The relationship between B-cell lymphoma 2, interleukin-1β, interleukin-17, and interleukin-33 and the development of diabetic nephropathy

Mahmoud, Basant; Abdel-Moneim, Adel; Negeem, Zinab; Nabil, Ahmed

doi:10.1007/s11033-022-07221-7

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…Sodium levels in G1, G2, G3a, G3b, and G4 significantly increased when compared to healthy controls. While, stage 5 DN (G5) revealed a substantial decline in sodium levels in contrast to all DN and healthy controls, our observations are in parallel with those of earlier studies ( Mahmoud et al, 2022 ). IL-6, a pro-inflammatory substance, precipitates in the beginning and development of prolonged inflammation, possibly lead to the appearance of micro - and macrovascular complications in diabetics ( Winter et al, 2018 ).…”

Section: Discussionsupporting

confidence: 92%

The implication of miR-200a and miR-132 expression and their correlations with NF-κB/TNF-alpha signaling in adults with diabetic nephropathy

Negeem,

Abdel Moneim,

Mahmoud

et al. 2024

Saudi Journal of Biological Sciences

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Section: Discussionsupporting

confidence: 92%

The implication of miR-200a and miR-132 expression and their correlations with NF-κB/TNF-alpha signaling in adults with diabetic nephropathy

Negeem,

Abdel Moneim,

Mahmoud

et al. 2024

Saudi Journal of Biological Sciences

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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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“…Una situación muy retadora para el desarrollo de este trabajo fue el hecho de que la mayoría de los niveles de ILs estaban por debajo del límite de detección de los kits de ELISA utilizados, a pesar de que los DuoSet han demostrado ser kits útiles para la detección de estas IL (14). Esta situación se pudo sortear usando métodos de extrapolación basados en la curva estándar.…”

Section: Discussionunclassified

Niveles de interleucina-17 e interleucina-33 en plasma y orina de pacientes con diabetes mellitus tipo 2 y enfermedad renal diabética

Cortés-Guzmán,

Suárez-Cano,

Narváez

et al. 2023

Rev.ACE

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Contexto: la diabetes mellitus tipo 2 (DM2) es una enfermedad prevalente que puede comprometer cualquier órgano. La enfermedad renal diabética (ERD) es una de las complicaciones de la DM2. Objetivo: conocer las características sociodemográficas, clínicas, de laboratorio clínico y biomarcadores interleucina (IL)-17 e IL-33 en plasma y orina de nuestra población con DM2 y ERD, y encontrar si hay diferencias al comparar con pacientes sin ERD y sin DM2. Metodología: en este estudio de corte transversal los datos se obtuvieron de las historias clínicas. Se midieron las IL-17 e IL-33 en plasma y orina mediante kits comerciales de ensayo de inmunoabsorción ligado a enzimas. Resultados: se incluyeron 62 pacientes con DM2, 23 pacientes con ERD, 39 pacientes sin ERD y 29 pacientes sin DM2. Aquellos con DM2 tienen mayores niveles en orina de IL-17 comparado con los que no tienen DM2 (p<0.001). Los pacientes sin ERD presentan mayores niveles de IL-33 en plasma comparado con los que padecen ERD (p=0.0046). En los pacientes con DM2 existe correlación positiva entre los niveles de IL-17 e IL-33 en orina. Los niveles de IL-33 en plasma presentaron un área bajo la curva de 0.74 para diferenciar entre pacientes con ERD y sin ERD. Conclusiones: los niveles de IL-17 en orina son mayores en pacientes con DM2. Los niveles de IL-33 en plasma son mayores en pacientes sin ERD. El nivel de IL-33 en plasma podría ser útil para diferenciar casos de ERD.

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The relationship between B-cell lymphoma 2, interleukin-1β, interleukin-17, and interleukin-33 and the development of diabetic nephropathy

Cited by 9 publications

References 38 publications

The implication of miR-200a and miR-132 expression and their correlations with NF-κB/TNF-alpha signaling in adults with diabetic nephropathy

The implication of miR-200a and miR-132 expression and their correlations with NF-κB/TNF-alpha signaling in adults with diabetic nephropathy

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

Niveles de interleucina-17 e interleucina-33 en plasma y orina de pacientes con diabetes mellitus tipo 2 y enfermedad renal diabética

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