value of hub genes. A total of 956 overlapping DEGs (478 up regulated and 478 down regulated genes) were identified in the NGS dataset. DEGs were mainly associated with GO functional terms and pathways in cellular response to stimulus. response to stimulus, immune system and neutrophil degranulation. There were 10 hub genes (MYC, LMNA, VCAM1, MAPK6, DDX3X, SHMT2, PHGDH, S100A9, FKBP5 and RPS6KA2) identified by PPI, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis. In conclusion, the DEGs, relative GO terms, pathways and hub genes identified in the present investigation might aid in understanding of the molecular mechanisms underlying COPD progression and provide potential molecular targets and biomarkers for COPD.

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