The low-density lipoprotein receptor-related protein 5 (LRP5) 4037C&gt;T polymorphism: candidate for susceptibility to type 1 diabetes mellitus

Souza, Karla Simone Costa de; Ururahy, Marcela Abbott Galvão; Oliveira, Yonara Monique da Costa; Loureiro, Melina Bezerra; Silva, Heglayne Pereira Vital da; Bortolin, Raul Hernandes; Luchessi, André Ducati; Arrais, Ricardo Fernando; Hirata, Rosário Dominguez Crespo; Almeida, Maria das Graças; Hirata, Mário Hiroyuki; Rezende, Adriana Augusto de

doi:10.20945/2359-3997000000057

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…[697], IL18 [698], CYGB (cytoglobin) [636], EGF (epidermal growth factor) [699], TRPM2 [700], BANK1 [701], SORT1 [643], HSPG2 [645], NOX4 [702], MYH9 [703], TRPV4 [704], B4GALNT1 [705], LAG3 [706], CACNA1C [654], LRP5 [707], PIM1 [658], MDK (midkine) [708], CA2 [709], TRPC6 [710], CYP2D6 [711] and STAT4 [712] might be a biological target of type 1 diabetes mellitus. CCK (cholecystokinin) [713], IL33 [714], IRF4 [715], CXCL8 [716], MMP3 [717], DDIT4L [718], KL (klotho) [719], CXCL6 [87], CXCL1 [720], STC1 [721], PDK4 [722], RAB27B [723], CXCL5 [724], GPRC5A [725], TXNIP (thioredoxin interacting protein) [726], RAB3B [727], HSPA5 [728], DKK1 [93], NAMPT (nicotinamidephosphoribosyltransferase) [729], TNFAIP3 [730], GHR (growth hormone receptor) [731], ANXA1 [732], CD40 [733], KITLG (KIT ligand) [734], SMURF2 [735], NRG1 [684], GCLC (glutamate-cysteine ligase catalytic subunit) [685], NPHP1 [736], EPOR (erythropoietin receptor) [737], ADK (adenosine kinase) [738], SAA1…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Diabetic patients are prone to diabetic kidney disease (DKD), which may cause cardiovascular damage, hypertension and obesity, and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of diabetic kidney disease (DKD) has not been fully elucidated, and current treatments remain inadequate. Therefore, it is essential to explore the molecular mechanism of DKD and its complications. Next Generation Sequancing (GSE217709) dataset was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were picked out by R software. Then Gene ontology (GO) and REACTOME pathway enrichment analysis were performed by g:Profiler database, protein-protein interaction (PPI) of DEGs was constructed by Human Integrated Protein-Protein Interaction rEference (HIPPIE) database . Module analysis was carried out by Cytoscape plug-in PEWCC. Subsequently, miRNA-hub gene regulatory network and TF- hub gene regulatory network were performed by miRNet database and NetworkAnalyst database. Finally, validation of hub genes was performed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. In total, 958 DEGs, including 479 up regulated and 479 down regulated genes, were identified. The GO and pathway enrichment changes of DEGs were mainly enriched in biological regulation, multicellular organismal process, signaling by GPCR and extracellular matrix organization. Ten hub genes (HSPA8, HSP90AA1, HSPA5, SDCBP, HSP90B1, VCAM1, MYH9, FLNA, MDFI and PML) associated with DKD and its complications were identified. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of DKD and its complications. The identified hub genes may participate in the onset and development of DKD and its complications and serve as therapeutic targets.

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Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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“…The following supporting information can be downloaded at: . References [ 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ] are cited in the Supplementary Material.…”

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confidence: 99%

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes

Timasheva

Balkhiyarova

Авзалетдинова

et al. 2023

IJMS

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We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, PFDR = 3.40 × 10−5), CCR5 rs333 (OR = 1.99, PFDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, PFDR = 2.64 × 10−4), TCF7L2 rs114758349 (OR = 1.77, PFDR = 9.37 × 10−5), and CCL2 rs1024611 (OR = 1.38, PFDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7%–87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10−6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = −17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

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Single nucleotide polymorphisms (rs3736228 and rs4988321) in low-density lipoprotein receptor-related protein-5 gene with predisposition to rheumatoid arthritis

Zehra

Ghouri

Jafari

et al. 2023

Gene

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The low-density lipoprotein receptor-related protein 5 (LRP5) 4037C>T polymorphism: candidate for susceptibility to type 1 diabetes mellitus

Cited by 4 publications

References 21 publications

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes

Single nucleotide polymorphisms (rs3736228 and rs4988321) in low-density lipoprotein receptor-related protein-5 gene with predisposition to rheumatoid arthritis

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