Machine-learning algorithm-based prediction of a diagnostic model based on oxidative stress-related genes involved in immune infiltration in diabetic nephropathy patients
Open accessAbstract
Diabetic nephropathy (DN) is the most prevalent microvascular consequence of diabetes and has recently risen to the position of the world’s second biggest cause of end-stage renal diseases. Growing studies suggest that oxidative stress (OS) responses are connected to the advancement of DN. This study aimed to developed a novel diagnostic model based on OS-related genes. The differentially expressed oxidative stress-related genes (DE-OSRGs) experiments required two human gene expression datasets, which were given by the GEO database (GSE30528 and GSE96804, respectively). The potential diagnostic genes were identified using the SVM-RFE assays and the LASSO regression model. CIBERSORT was used to determine the compositional patterns of the 22 different kinds of immune cell fraction seen in DN. These estimates were based on the combined cohorts. DN serum samples and normal samples were both subjected to RT-PCR in order to investigate the degree to which certain genes were expressed. In this study, we were able to locate 774 DE-OSRGs in DN. The three marker genes (DUSP1, PRDX6 and S100A8) were discovered via machine learning on two different machines. The high diagnostic value was validated by ROC tests, which focused on distinguishing DN samples from normal samples. The results of the CIBERSORT study suggested that DUSP1, PRDX6, and S100A8 may be associated to the alterations that occur in the immunological microenvironment of DN patients. Besides, the results of RT-PCR indicated that the expression of DUSP1, PRDX6, and S100A8 was much lower in DN serum samples compared normal serum samples. The diagnostic value of the proposed model was likewise verified in our cohort, with an area under the curve of 9.946. Overall, DUSP1, PRDX6, and S100A8 were identified to be the three diagnostic characteristic genes of DN. It’s possible that combining these genes will be effective in diagnosing DN and determining the extent of immune cell infiltration.
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Most molecular-omics-driven DN prediction studies were single-center; only two transcriptomics studies utilized public multi-cohort integrative to enable external validation.
“most studies were single-center; only two transcriptomics studies utilized public multi-cohort integrative to enable external validation [76, 77].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [76]
The Materials and methods section (no page given) says the study downloaded two public GEO expression datasets, GSE30528 and GSE96804. The Results section reports testing the diagnostic model on GSE96804 and then on GSE30528, supporting its use of a second cohort for validation. The paper does not establish that most other studies were single-center or that only two transcriptomics studies used this approach. (LLM, conf=0.86)
Experimental — accuracy not yet measuredUsing ML, one study identified three key oxidative stress-related genes, DUSP1, PRDX6, and S100A8, with strong diagnostic value.
“Using ML, one study identified three key oxidative stress-related genes, DUSP1, PRDX6, and S100A8, with strong diagnostic value [76]”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [76]
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Cites
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 12,116 · see citing works
Role of nrf2 in oxidative stress and toxicity
Ma, Q. (2013) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-011112-140320 · Cited by 4,903 · see citing works
Modulation of oxidative stress as an anticancer strategy
Gorrini, C., Harris, I. S., & Mak, T. W. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4002 · Cited by 3,579 · see citing works
Oxidative stress: a concept in redox biology and medicine
Sies, H. (2015) · Redox Biology · doi:10.1016/j.redox.2015.01.002 · Cited by 3,015 · see citing works
Diabetic Kidney Disease: Challenges, Progress, and Possibilities
Alicic, R. Z., Rooney, M. T., & Tuttle, K. R. (2017) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.11491116 · Cited by 2,898 · see citing works
The role of antioxidants in the chemistry of oxidative stress: A review
Pisoschi, A. M., & Pop, A. (2015) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2015.04.040 · Cited by 2,844 · see citing works
Oxidative stress and autophagy: The clash between damage and metabolic needs
Filomeni, G., De Zio, D., & Cecconi, F. (2015) · Cell Death & Differentiation · doi:10.1038/cdd.2014.150 · Cited by 2,195 · see citing works
Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes
Esser, N., Legrand-Poels, S., Piette, J., Scheen, A. J., & Paquot, N. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.04.006 · Cited by 1,981 · see citing works
Feldman, E. L., Callaghan, B. C., Pop-Busui, R., Zochodne, D. W., Wright, D. E., Bennett, D. L., Bril, V., Russell, J. W., & Viswanathan, V. (2019) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0092-1 · Cited by 1,697 · see citing works
Wang, S., Song, R., Wang, Z., Jing, Z., Wang, S., & Ma, J. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01298 · Cited by 1,450 · see citing works
Update on Diabetic Nephropathy: Core Curriculum 2018
Umanath, K., & Lewis, J. B. (2018) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2017.10.026 · Cited by 1,009 · see citing works
Diabetic Nephropathy: Challenges in Pathogenesis, Diagnosis, and Treatment
Samsu, N. (2021) · BioMed Research International · doi:10.1155/2021/1497449 · Cited by 1,001 · see citing works
Dendritic cell-based immunotherapy
Sabado, R. L., Balan, S., & Bhardwaj, N. (2016) · Cell Research · doi:10.1038/cr.2016.157 · Cited by 926 · see citing works
Inflammation and the pathogenesis of diabetic nephropathy
Wada, J., & Makino, H. (2012) · Clinical Science · doi:10.1042/cs20120198 · Cited by 775 · see citing works
A Glimpse of Various Pathogenetic Mechanisms of Diabetic Nephropathy
Kanwar, Y. S., Sun, L., Xie, P., Liu, F.-Y., & Chen, S. (2011) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev.pathol.4.110807.092150 · Cited by 772 · see citing works
Diabetes and Kidney Disease: Role of Oxidative Stress
Jha, J. C., Banal, C., Chow, B. S. M., Cooper, M. E., & Jandeleit-Dahm, K. (2016) · Antioxidants & Redox Signaling · doi:10.1089/ars.2016.6664 · Cited by 660 · see citing works
Update of pathophysiology and management of diabetic kidney disease
Lin, Y., Chang, Y.-H., Yang, S., Wu, K., & Chu, T. (2018) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2018.02.007 · Cited by 564 · see citing works
Calprotectin: from biomarker to biological function
Jukic, A., Bakiri, L., Wagner, E. F., Tilg, H., & Adolph, T. E. (2021) · Gut · doi:10.1136/gutjnl-2021-324855 · Cited by 500 · see citing works
Inflammation in Diabetic Nephropathy
Lim, A. K. H., & Tesch, G. H. (2012) · Mediators of Inflammation · doi:10.1155/2012/146154 · Cited by 439 · see citing works
Innate immunity in diabetes and diabetic nephropathy
Wada, J., & Makino, H. (2015) · Nature Reviews Nephrology · doi:10.1038/nrneph.2015.175 · Cited by 432 · see citing works
The role of the complement system in diabetic nephropathy
Flyvbjerg, A. (2017) · Nature Reviews Nephrology · doi:10.1038/nrneph.2017.31 · Cited by 397 · see citing works
Pathogenic Pathways and Therapeutic Approaches Targeting Inflammation in Diabetic Nephropathy
Rayego‐Mateos, S., Morgado‐Pascual, J. L., Opazo-Ríos, L., Guerrero‐Hue, M., García‐Caballero, C., Vázquez‐Carballo, C., Mas, S., Sanz, A. B., Herencia, C., Mezzano, S. A., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21113798 · Cited by 342 · see citing works
Thipsawat, S. (2021) · Diabetes and Vascular Disease Research · doi:10.1177/14791641211058856 · Cited by 333 · see citing works
Classification and Differential Diagnosis of Diabetic Nephropathy
Qi, C., Mao, X., Zhang, Z., & Wu, H. (2017) · Journal of Diabetes Research · doi:10.1155/2017/8637138 · Cited by 301 · see citing works
Introduction to diabetes mellitus.
Kaul, K., Tarr, J., Ahmad, S., Kohner, E., & Chibber, R. (2012) · Advances in Experimental Medicine and Biology · doi:10.1007/978-1-4614-5441-0_1 · Cited by 293 · see citing works
Ma, L., Wu, F., Shao, Q., Chen, G., Xu, L., & Lu, F. (2021) · Drug Design Development and Therapy · doi:10.2147/dddt.s319260 · Cited by 276 · see citing works
Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease
Jung, C., & Yoo, T. (2022) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2021.0329 · Cited by 274 · see citing works
Inflammatory Targets in Diabetic Nephropathy
Donate‐Correa, J., Luis-Rodríguez, D., Martín‐Núñez, E., Tagua, V. G., Hernández-Carballo, C., Ferri, C. M., Rodríguez‐Rodríguez, A. E., Fernández, C. M., & Navarro‐González, J. F. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020458 · Cited by 267 · see citing works
Macrophage Phenotype and Fibrosis in Diabetic Nephropathy
Calle, P., & Hotter, G. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082806 · Cited by 250 · see citing works
A systematic review of p53 regulation of oxidative stress in skeletal muscle
Beyfuss, K. A., & Hood, D. A. (2018) · Redox Report · doi:10.1080/13510002.2017.1416773 · Cited by 239 · see citing works
Diabetic nephropathy: Is there a role for oxidative stress?
Sagoo, M. K., & Gnudi, L. (2018) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2017.12.040 · Cited by 233 · see citing works
Oxidative stress and antioxidant strategies in dermatology
Baek, J.-O., & Lee, M. (2015) · Redox Report · doi:10.1179/1351000215y.0000000015 · Cited by 224 · see citing works
Acute Kidney Injury and Progression of Diabetic Kidney Disease
Yu, S. M.-W., & Bonventre, J. V. (2018) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2017.12.005 · Cited by 181 · see citing works
Diabetic nephropathy--a review of the natural history, burden, risk factors and treatment.
Ayodele, O. E., Alebiosu, C. O., & Salako, B. L. (2004) · PubMed Central · Cited by 174 · see citing works
Zhang, Q.-J., Hu, Y., Hu, J.-E., Ding, Y., Shen, Y., Xu, H., Chen, H., & Wu, N. (2021) · Life Sciences · doi:10.1016/j.lfs.2021.119529 · Cited by 170 · see citing works
Dai, X., Liao, R., Liu, C., Liu, S., Huang, H., Liu, J., Jin, T., Guo, H., Zheng, Z., Xia, M., et al. (2021) · Redox Biology · doi:10.1016/j.redox.2021.102033 · Cited by 147 · see citing works
Epigenetics in the pathogenesis of diabetic nephropathy
Li, X., Lu, L., Hou, W., Huang, T., Chen, X., Qi, J., Zhao, Y., & Zhu, M. (2021) · Acta Biochimica et Biophysica Sinica · doi:10.3724/abbs.2021016 · Cited by 140 · see citing works
Roles and crosstalks of macrophages in diabetic nephropathy
Li, H.-D., You, Y., Shao, B., Wu, W., Wang, Y.-F., Guo, J.-B., Meng, X., & Chen, H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1015142 · Cited by 134 · see citing works
Biomarkers of diabetic nephropathy: A 2017 update
Papadopoulou‐Marketou, N., Kanaka‐Gantenbein, C., Marketos, N., Chrousos, G. P., & Papassotiriou, I. (2017) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2017.1377682 · Cited by 134 · see citing works
Interleukin‐18 and diabetic nephropathy: A review
Yaribeygi, H., Atkin, S. L., & Sahebkar, A. (2018) · Journal of Cellular Physiology · doi:10.1002/jcp.27427 · Cited by 121 · see citing works
Liu, X., Hu, X., Yang, H., Gui, L.-M., Cai, Z.-X., Qi, M.-S., & Dai, C. (2022) · The American Journal of Chinese Medicine · doi:10.1142/s0192415x22500744 · Cited by 121 · see citing works
Ge, Y., Wang, J., Wu, D., Zhou, Y., Qiu, S., Chen, J., Zhu, X., Xiang, X., Li, H., & Zhang, D. (2019) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2019.07.007 · Cited by 118 · see citing works
Insights into Diabetic Kidney Disease Using Urinary Proteomics and Bioinformatics
Van, J. A. D., Scholey, J. W., & Konvalinka, A. (2017) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016091018 · Cited by 116 · see citing works
The Role of Peroxiredoxin 6 in Cell Signaling
Arevalo, J. A., & Vázquez‐Medina, J. P. (2018) · Antioxidants · doi:10.3390/antiox7120172 · Cited by 108 · see citing works
Nonproteinuric diabetic kidney disease
Yamanouchi, M., Furuichi, K., Hoshino, J., Ubara, Y., & Wada, T. (2020) · Clinical and Experimental Nephrology · doi:10.1007/s10157-020-01881-0 · Cited by 106 · see citing works
Maternal immune protection against infectious diseases
Langel, S. N., Blasi, M., & Permar, S. R. (2022) · Cell Host & Microbe · doi:10.1016/j.chom.2022.04.007 · Cited by 97 · see citing works
Atherogenic dyslipidemia and diabetic nephropathy
Russo, G. T., Piscitelli, P. A., Giandalia, A., Viazzi, F. C., Pontremoli, R., Fioretto, P., & Cosmo, S. D. (2020) · Journal of Nephrology · doi:10.1007/s40620-020-00739-8 · Cited by 90 · see citing works
Insights into predicting diabetic nephropathy using urinary biomarkers
Khan, N. U., Lin, J., Liu, X., Li, H., Lu, W., Zhong, Z., Zhang, H., Waqas, M., & Shen, L. (2020) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2020.140475 · Cited by 90 · see citing works
Emerging roles of neutrophil-borne S100A8/A9 in cardiovascular inflammation
Sreejit, G., Abdel‐Latif, A., Murphy, A. J., & Nagareddy, P. R. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.105212 · Cited by 72 · see citing works
Mitogen‐Activated Protein Kinase Phosphatase 1 as an Inflammatory Factor and Drug Target
Korhonen, R., & Moilanen, E. (2013) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/bcpt.12141 · Cited by 71 · see citing works
Non-invasive assessment of early stage diabetic nephropathy by DTI and BOLD MRI
Feng, Y., Ye, Y., Cheng, Z., Hu, J., Zhang, C., Qian, L., Lu, X.-H., & Cai, X. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190562 · Cited by 63 · see citing works
Peroxiredoxin 6: The Protector of Male Fertility
O’Flaherty, C. (2018) · Antioxidants · doi:10.3390/antiox7120173 · Cited by 62 · see citing works
Tziastoudi, M., Stefanidis, I., & Zintzaras, E. (2020) · Clinical Kidney Journal · doi:10.1093/ckj/sfaa077 · Cited by 62 · see citing works
PDIA3 epitope-driven immune autoreactivity contributes to hepatic damage in type 2 diabetes
Clement, C. C., Osan, J. K., Buqué, A., Nanaware, P. P., Chang, Y., Perino, G., Shetty, M., Yamazaki, T., Tsai, W. L., Urbanska, A. M., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abl3795 · Cited by 48 · see citing works
The S100 protein family in lung cancer
Wang, T., Du, G., & Wang, D. (2021) · Clinica Chimica Acta · doi:10.1016/j.cca.2021.05.028 · Cited by 47 · see citing works
The Roles of Peroxiredoxin 6 in Brain Diseases
Liao, J., Zhang, Y., Chen, X., & Zhang, J. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02427-5 · Cited by 39 · see citing works
Role of Dendritic Cell in Diabetic Nephropathy
Kim, H., Kim, M., Lee, H. Y., Park, H., Jhun, H., & Kim, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22147554 · Cited by 34 · see citing works
Concise review: current trends on applications of stem cells in diabetic nephropathy
Liu, D., Zheng, W., Pan, S., & Liu, Z. (2020) · Cell Death and Disease · doi:10.1038/s41419-020-03206-1 · Cited by 21 · see citing works
Li, S., Xie, H., Shi, Y., & Liu, H. (2022) · Medicine · doi:10.1097/md.0000000000031232 · Cited by 20 · see citing works
Diabetic nephropathy--a review.
Acharya, V. N., & Chawla, K. P. (1978) · PubMed · Cited by 5 · see citing works
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García-Llorens, G., Ouardi, M. E., & Valls‐Bellés, V. (2025) · Applied Sciences · doi:10.3390/app151810191 · Cited by 66 · see citing works
Zhou, X., Zhou, H., Luo, X., & Wu, R. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1388690 · Cited by 19 · see citing works
Dholariya, S. J., Dutta, S., Sonagra, A. D., Kaliya, M., Singh, R., Parchwani, D. N., & Motiani, A. (2024) · Current Medical Research and Opinion · doi:10.1080/03007995.2024.2423737 · Cited by 15 · see citing works
Wang, L., Su, J., Liu, Z., Ding, S., Li, Y., Hou, B., Hu, Y., Dong, Z., Tang, J., Liu, H., et al. (2024) · BioData Mining · doi:10.1186/s13040-024-00369-x · Cited by 11 · see citing works
Ren, Y., & Zhang, H. (2024) · International Urology and Nephrology · doi:10.1007/s11255-024-04017-5 · Cited by 6 · see citing works
Xie, X., Wu, C., Huang, Z., Zhou, Y., Huang, J., Dao, F., Yan, D., Deng, K., Lyu, H., Ma, C., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag083 · Cited by 4 · see citing works
“Cohort sizes were generally small, ranging from 48 to562participants,and moststudiesweresingle-center;onlytwotran-scriptomics studies utilized public multi-cohort integrative to enable external validation [76, 77].”
Zhang, J., Cheng, L. L., Jiang, S., & Zhu, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-99395-w · Cited by 3 · see citing works
Mechanisms, Impact, and Therapeutic Strategies for Oxidative Stress in Metabolic Diseases
Reyed, R. M., & Prabhakar, P. K. (2025) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-7919-6.ch006 · Cited by 3 · see citing works
Wang, Y., Wang, J. C., & Chen, H. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07746-6 · Cited by 4 · see citing works
Tang, H., Li, K., & Wang, X. (2025) · Molecular Medicine Reports · doi:10.3892/mmr.2025.13614
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Aldaghi, T., Chrissan, A., Mužík, J., Rezaei-Tavirani, M., & Nazari, E. (2025) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-025-01814-2
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Andrés, C. M. C., de la Lastra, J. M. P., Munguira, E. B., Andrés, C., & Lebeña, E. P. (2026) · Stresses · doi:10.3390/stresses6030070
- Authors
- Zhu, H.-M., Liu, N., Sun, D.-X., & Luo, L.
- Affiliations
- Third Hospital of Nanchang (CN)
- Shenzhen University (CN)
- Ganzhou People's Hospital (CN)
- Corresponding author
- Luo, Liang
- ORCID
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2023-07-24
- Volume
- 14
- Pages
- 1202298-1202298
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC10406381
- OpenAlex
- W4385198380
- Cited by
- 12
- References
- 60
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4
- FWCI
- 2.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Science, Technology and Innovation Commission of Shenzhen Municipality
- Health Commission of Jiangxi Province
- Topics
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Medicine › Nephrology › Renal Diseases and Glomerulopathies
- Keywords
- diabetic nephropathy, oxidative stress, diagnostic biomarkers, immune cell infiltration, MKP-1, peroxiredoxin 6, S100A8, differentially expressed genes, serum gene expression, tumor immune microenvironment
- MeSH terms
- Machine Learning, Algorithms, Biological Assay, Diabetes Mellitus, Diabetic Nephropathies, Humans, Oxidative Stress, Calgranulin A
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