BMP1 5′UTR + 104 T/C gene variation: can be a predictive marker for serum HDL and apoprotein A1 levels in male patients with coronary heart disease

Akadam-Teker, Başak; Özkara, Gülçin; Kurnaz-Gomleksiz, Ozlem; Buğra, Zehra; Teker, Erhan; Öztürk, Oğuz; Yilmaz-Aydogan, Hülya

doi:10.1007/s11033-018-4283-8

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Krishnan et al [133], Hu et al [134], Martins et al [135], Prieto-Sánchez et al [136], Sugulle et al [137], Zhao et al [138], Siddiqui et al [139], Han et al [140], Lappas et al [141], Wang et al [142], Artunc-Ulkumen et al [143], Blois et al [144], Vacínová et al [145] and Vilmi-Kerälä et al [146] demonstrated that the expression of CREBRF (CREB3 regulatory factor), STRA6, EGFR (epidermal growth factor receptor), MFSD2A, GDF15, PAK1, VCAM1, IGFBP2, IGFBP7, PRKCA (protein kinase C alpha), ADAMTS9, LGALS1, BIN1, TIMP1 and are associated with progression of GDM. Aquila et al, [147],Chen et al [148], Xie et al [149], Zhang et al [150], Aspit et al [151], Akadam-Teker et al [152], Jiang et al [153], Cetinkaya et al [154], Grond-Ginsbach et al [155], Dong et al [156], Chardon et al [157], Chen et al [158], Yamada et al [159], Hu et al [160], Bobik and Kalinina [161], Schwanekamp et al [162], Liu et al [163], Schroer et al [164], Raza et al [165], Yang et al [166], Azuaje et al [167], Durbin et al [168], Chowdhury et al [169], Wang et al [170], Li et al [171], Lv et al [172], Bertoli-Avella et al [173], Grossman et al [174], Andenæs et al [175] and Chen et al [176] demonstrated that HES1, SPIN1, TBX3, EVA1A, CAP2, BMP1, HSPB8, RDX (radixin), COL5A1, LIMS2, PARVA (parvin alpha), EGFLAM (EGF like, fibronectin type III and laminin G domains), NEXN (nexilin F-actin binding protein), TNFRSF14, TGFBI (transforming growth factor beta induced), HAVCR2, CDH11, COL4A1, COL4A2, COL5A2, SHROOM3, HYAL2, PDLIM3, ETS2, PLSCR4, TGFB3, COL6A2 and LTBP2 could induce cardiovascular diseases, but these genes might be essential for progression of GDM. Flamant et al [177], Wan et al [178], Zhang et al [179], Vallvé et al [180], Heximer and Husain [181], Selvarajah et al [182], Jain et al [183], Sun et al [184], Satomi-Kobayashi et al [185], Jiang et al [186], Waghulde et al [187] and Dahal et al [188] reported that DDR1, CAST (calpastatin), KYNU (kynureninas...…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Vastrad¹,

Vastrad²,

Tengli

2021

Preprint

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Gestational diabetes mellitus (GDM) is one of the metabolic diseases during pregnancy. The identification of the central molecular mechanisms liable for the disease pathogenesis might lead to the advancement of new therapeutic options. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non GDM samples were analyzed with limma package. Gene ontology (GO) and REACTOME enrichment analysis were performed using ToppGene. Then we constructed the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA-hub gene network and TF-hub gene regulatory network by the miRNet database and NetworkAnalyst database. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up regulated and 430 down regulated genes. Biological process analysis of GO enrichment analysis showed these DEGs were mainly enriched in reproduction, nuclear outer membrane-endoplasmic reticulum membrane network, identical protein binding, cell adhesion, supramolecular complex and signaling receptor binding. Signaling pathway enrichment analysis indicated that these DEGs played a vital in cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA in the center of the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. Our results indicated that HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA could be the potential novel biomarkers for GDM diagnosis, prognosis and the promising therapeutic targets. The current might be essential to understanding the molecular mechanism of GDM initiation and development.

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Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Vastrad¹,

Vastrad²,

Tengli

2021

Preprint

View full text Add to dashboard Cite

show abstract

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Alur

Raju

Vastrad³

et al. 2021

Bioscience Reports

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Gestational diabetes mellitus (GDM) is the metabolic disorder that appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non-GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein–protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA–hub gene network and TF–hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up-regulated and 430 down-regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3 and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.

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Influence of rs670 variant of APOA1 gene on serum HDL response to an enriched-polyunsaturated vs. an enriched-monounsaturated fat hypocaloric diet

Román¹,

Jáuregui²,

Primo³

et al. 2019

Nutr Hosp

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BMP1 5′UTR + 104 T/C gene variation: can be a predictive marker for serum HDL and apoprotein A1 levels in male patients with coronary heart disease

Cited by 4 publications

References 26 publications

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Influence of rs670 variant of APOA1 gene on serum HDL response to an enriched-polyunsaturated vs. an enriched-monounsaturated fat hypocaloric diet

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