Significance of Soluble CD93 in Type 2 Diabetes as a Biomarker for Diabetic Nephropathy: Integrated Results from Human and Rodent Studies

Lee, Minyoung; Park, Ho Seon; Choi, Min Yeong; Kim, Hak Zoo; Moon, Sung Jin; Ha, Ji Yoon; Choi, Ae Jin; Park, Young Woo; Park, Jong Suk; Shin, Eui‐Cheol; Ahn, Chul Woo; Kang, Shinae

doi:10.3390/jcm9051394

Cited by 14 publications

(24 citation statements)

References 40 publications

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“…The soluble form (sCD93) results from metalloproteinase-dependent shedding of CD93 extracellular domain [ 4 ]. The sources of circulating sCD93 include monocytes, neutrophils, and endothelial cells stimulated by inflammatory mediators (tumor necrosis factor α–TNFα and lipopolysaccharide) [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…There are single reports linking sCD93 with renal diseases, i.e., diabetic nephropathy in type 2 diabetes [ 6 ], and renal involvement in antineutrophil cytoplasmic antibody associated vasculitis [ 16 ]. In 2016, Ikewaki et al [ 20 ] reported a strong correlation between sCD93 and serum creatinine and cystatin C in 14 patients with chronic kidney failure.…”

Section: Introductionmentioning

confidence: 99%

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Soluble Complement Component 1q Receptor 1 (sCD93) Is Associated with Graft Function in Kidney Transplant Recipients

et al. 2021

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Cluster of differentiation 93 (CD93), also known as complement component 1q receptor 1 is a transmembrane glycoprotein expressed in endothelial and hematopoietic cells and associated with phagocytosis, cell adhesion, angiogenesis and inflammation. The extracellular part, soluble CD93 (sCD93), is released to body fluids in inflammation. Data on sCD93 in kidney diseases are limited. Our aim was to evaluate serum sCD93 in long-term kidney transplant recipients as a marker of inflammation and endothelial dysfunction that may be potentially useful in early recognition of graft dysfunction. Seventy-eight adult patients with functioning kidney graft and stable clinical state were examined at least one year after kidney transplantation. Serum sCD93 was measured by enzyme immunosorbent assay. Estimated glomerular filtration rate (eGFR) and albuminuria or proteinuria were assessed at baseline and over one-year follow-up. Increased sCD93 was associated with lower baseline eGFR independently of the confounders. Moreover, sCD93 was negatively associated with eGFR during one-year follow-up in simple analysis; however, this was not confirmed after adjustment for confounders. Baseline sCD93 was positively associated with baseline albuminuria and with increased proteinuria during the follow-up. Serum sCD93 was not correlated with other studied inflammatory markers (interleukin 6, C-reactive protein, procalcitonin and C3 and C4 complement components). To the best of our knowledge, this is the first report regarding the concentrations of sCD93 in kidney transplant recipients and one of the first reports showing the inverse association between sCD93 and renal function. Serum sCD93 should be further evaluated as a diagnostic and prognostic marker in renal transplantation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soluble Complement Component 1q Receptor 1 (sCD93) Is Associated with Graft Function in Kidney Transplant Recipients

et al. 2021

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“…Neutrophil degranulation [52], innate immune system [53], platelet degranulation [54], extracellular matrix organization [55], diseases of glycosylation [56], platelet activation, signaling and aggregation [57], hemostasis [58], secretion [59], secretory vesicle [60], transmembrane transporter activity [61], cell adhesion [62], localization of cell [63], extracellular matrix [55], intrinsic component of plasma membrane [64], structural molecule activity [65], signaling receptor binding [66], have been highly noted in T2DM. Reports indicate that HIF1A [67], HLA-DRB1 [68], CHI3L1 [69], ADORA2A [70], ADRB2 [71], CLU (clusterin) [72], AGT (angiotensinogen) [73], VCAM1 [74], PPARA (peroxisome proliferator activated receptor alpha) [75], APOL1 [76], ZFP36 [77], PPM1B [78], SOCS1 [79], SNCA (synuclein alpha) [80], CTSS (cathepsin S) [81], IL6R [82], CFB (complement factor B) [83], DEFB1 [84], VNN1 [85], RAB27A [86], DPP4 [87], RARRES2 [88], CASP1 [89], LCN2 [90], REG3A [91], CD74 [92], PCSK2 [93], CHGB (chromogranin B) [94], TTR (transthyretin) [95], LRG1 [96], ALB (albumin) [97], DPP7 [98], APOH (apolipoprotein H) [99], CTSD (cathepsin D) [100], GCG (glucagon) [101], KCNQ1 [102], NR4A1 [103], PLIN5 [104], ALDH2 [105], ANG (angiogenin) [106], CLDN7 [107], PRLR (prolactin receptor) [108], SOD2 [109], MLXIPL (MLX interacting protein like) [110], CTSD (cathepsin D) [111], PECAM1 [112], ADA (adenosine deaminase) [113], MFGE8 [114], COL1A1 [115], COL3A1 [116], NID2 [117], ARG1 [118], CD93 [119], IGF2 […”

Section: Discussionmentioning

confidence: 99%

“…A total of 925 DEGs (447 up regulated genes and 478 down regulated genes) were screened between T2DM and control samples. Emmens et al [39] found that PENK [110], CTSD (cathepsin D) [111], PECAM1 [112], ADA (adenosine deaminase) [113], MFGE8 [114], COL1A1 [115], COL3A1 [116], NID2 [117], ARG1 [118], CD93 [119], IGF2 [120], IL18 [121], LAMA1 [122], HPSE (heparanase) [123], BMP4 [124] [132], MMP3 [133], MMP9 [134], MMP11 [135], IL16 [136], TNFRSF11B [137], TIMP3 [138] and CAPN3 [139] were found in T2DM. HLA-A [140], HLA-B [141], HLA-C [142], HLA-DPB1 [143], HLA-E [144], ERBB3 [145], MFAP4 [146] and JAK3 [147] have been reported significantly expressed in type 1 diabetes mellitus, but these genes might be involved in T2DM progression.…”

Section: Discussionmentioning

confidence: 99%

“…Emmens et al [39] found that PENK (proenkephalin) expression was up regulated in cardiovascular diseases, but this gene might be responsible for progression of T2DM. Recent studies have proposed that the SAA1 [ [101], KCNQ1 [102], NR4A1 [103], PLIN5 [104], ALDH2 [105], ANG (angiogenin) [106], CLDN7 [107], PRLR (prolactin receptor) [108], SOD2 [109], MLXIPL (MLX interacting protein like) [110], CTSD (cathepsin D) [111], PECAM1 [112], ADA (adenosine deaminase) [113], MFGE8 [114], COL1A1 [115], COL3A1 [116], NID2 [117], ARG1 [118], CD93 [119], IGF2 [120], IL18 [121], LAMA1 [122], HPSE (heparanase) [123], BMP4 [124], CXCR4 [125], KDR (kinase insert domain receptor) [126], ESAM (endothelial cell adhesion molecule) [127], THBS1 [128], CD34 [129], SERPINE1 [130], WNT5B [131], MGP (matrix Gla protein) [132], MMP3 [133], MMP9 [134], MMP11 [135], IL16 [136], TNFRSF11B [137], TIMP3 [138] and CAPN3 [139] were found in T2DM. HLA-A [140], HLA-B [141], HLA-C [142], HLA-DPB1 [143], HLA-E [144], ERBB3 [145], MFAP4 [146] and JAK3…”

Section: Discussionmentioning

confidence: 99%

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Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

Vastrad¹,

Vastrad²

2021

Preprint

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Type 2 diabetes mellitus (T2DM) is etiologically related to metabolic disorder. The aim of our study was to screen out candidate genes of T2DM and to elucidate the underlying molecular mechanisms by bioinformatics methods. Expression profiling by high throughput sequencing data of GSE154126 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between T2DM and normal control were identified. And then, functional enrichment analyses of gene ontology (GO) and REACTOME pathway analysis was performed. Protein protein interaction (PPI) network and module analyses were performed based on the DEGs. Additionally, potential miRNAs of hub genes were predicted by miRNet database. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst database. Further, validations were performed by receiver operating characteristic curve (ROC) analysis and real time polymerase chain reaction (RT PCR). In total, 925 DEGs were identified in T2DM, including 447 up regulated genes and 478 down-regulated genes. Functional enrichment analysis results showed that up regulated DEGs were significantly enriched in defense response, neutrophil degranulation, cell adhesion and extracellular matrix organization. The top 10 hub genes, JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 were identified from the PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network. Furthermore, ROC analysis and RT PCR revealed that JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 might serve as biomarkers in T2DM. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of T2DM. The identified hub genes may participate in the onset and advancement of T2DM and serve as therapeutic targets.

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Ultrasound Imaging of Tumor Vascular CD93 with MMRN2 Modified Microbubbles for Immune Microenvironment Prediction

Wang,

Xing,

Liang

et al. 2024

Advanced Materials

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Vascular microenvironment is found to be closely related to immunotherapy efficacy. Identification and ultrasound imaging of the unique vascular characteristics able to predict immune microenvironment are important for immunotherapy decision‐making. Herein, it is proved that high CD93 expression in the tumor vessels is closely related to the poor immune response of prostate cancer. For ultrasound molecular imaging of CD93, CD93‐targeted microbubbles (MBs) consisted of a gaseous core and the MMRN2 (Multimerin‐2) containing cell membrane/lipid hybrid membrane are then synthesized. In vitro and in vivo assays demonstrate that these MBs can recognize CD93 efficiently and then accumulate within tumor regions highly expressing CD93. Contrast‐enhanced ultrasound imaging with CD93‐targeted MBs demonstrates that ultrasound intensity is negatively related with inflammatory tumor immune microenvironment and cytotoxic T cell infiltration. Together, endothelial expression of CD93 in tumor is a unique predictor of immunosuppressive microenvironment and CD93‐targeted MBs have a great potential of evaluating tumor immune status.This article is protected by copyright. All rights reserved

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Significance of Soluble CD93 in Type 2 Diabetes as a Biomarker for Diabetic Nephropathy: Integrated Results from Human and Rodent Studies

Cited by 14 publications

References 40 publications

Soluble Complement Component 1q Receptor 1 (sCD93) Is Associated with Graft Function in Kidney Transplant Recipients

Soluble Complement Component 1q Receptor 1 (sCD93) Is Associated with Graft Function in Kidney Transplant Recipients

Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

Ultrasound Imaging of Tumor Vascular CD93 with MMRN2 Modified Microbubbles for Immune Microenvironment Prediction

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