Abstract
Diabetic kidney disease (DKD) is a leading cause of renal failure. Inflammation of the renal tubules and interstitium is a critical factor in the progression of DKD; however, the key regulatory genes and characteristics of the immune microenvironment remain poorly understood. This study aims to identify key inflammatory biomarkers in the renal tubule tissues of DKD patients and to elucidate their potential immunoregulatory mechanisms. By integrating multiple GEO transcriptome datasets and employing differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (LASSO, Random Forest), we identified arachidonate 5-lipoxygenase (ALOX5) as a crucial feature gene of renal tubular inflammation in DKD. Clinical correlation analysis revealed that ALOX5 is significantly upregulated in DKD tissues, with high expression closely associated with decreased glomerular filtration rate and infiltration of M1 macrophages. Additionally, combining single-cell sequencing pseudotime analysis and multiplex immunohistochemistry (mIHC), we demonstrated that ALOX5 and its partner protein ALOX5AP are primarily expressed in CD68\(^+\) macrophages infiltrating the renal interstitium. They exhibit a high degree of co-localization with NF-\(\kappa\)B/p65, iNOS, and CYSLTR1, suggesting that they may mediate the pro-inflammatory polarization of macrophages through the leukotriene-NF-\(\kappa\)B axis. Finally, based on molecular docking and ADMET analysis, we screened the natural small molecule honokiol as a potential inhibitor of ALOX5, which possesses favorable pharmacokinetic properties. This study suggests that ALOX5 is a potential biomarker of immune microenvironment imbalance in DKD and provides a rationale for further investigation of targeted anti-inflammatory strategies, with honokiol as a candidate compound.
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Data availability
The datasets generated and analyzed during the current study are available in the GEO repository (Accession numbers: GSE104954, GSE30529, GSE47184, GSE99325, GSE209781). Requests for additional data or materials should be directed to J.Y. (email: yuanjinger@126.com)
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Acknowledgements
We are grateful to Guizhou University and Guizhou Provincial People’s Hospital for providing the valuable research platform and support during the course of this study.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 82360148); Guizhou Science & Technology Department (Grant No. QKHCG2023-ZD010); Talent Fund of Guizhou Provincial People’s Hospital (Grant No. [2022]-1); Guizhou Provincial People’s Hospital Research Fund, Youth Fund (Grant No. GZSYQN[2021] 18); Guizhou Provincial Medical Research Joint Fund for High-quality Development of Health and Health Care in 2024 (Grant No. 2024GZYXKYJJXM0007); and the General Program of Guizhou Provincial Department of Science and Technology (Grant No. Qiankehe Basic MS [2025] 494).
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W.L. and Y.D. conceived and designed the study. Y.Z. and J.Y. supervised the project and acquired funding. W.L., Y.D., and Q.X. developed the methodology. W.L., L.Z., Y.Z., and J.Y. designed the bioinformatics analyses. W.L., L.Z., Y.Z., and X.X. performed data processing, statistical analyses, and visualization. Y.D., L.L., and F.Y. curated the data and assisted with validation. Y.Z., J.Y., D.A., and L.L. coordinated patient recruitment and sample collection. W.L., Y.D., D.Y., K.Y., and D.A. performed the multiplex immunohistochemistry experiments. X.L. and L.J.L. provided technical support for imaging and interpretation. W.L. drafted the manuscript. Y.D., Y.Z., and J.Y. revised the manuscript. All authors reviewed and approved the final manuscript.
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All kidney biopsy samples were collected after obtaining informed consent from the patients. This study was approved by the Ethics Committee of Guizhou Provincial People’s Hospital (Approval No: 2022-65) and was conducted in strict accordance with the Declaration of Helsinki and relevant ethical guidelines for human research.
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Lu, W., Deng, Y., Zhai, L. et al. Integrated machine learning and multi-omics analysis identifies ALOX5 as a potential therapeutic target for tubulointerstitial inflammation in diabetic kidney disease. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44445-0
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DOI: https://doi.org/10.1038/s41598-026-44445-0
