Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease
This study highlights abnormal protein glycosylation as a key player in DKD and identifies six hub genes with potential as diagnostic biomarkers. The molecular subtypes and immune infiltration patterns provide insights into disease heterogeneity, paving the way for personalized therapies. Future studies should validate these findings in larger cohorts with explicit sample sizes to strengthen clinical applicability.
Authors
- Fengmin Shao (ORCID: https://orcid.org/0000-0003-2905-4312)
- Chunling Huang (ORCID: https://orcid.org/0000-0002-5410-4935)
- Wenxin Bai
- Yue Gu (ORCID: https://orcid.org/0000-0002-3852-9319)
- Zhenkui Qin
- Na Li (ORCID: https://orcid.org/0000-0001-6455-0501)
- Shasha Wang (ORCID: https://orcid.org/0000-0002-8724-4627)
- Lei Yan (ORCID: https://orcid.org/0000-0002-2191-5096)
- Ziyang Liu (ORCID: https://orcid.org/0009-0009-2839-0972)
Institutions
- Henan Provincial People's Hospital (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1371/journal.pone.0329640
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 8.81