Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Diabetes Reviews
  4. Volume 21, Issue 2
  5. Article
Volume 21, Issue 2
  • ISSN: 1573-3998
  • E-ISSN: 1875-6417

Abstract

Background

Diabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life of diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship with DN is unclear. Therefore, screening biomarkers related to sphingolipid metabolism is crucial for treating DN.

Methods

To identify Differentially Expressed Genes (DEGs) in the GSE142153 dataset, we conducted a differential expression analysis (DN samples versus control samples). The intersection genes were obtained by overlapping DEGs and Sphingolipid Metabolism-Related Genes (SMRGs). Furthermore, The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to filter biomarkers. We further analyzed the Gene Set Enrichment analysis (GSEA) and the immunoinfiltrational analysis based on biomarkers.

Results

We identified 2,186 DEGs associated with DN. Then, five SMR-DEGs were obtained. Subsequently, biomarkers associated with sphingolipid metabolism ( and ) were identified by applying machine learning and expression analysis. In addition, GSEA showed that these biomarkers were correlated with cytokine cytokine receptor interaction’. Significant variations in B cells, DCs, Tems, and Th2 cells between the two groups suggested that these cells might have a role in DN.

Conclusion

Overall, we obtained two sphingolipid metabolism-related biomarkers ( and ) associated with DN, which laid a theoretical foundation for treating DN.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cdr/10.2174/0115733998297749240418071555
2024-05-07
2024-11-19

  • From This Site

    /content/journals/cdr/10.2174/0115733998297749240418071555
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. IchinoseK. KawasakiE. EguchiK. Recent advancement of understanding pathogenesis of type 1 diabetes and potential relevance to diabetic nephropathy.Am. J. Nephrol.200727655456410.1159/000107758 17823503
     [Google Scholar]
  2. AlicicR.Z. RooneyM.T. TuttleK.R. Diabetic kidney disease.Clin. J. Am. Soc. Nephrol.201712122032204510.2215/CJN.11491116 28522654
     [Google Scholar]
  3. ValenciaW.M. FlorezH. How to prevent the microvascular complications of type 2 diabetes beyond glucose control.BMJ2017356i650510.1136/bmj.i6505 28096078
     [Google Scholar]
  4. SelbyN.M. TaalM.W. An updated overview of diabetic nephropathy: Diagnosis, prognosis, treatment goals and latest guidelines.Diabetes Obes. Metab.202022S1Suppl. 131510.1111/dom.14007 32267079
     [Google Scholar]
  5. PhillipsA.O. BaboolalK. RileyS. Association of prolonged hyperglycemia with glomerular hypertrophy and renal basement membrane thickening in the Goto Kakizaki model of non-insulin-dependent diabetes mellitus.Am. J. Kidney Dis.200137240041010.1053/ajkd.2001.21322 11157383
     [Google Scholar]
  6. WangK. HuJ. LuoT. Effects of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on all-cause mortality and renal outcomes in patients with diabetes and albuminuria: A systematic review and meta-analysis.Kidney Blood Press. Res.201843376877910.1159/000489913 29794446
     [Google Scholar]
  7. PalmerS.C. TendalB. MustafaR.A. Sodium-glucose cotransporter protein-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials.BMJ2021372m457310.1136/bmj.m4573 33441402
     [Google Scholar]
  8. LinY. WeiY. WeiY. Dexmedetomidine alleviates oxidative stress and mitochondrial dysfunction in diabetic peripheral neuropathy via the microRNA-34a/SIRT2/S1PR1 axis.Int. Immunopharmacol.202311710991010.1016/j.intimp.2023.109910 37012886
     [Google Scholar]
  9. CerychovaR. BohuslavovaR. PapousekF, et al Adverse effects of Hif1a mutation and maternal diabetes on the offspring heart.Cardiovasc. Diabetol.20181716810.1186/s12933‑018‑0713‑0 29753320
     [Google Scholar]
  10. LiuD. LiuX. SuY. ZhangX. Renal expression of proto-oncogene Ets-1 on matrix remodeling in experimental diabetic nephropathy.Acta Histochem.2011113552753310.1016/j.acthis.2010.05.006 20598359
     [Google Scholar]
  11. GomesC.P. TorloniM.R. Gueuvoghlanian-SilvaB.Y. AlexandreS.M. MattarR. DaherS. Cytokine levels in gestational diabetes mellitus: A systematic review of the literature.Am. J. Reprod. Immunol.201369610.1111/aji.12088
     [Google Scholar]
  12. PhipsonB. LeeS. MajewskiI.J. AlexanderW.S. SmythG.K. Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression.Ann. Appl. Stat.201610294696310.1214/16‑AOAS920 28367255
     [Google Scholar]
  13. HuY. YangC. ShenG. Hyperglycemia‐triggered sphingosine‐1‐phosphate and sphingosine‐1‐phosphate receptor 3 signaling worsens liver ischemia/reperfusion injury by regulating M1/M2 polarization.Liver Transpl.20192571074109010.1002/lt.25470 30972941
     [Google Scholar]
  14. ZhangX. HeD. XiangY. DYSF promotes monocyte activation in atherosclerotic cardiovascular disease as a DNA methylation-driven gene.Transl. Res.2022247193810.1016/j.trsl.2022.04.001 35460889
     [Google Scholar]
  15. WuT. HuE. XuS. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.Innovation20212310014110.1016/j.xinn.2021.100141 34557778
     [Google Scholar]
  16. SpiegelS. MilstienS. Exogenous and intracellularly generated sphingosine 1-phosphate can regulate cellular processes by divergent pathways.Biochem. Soc. Trans.20033161216121910.1042/bst0311216 14641029
     [Google Scholar]
  17. HorrocksL.A. Composition of myelin from peripheral and central nervous systems of the squirrel monkey.J. Lipid Res.19678656957610.1016/S0022‑2275(20)38877‑5 4964432
     [Google Scholar]
  18. MatherA.R. SiskindL.J. Glycosphingolipids and kidney disease.Adv. Exp. Med. Biol.201172112113810.1007/978‑1‑4614‑0650‑1_8 21910086
     [Google Scholar]
  19. MerrillA.H.Jr SchmelzE-M. DillehayD.L. Sphingolipids--the enigmatic lipid class: biochemistry, physiology, and pathophysiology.Toxicol. Appl. Pharmacol.1997142120822510.1006/taap.1996.8029 9007051
     [Google Scholar]
  20. NowlingT.K. MatherA.R. ThiyagarajanT. Renal glycosphingolipid metabolism is dysfunctional in lupus nephritis.J. Am. Soc. Nephrol.20152661402141310.1681/ASN.2014050508 25270066
     [Google Scholar]
  21. ShaymanJ.A. Eliglustat tartrate.Drugs Future201035861362010.1358/dof.2010.35.8.1505566 22563139
     [Google Scholar]
  22. ShaymanJ.A. RadinN.S. Structure and function of renal glycosphingolipids.Am. J. Physiol.19912603 Pt 2F291F302 2000947
     [Google Scholar]
  23. SubathraM. KorrapatiM. HowellL.A. Kidney glycosphingolipids are elevated early in diabetic nephropathy and mediate hypertrophy of mesangial cells.Am. J. Physiol. Renal Physiol.20153093F204F21510.1152/ajprenal.00150.2015 26041445
     [Google Scholar]
  24. ThudichumJ.L.W. Further researches on the chemical constitution of the brain.Rep Med Officer Privy Council Local Governm Board18833221261
     [Google Scholar]
  25. ZadorI.Z. DeshmukhG.D. KunkelR. JohnsonK. RadinN.S. ShaymanJ.A. A role for glycosphingolipid accumulation in the renal hypertrophy of streptozotocin-induced diabetes mellitus.J. Clin. Invest.199391379780310.1172/JCI116299 8450061
     [Google Scholar]
  26. DemonbreunA.R. RossiA.E. AlvarezM.G. Dysferlin and myoferlin regulate transverse tubule formation and glycerol sensitivity.Am. J. Pathol.2014184124825910.1016/j.ajpath.2013.09.009 24177035
     [Google Scholar]
  27. ChiHao PengF. YangJ. Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.Front. Endocrinol. (Lausanne)202213105631010.3389/fendo.2022.1056310
     [Google Scholar]
  28. RenZhijing HeY. YangQ. A comprehensive analysis of the Glutathione Peroxidase 8 (GPX8) in human cancer.Front. Oncol.20221281281110.3389/fonc.2022.812811
     [Google Scholar]
  29. YuG. WangL.G. HanY. HeQ.Y. clusterProfiler: An R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.0118 22455463
     [Google Scholar]
  30. WuXiaoqing LuWenping XuChaojie PTGIS may be a predictive marker for ovarian cancer by regulating fatty acid metabolism.Comput. Math. Methods Med.20232023239772810.1155/2023/2397728
     [Google Scholar]
  31. ShiHongshuo YuanXin LiuGuobin FanWeijing Identifying and validating GSTM5 as an immunogenic gene in diabetic foot ulcer using bioinformatics and machine learning.J. Inflamm. Res.2023166241625610.2147/JIR.S442388
     [Google Scholar]
  32. RobinX. TurckN. HainardA. pROC: an open-source package for R and S+ to analyze and compare ROC curves.BMC Bioinformatics20111217710.1186/1471‑2105‑12‑77 21414208
     [Google Scholar]
  33. LiuTing-Ting LiRui HuoChen Identification of CDK2-related immune forecast model and cerna in lung adenocarcinoma, a pancancer analysis.Front. Cell Dev. Biol.2021968200210.3389/fcell.2021.682002
     [Google Scholar]
  34. LianY. WangQ. MuJ. Network pharmacology assessment of Qingkailing injection treatment of cholestatic hepatitis.J. Tradit. Chin. Med.202141116718010.19852/j.cnki.jtcm.20201208.001 33522210
     [Google Scholar]
  35. WilsonK.H.S. EckenrodeS.E. LiQ.Z. Microarray analysis of gene expression in the kidneys of new- and post-onset diabetic NOD mice.Diabetes20035282151215910.2337/diabetes.52.8.2151 12882935
     [Google Scholar]
  36. WoronieckaK.I. ParkA.S.D. MohtatD. ThomasD.B. PullmanJ.M. SusztakK. Transcriptome analysis of human diabetic kidney disease.Diabetes20116092354236910.2337/db10‑1181 21752957
     [Google Scholar]
  37. HodginJ.B. NairV. ZhangH. Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli.Diabetes201362129930810.2337/db11‑1667 23139354
     [Google Scholar]
  38. MiH. DongQ. MuruganujanA. GaudetP. LewisS. ThomasP.D. PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium.Nucleic Acids Res.201038Database issueSuppl. 1D204D21010.1093/nar/gkp1019 20015972
     [Google Scholar]
  39. YangW. LuJ. WengJ. Prevalence of diabetes among men and women in China.N. Engl. J. Med.2010362121090110110.1056/NEJMoa0908292 20335585
     [Google Scholar]
  40. LiuW. TangF. DengY. Berberine reduces fibronectin and collagen accumulation in rat glomerular mesangial cells cultured under high glucose condition.Mol. Cell. Biochem.20093251-29910510.1007/s11010‑008‑0024‑y 19142714
     [Google Scholar]
  41. PuffR. DamesP. WeiseM. Reduced proliferation and a high apoptotic frequency of pancreatic beta cells contribute to genetically-determined diabetes susceptibility of db/db BKS mice.Horm. Metab. Res.201143530631110.1055/s‑0031‑1271817 21412687
     [Google Scholar]
  42. ChunJ. HlaT. LynchK.R. SpiegelS. MoolenaarW.H. International union of basic and clinical pharmacology. LXXVIII. Lysophospholipid receptor nomenclature: TABLE 1.Pharmacol. Rev.201062457958710.1124/pr.110.003111 21079037
     [Google Scholar]
  43. UedaN. Ceramide-induced apoptosis in renal tubular cells: a role of mitochondria and sphingosine-1-phoshate.Int. J. Mol. Sci.201516125076512410.3390/ijms16035076 25751724
     [Google Scholar]
  44. LebrecJ.J.P. HuizingaT.W.J. ToesR.E.M. Houwing-DuistermaatJ.J. van HouwelingenH.C. Integration of gene ontology pathways with North American Rheumatoid Arthritis Consortium genome-wide association data via linear modeling.BMC Proc.20093S7Suppl. 7S9410.1186/1753‑6561‑3‑S7‑S94 20018091
     [Google Scholar]
  45. BlahoV.A. HlaT. An update on the biology of sphingosine 1-phosphate receptors.J. Lipid Res.20145581596160810.1194/jlr.R046300 24459205
     [Google Scholar]
  46. Tan-ChenS. GuittonJ. BourronO. Le StunffH. HajduchE. Sphingolipid metabolism and signaling in skeletal muscle: From physiology to physiopathology.Frontiers in endocrinology20201149110.3389/fendo.2020.00491
     [Google Scholar]
  47. LiX. LiuW. WangQ. Emodin suppresses cell proliferation and fibronectin expression via p38MAPK pathway in rat mesangial cells cultured under high glucose.Mol. Cell. Endocrinol.20093071-215716210.1016/j.mce.2009.03.006 19524136
     [Google Scholar]
  48. GurleyS.B. ClareS.E. SnowK.P. HuA. MeyerT.W. CoffmanT.M. Impact of genetic background on nephropathy in diabetic mice.Am. J. Physiol. Renal Physiol.20062901F214F22210.1152/ajprenal.00204.2005 16118394
     [Google Scholar]
  49. HolmL.J. KrogvoldL. HasselbyJ.P. Abnormal islet sphingolipid metabolism in type 1 diabetes.Diabetologia20186171650166110.1007/s00125‑018‑4614‑2 29671030
     [Google Scholar]
  50. MasonR.M. WahabN.A. Extracellular matrix metabolism in diabetic nephropathy.J. Am. Soc. Nephrol.20031451358137310.1097/01.ASN.0000065640.77499.D7 12707406
     [Google Scholar]
  51. AlaameryM. AlbesherN. AljawiniN. Role of sphingolipid metabolism in neurodegeneration.J. Neurochem.20211581253510.1111/jnc.15044 32402091
     [Google Scholar]
  52. SuiJ. HeM. WangY. ZhaoX. HeY. ShiB. Sphingolipid metabolism in type 2 diabetes and associated cardiovascular complications.Exp. Ther. Med.20191853603361410.3892/etm.2019.7981 31602237
     [Google Scholar]
  53. HuW. BielawskiJ. SamadF. MerrillA.H.Jr CowartL.A. Palmitate increases sphingosine-1-phosphate in C2C12 myotubes via upregulation of sphingosine kinase message and activity.J. Lipid Res.20095091852186210.1194/jlr.M800635‑JLR200 19369694
     [Google Scholar]
  54. EnglishD. GarciaJ.G. BrindleyD.N. Platelet-released phospholipids link hemostasis and angiogenesis Cardiovasc 2001; 49: 588-99.;Sadeghabadi ZA, Samani KG, Yaghubi F, Mohseni R. Chicoric acid ameliorates palmitate-induced sphingosine 1-phosphate signaling pathway in the PBMCs of patients with newly diagnosed type 2 diabetes.J. Diabetes Metab. Disord.202222130731410.1007/s40200‑022‑01134‑9
     [Google Scholar]
  55. SabanerM.C. AkdoganM. DoğanM. Inflammatory cytokines, oxidative and antioxidative stress levels in patients with diabetic macular edema and hyperreflective spots.Eur. J. Ophthalmol.20213152535254510.1177/1120672120962054 33008266
     [Google Scholar]
  56. PlowmanT.J. ShahM.H. FernandezE. ChristensenH. AigesM. RamanaK.V. Role of innate immune and inflammatory responses in the development of secondary diabetic complications.Curr. Mol. Med.202323990192010.2174/1566524023666220922114701 36154569
     [Google Scholar]
  57. ShikataK. MakinoH. Microinflammation in the pathogenesis of diabetic nephropathy.J. Diabetes Invest.20134142149
     [Google Scholar]
  58. GoldbergR.B. Cytokine and cytokine-like inflammation markers, endothelial dysfunction, and imbalanced coagulation in development of diabetes and its complications.J. Clin. Endocrinol. Metab.20099493171318210.1210/jc.2008‑2534 19509100
     [Google Scholar]
  59. FardonN.J.M. WilkinsonR. ThomasT.H. Abnormalities in primary granule exocytosis in neutrophils from Type I diabetic patients with nephropathy.Clin. Sci. (Lond.)20021021697510.1042/cs1020069 11749662
     [Google Scholar]
  60. SubeiA.M. CohenJ.A. Sphingosine 1-phosphate receptor modulators in multiple sclerosis.CNS Drugs201529756557510.1007/s40263‑015‑0261‑z 26239599
     [Google Scholar]
  61. RahmanI. L-selectin regulates human neutrophil transendothelial migration.J. Cell Sci.20211343jcs25034010.1242/jcs.250340
     [Google Scholar]
  62. SatoY. KannoS. OdaN. Properties of two VEGF receptors, Flt-1 and KDR, in signal transduction.Ann. N. Y. Acad. Sci.2000902120120710.1111/j.1749‑6632.2000.tb06314.x 10865839
     [Google Scholar]
  63. WangJ. HuangH. LiuP. Inhibition of phosphorylation of p38 MAPK involved in the protection of nephropathy by emodin in diabetic rats.Eur. J. Pharmacol.20065531-329730310.1016/j.ejphar.2006.08.087 17074319
     [Google Scholar]
  64. OliveraA. RosenfeldtH.M. BektasM. Sphingosine kinase type 1 induces G12/13-mediated stress fiber formation, yet promotes growth and survival independent of G protein-coupled receptors.J. Biol. Chem.200327847464524646010.1074/jbc.M308749200 12963721
     [Google Scholar]
  65. YangA. IshiiI. ChunJ. In vivo roles of lysophospholipid receptors revealed by gene targeting studies in mice.Biochim. Biophys. Acta Mol. Cell Biol. Lipids200215821-319720310.1016/S1388‑1981(02)00172‑5 12069829
     [Google Scholar]
  66. GleyK. MuraniE. HaackF. Haplotypes of coping behavior associated QTL regions reveal distinct transcript profiles in amygdala and hippocampus.Behav. Brain Res.201937211203810.1016/j.bbr.2019.112038 31202863
     [Google Scholar]
  67. MoyesK.M. DrackleyJ.K. MorinD.E. Gene network and pathway analysis of bovine mammary tissue challenged with Streptococcus uberis reveals induction of cell proliferation and inhibition of PPARγ signaling as potential mechanism for the negative relationships between immune response and lipid metabolism.BMC Genomics200910154210.1186/1471‑2164‑10‑542 19925655
     [Google Scholar]
  68. GuhaM. XuZ.G. TungD. LantingL. NatarajanR. Specific down‐regulation of connective tissue growth factor attenuates progression of nephropathy in mouse models of type 1 and type 2 diabetes.FASEB J.200721123355336810.1096/fj.06‑6713com 17554073
     [Google Scholar]
  69. LanT. ShenX. LiuP. RETRACTED: Berberine ameliorates renal injury in diabetic C57BL/6 mice: Involvement of suppression of SphK–S1P signaling pathway.Arch. Biochem. Biophys.2010502211212010.1016/j.abb.2010.07.012 20646989
     [Google Scholar]
  70. BaiY. WangL. LiY. High ambient glucose levels modulates the production of MMP-9 and alpha5(IV) collagen by cultured podocytes.Cell. Physiol. Biochem.2006171-2576810.1159/000091464 16543722
     [Google Scholar]
  71. WahabN. CoxD. WitherdenA. MasonR.M. Connective tissue growth factor (CTGF) promotes activated mesangial cell survival via up-regulation of mitogen-activated protein kinase phosphatase-1 (MKP-1).Biochem. J.2007406113113810.1042/BJ20061817 17489738
     [Google Scholar]
  72. ElliottH.R. SharpG.C. ReltonC.L. LawlorD.A. Epigenetics and gestational diabetes: a review of epigenetic epidemiology studies and their use to explore epigenetic mediation and improve prediction.Diabetologia201962122171217810.1007/s00125‑019‑05011‑8 31624900
     [Google Scholar]
  73. SiehlerS. ManningD.R. Pathways of transduction engaged by sphingosine 1-phosphate through G protein-coupled receptors.Biochim. Biophys. Acta Mol. Cell Biol. Lipids200215821-3949910.1016/S1388‑1981(02)00142‑7 12069815
     [Google Scholar]
  74. MastejK. AdamiecR. Neutrophil surface expression of CD11b and CD62L in diabetic microangiopathy.Acta Diabetol.200845318319010.1007/s00592‑008‑0040‑0 18496641
     [Google Scholar]
  75. KawanabeT. KawakamiT. YatomiY. ShimadaS. SomaY. Sphingosine 1-phosphate accelerates wound healing in diabetic mice.J. Dermatol. Sci.2007481536010.1016/j.jdermsci.2007.06.002 17643267
     [Google Scholar]
  76. KamiuchiK. HasegawaG. ObayashiH. Leukocyte–endothelial cell adhesion molecule 1 (LECAM-1) polymorphism is associated with diabetic nephropathy in type 2 diabetes mellitus.J. Diabetes Complications200216533333710.1016/S1056‑8727(01)00226‑4 12200076
     [Google Scholar]
  77. KaradayiK. TopC. GulecekO. The relationship between soluble L-selectin and the development of diabetic retinopathy.Ocul. Immunol. Inflamm.200311212312910.1076/ocii.11.2.123.15920
     [Google Scholar]
  78. TaharaA. TsukadaJ. TomuraY. YatsuT. ShibasakiM. Effects of high glucose on AVP-induced hyperplasia, hypertrophy, and type IV collagen synthesis in cultured rat mesangial cells.Endocr. Res.201237421622710.3109/07435800.2012.671400 22594926
     [Google Scholar]
  79. TutinoG.E. TamC.H.T. OzakiR. Long‐term maternal cardiometabolic outcomes 22 years after gestational diabetes mellitus.J. Diabetes Investig.202011498599310.1111/jdi.13209 31912653
     [Google Scholar]
  80. ZhangL. PangS. DengB. High glucose induces renal mesangial cell proliferation and fibronectin expression through JNK/NF-κB/NADPH oxidase/ROS pathway, which is inhibited by resveratrol.Int. J. Biochem. Cell Biol.201244462963810.1016/j.biocel.2012.01.001 22245600
     [Google Scholar]
  81. DammP. Houshmand-OeregaardA. KelstrupL. LauenborgJ. MathiesenE.R. ClausenT.D. Gestational diabetes mellitus and long-term consequences for mother and offspring: a view from Denmark.Diabetologia20165971396139910.1007/s00125‑016‑3985‑5 27174368
     [Google Scholar]
  82. HoweC.G. CoxB. ForeR. Maternal gestational diabetes mellitus and newborn DNA methylation: findings from the pregnancy and childhood epigenetics consortium.Diabetes Care20204319810510.2337/dc19‑0524 31601636
     [Google Scholar]
  83. VasuS. KumanoK. DardenC.M. RahmanI. LawrenceM.C. NaziruddinB. MicroRNA signatures as future biomarkers for diagnosis of diabetes states.Cells2019812153310.3390/cells8121533 31795194
     [Google Scholar]
  84. YoffeL. PolskyA. GilamA. Early diagnosis of gestational diabetes mellitus using circulating microRNAs.Eur. J. Endocrinol.2019181556557710.1530/EJE‑19‑0206 31539877
     [Google Scholar]
  85. StirmL. HuypensP. SassS. Maternal whole blood cell miRNA-340 is elevated in gestational diabetes and inversely regulated by glucose and insulin.Sci. Rep.201881136610.1038/s41598‑018‑19200‑9 29358694
     [Google Scholar]
  86. HaertleL. El HajjN. DittrichM. Epigenetic signatures of gestational diabetes mellitus on cord blood methylation.Clin. Epigenetics2017912810.1186/s13148‑017‑0329‑3 28360945
     [Google Scholar]
  87. ShangJ. WangL. ZhangY. Chemerin/ChemR23 axis promotes inflammation of glomerular endothelial cells in diabetic nephropathy.J. Cell. Mol. Med.20192353417342810.1111/jcmm.14237 30784180
     [Google Scholar]
  88. GroveK.J. VoziyanP.A. SpragginsJ.M. Diabetic nephropathy induces alterations in the glomerular and tubule lipid profiles.J. Lipid Res.20145571375138510.1194/jlr.M049189
     [Google Scholar]
  89. HickeyF.B. MartinF. Diabetic kidney disease and immune modulation.Curr. Opin. Pharmacol.201313460261210.1016/j.coph.2013.05.002 23721739
     [Google Scholar]
  90. RitchieM.E. PhipsonB. WuD. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res.2015437e4710.1093/nar/gkv007 25605792
     [Google Scholar]
  91. AgarwalV BellGW NamJW BartelDP Predicting effective microRNA target sites in mammalian mRNAs.eLife20154e0500510.7554/eLife.05005 26267216
     [Google Scholar]
  92. KorrapatiM.C. HowellL.H. ShanerB.E. MegyesiJ.K. SiskindL.J. SchnellmannR.G. Suramin: a potential therapy for diabetic nephropathy.PloS one20138e7365510.1371/journal.pone.0073655
     [Google Scholar]
  93. LahiriS. FutermanA.H. The metabolism and function of sphingolipids and glycosphingolipids.Cell. Mol. Life Sci.200764172270228410.1007/s00018‑007‑7076‑0 17558466
     [Google Scholar]
  94. LukinaE. WatmanN. DragoskyM. Eliglustat, an investigational oral therapy for Gaucher disease type 1: Phase 2 trial results after 4years of treatment.Blood Cells Mol. Dis.201453427427610.1016/j.bcmd.2014.04.002 24835462
     [Google Scholar]
  95. ChouC.H. ShresthaS. YangC.D. miRTarBase update 2018: A resource for experimentally validated microRNA-target interactions.Nucleic Acids Res.201846D1D296D30210.1093/nar/gkx1067 29126174
     [Google Scholar]
/content/journals/cdr/10.2174/0115733998297749240418071555
Loading
Bioinformatics to Identify Biomarkers of Diabetic Nephropathy based on Sphingolipid Metabolism and their Molecular Mechanisms
Curr. Diabetes Rev. 21, E070524229720 (2025); https://doi.org/10.2174/0115733998297749240418071555
/content/journals/cdr/10.2174/0115733998297749240418071555
/content/journals/cdr/10.2174/0115733998297749240418071555
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): biomarkers; Diabetic nephropathy; hyperinsulinemia; machine learning; RAS; sphingolipid metabolism

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test