Association of FKBP5 genotype with depressive symptoms in patients with coronary heart disease: a prospective study

Brandt, Julia; Warnke, Katharina; Jörgens, Silke; Arolt, Volker; Beer, Katja; Domschke, Katharina; Haverkamp, Wilhelm; Kuhlmann, Stella Linnea; Müller‐Nordhorn, Jacqueline; Rieckmann, Nina; Schwarte, Kathrin; Ströhle, Andreas; Tschorn, Mira; Waltenberger, Johannes; Grosse, Laura

doi:10.1007/s00702-020-02243-6

Cited by 13 publications

(17 citation statements)

References 62 publications

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“…TP63 might serve as a potential prognostic factor in cardiomyopathy [ 175 ]. The expression of FKBP5 is related to the progression of coronary artery disease [ 176 ]. CD247 plays a central role in hypertension [ 177 ], but this gene might be involved in the HF.…”

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.

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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

“…TP63 might serve as a potential prognostic factor in cardiomyopathy [166]. The expression of FKBP5 is related to the progression of coronary artery disease [167]. CD247 plays a central role in hypertension [168], but this gene might be involved in the HF.…”

Section: Discussionmentioning

confidence: 99%

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

Vastrad¹,

Tengli

Vastrad³

2021

Preprint

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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.

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“…DEFB1 [136], SLC11A1 [137], SPINK1 [138], CCR1 [139], GLUL (glutamate-ammonia ligase) [140], GPR84 [141], SIGLEC5 [142], SIGLEC7 [143], LGR5 [144], CD38 [145], GRB14 [146], PRDX1 [147], SLC19A2 [148], CADM2 [149], TRPM5 [150], COL1A1 [151], CTRB1 [152], UTS2R [153], CRTC1 [154], MUC5B [155], TMPRSS6 [156], SLC5A2 [157] and KCNJ9 [158] expression were shown to play an important role in diabetes mellitus, but these genes might be novel target for MI. MT1A [159], LYVE1 [160], S100A12 [161], GCKR (glucokinase regulator) [162], TLR8 [163], MRC1 [164], AGTR1 [165], P2RY12 [166], MSR1 [167], NQO1 [168], FKBP5 [169], CMTM5 [170], ADH1C [171], APLNR (apelin receptor) [172], SFRP4 [173], CCL3 [174], COL11A2 [175], EGR3 [176] and IL2RB [177] expression have a role in coronary artery disease. EDN2 [178], SNX10 [179] and KCNN1 [180] were played a predominant role in progression of atrial fibrillation.…”

Section: Discussionmentioning

confidence: 99%

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Myocardial infarction (MI) is the leading cardiovascular diseases in worldwide, yet relatively little is known about the genes and signaling pathways involved in MI progression. The present investigation aimed to elucidate potential crucial candidate genes and pathways in MI. expression profiling by high throughput sequencing dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 20 MI samples and 12 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis, a protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Hub genes were validated by receiver operating characteristic curve (ROC) analysis. In total, 958 DEGs were identified, of which 480 were up regulated and 478 were down regulated. GO and pathway enrichment analysis results revealed that the DEGs were mainly enriched in, immune system, neuronal system, response to stimulus, and multicellular organismal process. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed by using Cytoscape software, and CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 were identified as the hub genes. Our results highlight the important roles of the genes including CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 in MI pathogenesis or therapeutic management.

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Association of FKBP5 genotype with depressive symptoms in patients with coronary heart disease: a prospective study

Cited by 13 publications

References 62 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

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

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

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