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image of Identification of Ferroptosis-related Genes for Diabetic Nephropathy by Bioinformatics and Experimental Validation

Abstract

Objective

The present study delves into the exploration of diagnostic biomarkers linked with ferroptosis in the context of diabetic nephropathy, unraveling their underlying molecular mechanisms.

Methods

In this study, we retrieved datasets GSE96804 and GSE30529 as the training cohort, followed by screening for Differentially Expressed Genes (DEGs). By intersecting these DEGs with known ferroptosis-related genes, we obtained the differentially expressed genes related to ferroptosis (DEFGs). Subsequently, Weighted Correlation Network Analysis (WGCNA) was carried out to identify key modules associated with Diabetic Nephropathy (DN), culminating in the identification of a significant gene. Enrichment analysis and Gene Set Enrichment Analysis (GSEA) were then carried out on the DEFGs and genes linked to the significant gene. To validate our findings, we employed cohorts GSE30528 and GSE43950, utilizing ROC curve analysis to assess diagnostic efficacy for DN, as measured by the area under the curve (AUC). Immune cell infiltration was analyzed and compared between groups using the CIBERSORT algorithm. Bayesian co-localization analysis was performed to examine the co-location of DEFGs and DN. Finally, to validate the hub genes identified, we conducted quantitative real-time polymerase chain reaction (qRT-PCR) experiments .

Results

FUZ, GLI1, GLI2, GLI3, and DVL2 were identified as the hub genes. Functional enrichment analysis demonstrated that ferroptosis and immune response play an important role in DN. ROC analysis showed that the identified genes had good diagnostic efficiency in DN. The results of the immune infiltration analysis showed that there may be crosstalk between ferroptosis and immune cells in DN. Bayesian co-localization analysis revealed the genetic correlation between the hub genes and DN. The outcomes of the qRT-PCR analyses corroborated the reliability of the identified hub genes as robust molecular markers for targeted therapy in DN.

Conclusion

The interplay between immune inflammatory reactions and ferroptosis emerges as a crucial pathogenic mechanism, offering novel insights into the molecular therapy of DN. Furthermore, the identification of FUZ, GLI1, GLI2, GLI3, and DVL2 as potential targets holds promise for future therapeutic interventions aimed at treating DN.

© 2025 Bentham Science Publishers
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2025-01-24
2025-04-07

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References

  1. Jiang R. Law E. Zhou Z. Yang H. Wu E.Q. Seifeldin R. Clinical trajectories, healthcare resource use, and costs of diabetic nephropathy among patients with type 2 diabetes: A latent class analysis. Diabetes Ther. 2018 9 3 1021 1036 10.1007/s13300‑018‑0410‑8 29600504
    [Google Scholar]
  2. Chen Y. Liu Q. Shan Z. Mi W. Zhao Y. Li M. Wang B. Zheng X. Feng W. Catalpol ameliorates podocyte injury by stabilizing cytoskeleton and enhancing autophagy in diabetic nephropathy. Front. Pharmacol. 2019 10 1477 10.3389/fphar.2019.01477 31920663
    [Google Scholar]
  3. Yang C. Wang H. Zhao X. Matsushita K. Coresh J. Zhang L. Zhao M.H. CKD in China: Evolving spectrum and public health implications. Am. J. Kidney Dis. 2020 76 2 258 264 10.1053/j.ajkd.2019.05.032 31492486
    [Google Scholar]
  4. Calle P. Hotter G. Macrophage phenotype and fibrosis in diabetic nephropathy. Int. J. Mol. Sci. 2020 21 8 2806 10.3390/ijms21082806 32316547
    [Google Scholar]
  5. Sun H.J. Xiong S.P. Cao X. Cao L. Zhu M.Y. Wu Z.Y. Bian J.S. Polysulfide-mediated sulfhydration of SIRT1 prevents diabetic nephropathy by suppressing phosphorylation and acetylation of p65 NF-κB and STAT3. Redox Biol. 2021 38 101813 10.1016/j.redox.2020.101813 33279869
    [Google Scholar]
  6. Qiao S. Liu R. Lv C. Miao Y. Yue M. Tao Y. Wei Z. Xia Y. Dai Y. Bergenin impedes the generation of extracellular matrix in glomerular mesangial cells and ameliorates diabetic nephropathy in mice by inhibiting oxidative stress via the mTOR/β-TrcP/Nrf2 pathway. Free Radic. Biol. Med. 2019 145 118 135 10.1016/j.freeradbiomed.2019.09.003 31494242
    [Google Scholar]
  7. Typiak M. Piwkowska A. Antiinflammatory actions of Klotho: Implications for therapy of diabetic nephropathy. Int. J. Mol. Sci. 2021 22 2 956 10.3390/ijms22020956 33478014
    [Google Scholar]
  8. Saleh H. Salama M. Hussein R.M. Polyethylene glycol capped gold nanoparticles ameliorate renal ischemia–reperfusion injury in diabetic mice through AMPK-Nrf2 signaling pathway. Environ. Sci. Pollut. Res. Int. 2022 29 51 77884 77907 10.1007/s11356‑022‑21235‑5 35688972
    [Google Scholar]
  9. Kim S. Kang S.W. Joo J. Han S.H. Shin H. Nam B.Y. Park J. Yoo T.H. Kim G. Lee P. Park J.T. Characterization of ferroptosis in kidney tubular cell death under diabetic conditions. Cell Death Dis. 2021 12 2 160 10.1038/s41419‑021‑03452‑x 33558472
    [Google Scholar]
  10. Chen Y. Huang G. Qin T. Zhang Z. Wang H. Xu Y. Shen X. Ferroptosis: A new view on the prevention and treatment of diabetic kidney disease with traditional Chinese medicine. Biomed. Pharmacother. 2024 170 115952 10.1016/j.biopha.2023.115952 38056233
    [Google Scholar]
  11. Pan Y. Jiang S. Hou Q. Qiu D. Shi J. Wang L. Chen Z. Zhang M. Duan A. Qin W. Zen K. Liu Z. Dissection of glomerular transcriptional profile in patients with diabetic nephropathy: SRGAP2a protects podocyte structure and function. Diabetes 2018 67 4 717 730 10.2337/db17‑0755 29242313
    [Google Scholar]
  12. Shi J.S. Qiu D.D. Le W.B. Wang H. Li S. Lu Y.H. Jiang S. Identification of transcription regulatory relationships in diabetic nephropathy. Chin. Med. J. (Engl.) 2018 131 23 2886 2890 30511699
    [Google Scholar]
  13. Woroniecka K.I. Park A.S.D. Mohtat D. Thomas D.B. Pullman J.M. Susztak K. Transcriptome analysis of human diabetic kidney disease. Diabetes 2011 60 9 2354 2369 10.2337/db10‑1181 21752957
    [Google Scholar]
  14. Huber W. Carey V.J. Gentleman R. Anders S. Carlson M. Carvalho B.S. Bravo H.C. Davis S. Gatto L. Girke T. Gottardo R. Hahne F. Hansen K.D. Irizarry R.A. Lawrence M. Love M.I. MacDonald J. Obenchain V. Oleś A.K. Pagès H. Reyes A. Shannon P. Smyth G.K. Tenenbaum D. Waldron L. Morgan M. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 2015 12 2 115 121 10.1038/nmeth.3252 25633503
    [Google Scholar]
  15. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 43 7 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  16. Huang Y. Yang D.D. Li X.Y. Fang D.L. Zhou W.J. ZBP1 is a significant pyroptosis regulator for systemic lupus erythematosus. Ann. Transl. Med. 2021 9 24 1773 10.21037/atm‑21‑6193 35071467
    [Google Scholar]
  17. Langfelder P. Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008 9 1 559 10.1186/1471‑2105‑9‑559 19114008
    [Google Scholar]
  18. Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005 102 43 15545 15550 10.1073/pnas.0506580102 16199517
    [Google Scholar]
  19. Liberzon A. Subramanian A. Pinchback R. Thorvaldsdóttir H. Tamayo P. Mesirov J.P. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011 27 12 1739 1740 10.1093/bioinformatics/btr260 21546393
    [Google Scholar]
  20. Wang Q. Qiao W. Zhang H. Liu B. Li J. Zang C. Mei T. Zheng J. Zhang Y. Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma. Front. Immunol. 2022 13 1019638 10.3389/fimmu.2022.1019638 36505501
    [Google Scholar]
  21. Newman A.M. Liu C.L. Green M.R. Gentles A.J. Feng W. Xu Y. Hoang C.D. Diehn M. Alizadeh A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 2015 12 5 453 457 10.1038/nmeth.3337 25822800
    [Google Scholar]
  22. Wallace C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet. 2020 16 4 e1008720 10.1371/journal.pgen.1008720 32310995
    [Google Scholar]
  23. Ou Y.N. Yang Y.X. Deng Y.T. Zhang C. Hu H. Wu B.S. Liu Y. Wang Y.J. Zhu Y. Suckling J. Tan L. Yu J.T. Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood. Mol. Psychiatry 2021 26 10 6065 6073 10.1038/s41380‑021‑01251‑6 34381170
    [Google Scholar]
  24. Zhang B. Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005 4 10.2202/1544‑6115.1128 16646834
    [Google Scholar]
  25. Kanehisa M. Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 28 1 27 30 10.1093/nar/28.1.27 10592173
    [Google Scholar]
  26. Kanehisa M. Furumichi M. Sato Y. Kawashima M. Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023 51 D1 D587 D592 10.1093/nar/gkac963 36300620
    [Google Scholar]
  27. Geng X. Wang W. Feng Z. Liu R. Cheng X. Shen W. Dong Z. Cai G. Chen X. Hong Q. Wu D. Identification of key genes and pathways in diabetic nephropathy by bioinformatics analysis. J. Diabetes Investig. 2019 10 4 972 984 10.1111/jdi.12986 30536626
    [Google Scholar]
  28. Wu Y. Chen Y. Research progress on ferroptosis in diabetic kidney disease. Front. Endocrinol. (Lausanne) 2022 13 945976 10.3389/fendo.2022.945976 36246888
    [Google Scholar]
  29. Hu Y. Shi R. Mo R. Hu F. Nomogram for the prediction of diabetic nephropathy risk among patients with type 2 diabetes mellitus based on a questionnaire and biochemical indicators: A retrospective study. Aging (Albany NY) 2020 12 11 10317 10336 10.18632/aging.103259 32484786
    [Google Scholar]
  30. Wu Y. Zhao Y. Yang H. Wang Y. Chen Y. HMGB1 regulates ferroptosis through Nrf2 pathway in mesangial cells in response to high glucose. Biosci. Rep. 2021 41 2 BSR20202924 10.1042/BSR20202924 33565572
    [Google Scholar]
  31. Yang R. Gao W. Wang Z. Jian H. Peng L. Yu X. Xue P. Peng W. Li K. Zeng P. Polyphyllin I induced ferroptosis to suppress the progression of hepatocellular carcinoma through activation of the mitochondrial dysfunction via Nrf2/HO-1/GPX4 axis. Phytomedicine 2024 122 155135 10.1016/j.phymed.2023.155135 37856990
    [Google Scholar]
  32. Wang Y. Bi R. Quan F. Cao Q. Lin Y. Yue C. Cui X. Yang H. Gao X. Zhang D. Ferroptosis involves in renal tubular cell death in diabetic nephropathy. Eur. J. Pharmacol. 2020 888 173574 10.1016/j.ejphar.2020.173574 32976829
    [Google Scholar]
  33. Wang M.Z. Cai Y.F. Fang Q.J. Liu Y.L. Wang J. Chen J.X. Fu Y. Wan B.Y. Tu Y. Wu W. Wan Y.G. Mu G.L. Inhibition of ferroptosis of renal tubular cells with total flavones of Abelmoschus manihot alleviates diabetic tubulopathy. Anat. Rec. (Hoboken) 2023 306 12 3199 3213 10.1002/ar.25123 36440653
    [Google Scholar]
  34. Park T.J. Haigo S.L. Wallingford J.B. Ciliogenesis defects in embryos lacking inturned or fuzzy function are associated with failure of planar cell polarity and Hedgehog signaling. Nat. Genet. 2006 38 3 303 311 10.1038/ng1753 16493421
    [Google Scholar]
  35. Adler P.N. Planar signaling and morphogenesis in Drosophila. Dev. Cell 2002 2 5 525 535 10.1016/S1534‑5807(02)00176‑4 12015961
    [Google Scholar]
  36. He M. Li K. Yu C. Lv B. Zhao N. Deng J. Cao L. Huang H. Yin A. Shi T. Wang L. In vitro study of FUZ as a novel potential therapeutic target in non-small-cell lung cancer. Life Sci. 2018 197 91 100 10.1016/j.lfs.2018.02.007 29421438
    [Google Scholar]
  37. Kramann R. Hedgehog Gli signalling in kidney fibrosis. Nephrol. Dial. Transplant. 2016 31 12 1989 1995 10.1093/ndt/gfw102 27229466
    [Google Scholar]
  38. Doheny D. Manore S.G. Wong G.L. Lo H.W. Hedgehog signaling and truncated GLI1 in cancer. Cells 2020 9 9 2114 10.3390/cells9092114 32957513
    [Google Scholar]
  39. Skoda A.M. Simovic D. Karin V. Kardum V. Vranic S. Serman L. The role of the Hedgehog signaling pathway in cancer: A comprehensive review. Bosn. J. Basic Med. Sci. 2018 18 1 8 20 10.17305/bjbms.2018.2756 29274272
    [Google Scholar]
  40. Kramann R. Fleig S.V. Schneider R.K. Fabian S.L. DiRocco D.P. Maarouf O. Wongboonsin J. Ikeda Y. Heckl D. Chang S.L. Rennke H.G. Waikar S.S. Humphreys B.D. Pharmacological GLI2 inhibition prevents myofibroblast cell-cycle progression and reduces kidney fibrosis. J. Clin. Invest. 2015 125 8 2935 2951 10.1172/JCI74929 26193634
    [Google Scholar]
  41. Shen M. Zhang Z. Wang P. GLI3 promotes invasion and predicts poor prognosis in colorectal cancer. BioMed Res. Int. 2021 2021 1 10 10.1155/2021/8889986 33506047
    [Google Scholar]
  42. Peng J. Zhang D. Coexpression of EphA10 and Gli3 promotes breast cancer cell proliferation, invasion and migration. J. Investig. Med. 2021 69 6 1215 1221 10.1136/jim‑2021‑001836 33990369
    [Google Scholar]
  43. Chang B. Tessneer K.L. McManus J. Liu X. Hahn S. Pasula S. Wu H. Song H. Chen Y. Cai X. Dong Y. Brophy M.L. Rahman R. Ma J.X. Xia L. Chen H. Epsin is required for Dishevelled stability and Wnt signalling activation in colon cancer development. Nat. Commun. 2015 6 1 6380 10.1038/ncomms7380 25871009
    [Google Scholar]
  44. Zhang H. Nair V. Saha J. Atkins K.B. Hodgin J.B. Saunders T.L. Myers M.G. Jr Werner T. Kretzler M. Brosius F.C. Podocyte-specific JAK2 overexpression worsens diabetic kidney disease in mice. Kidney Int. 2017 92 4 909 921 10.1016/j.kint.2017.03.027 28554737
    [Google Scholar]
  45. Bernard E. Nannya Y. Hasserjian R.P. Devlin S.M. Tuechler H. Medina-Martinez J.S. Yoshizato T. Shiozawa Y. Saiki R. Malcovati L. Levine M.F. Arango J.E. Zhou Y. Solé F. Cargo C.A. Haase D. Creignou M. Germing U. Zhang Y. Gundem G. Sarian A. van de Loosdrecht A.A. Jädersten M. Tobiasson M. Kosmider O. Follo M.Y. Thol F. Pinheiro R.F. Santini V. Kotsianidis I. Boultwood J. Santos F.P.S. Schanz J. Kasahara S. Ishikawa T. Tsurumi H. Takaori-Kondo A. Kiguchi T. Polprasert C. Bennett J.M. Klimek V.M. Savona M.R. Belickova M. Ganster C. Palomo L. Sanz G. Ades L. Della Porta M.G. Elias H.K. Smith A.G. Werner Y. Patel M. Viale A. Vanness K. Neuberg D.S. Stevenson K.E. Menghrajani K. Bolton K.L. Fenaux P. Pellagatti A. Platzbecker U. Heuser M. Valent P. Chiba S. Miyazaki Y. Finelli C. Voso M.T. Shih L.Y. Fontenay M. Jansen J.H. Cervera J. Atsuta Y. Gattermann N. Ebert B.L. Bejar R. Greenberg P.L. Cazzola M. Hellström-Lindberg E. Ogawa S. Papaemmanuil E. Implications of TP53 allelic state for genome stability, clinical presentation and outcomes in myelodysplastic syndromes. Nat. Med. 2020 26 10 1549 1556 10.1038/s41591‑020‑1008‑z 32747829
    [Google Scholar]
  46. Chen L. Wang C. Wang Y. Hong T. Zhang G. Cui X. Functions, roles, and biological processes of ferroptosis-related genes in renal cancer: A pan-renal cancer analysis. Front. Oncol. 2022 11 697697 10.3389/fonc.2021.697697 35360452
    [Google Scholar]
  47. He X. Cilia put a brake on Wnt signalling. Nat. Cell Biol. 2008 10 1 11 13 10.1038/ncb0108‑11 18172427
    [Google Scholar]
  48. Miao J. Liu J. Niu J. Zhang Y. Shen W. Luo C. Liu Y. Li C. Li H. Yang P. Liu Y. Hou F.F. Zhou L. Wnt/β‐catenin/RAS signaling mediates age‐related renal fibrosis and is associated with mitochondrial dysfunction. Aging Cell 2019 18 5 e13004 10.1111/acel.13004 31318148
    [Google Scholar]
  49. Edeling M. Ragi G. Huang S. Pavenstädt H. Susztak K. Developmental signalling pathways in renal fibrosis: The roles of Notch, Wnt and Hedgehog. Nat. Rev. Nephrol. 2016 12 7 426 439 10.1038/nrneph.2016.54 27140856
    [Google Scholar]
  50. Chen X. Kang R. Kroemer G. Tang D. Ferroptosis in infection, inflammation, and immunity. J. Exp. Med. 2021 218 6 e20210518 10.1084/jem.20210518 33978684
    [Google Scholar]
  51. Matsushita M. Freigang S. Schneider C. Conrad M. Bornkamm G.W. Kopf M. T cell lipid peroxidation induces ferroptosis and prevents immunity to infection. J. Exp. Med. 2015 212 4 555 568 10.1084/jem.20140857 25824823
    [Google Scholar]
  52. Song S. Yu J. Identification of the shared genes in type 2 diabetes mellitus and osteoarthritis and the role of quercetin. J. Cell. Mol. Med. 2024 28 4 e18127 10.1111/jcmm.18127 38332532
    [Google Scholar]
  53. Zschocke J. Byers P.H. Wilkie A.O.M. Mendelian inheritance revisited: Dominance and recessiveness in medical genetics. Nat. Rev. Genet. 2023 24 7 442 463 10.1038/s41576‑023‑00574‑0 36806206
    [Google Scholar]
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Identification of Ferroptosis-related Genes for Diabetic Nephropathy by Bioinformatics and Experimental Validation
Bentham Science Publishers ; https://doi.org/10.2174/0113816128349101250102113613
/content/journals/cpd/10.2174/0113816128349101250102113613
/content/journals/cpd/10.2174/0113816128349101250102113613
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  • Article Type:     Research Article
Keywords: ferroptosis ; diabetic nephropathy ; Bayesian co-localization analysis ; Bioinformatics ; immune-inflammatory reaction ; experimental validation

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