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已发表论文
慢性肾脏病中 m6A RNA 甲基化相关特征用于风险预测、诊断生物标志物和免疫亚型的机器学习与实验验证
Authors Chen J, Wang Z, Liao Y, Ding W
Received 31 July 2025
Accepted for publication 22 January 2026
Published 2 February 2026 Volume 2026:19 553934
DOI https://doi.org/10.2147/JIR.S553934
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Wenjian Li
Jiaheng Chen,* Zhiwei Wang,* Yanting Liao, Wencong Ding
The Department of Nephrology and Hemopurification Center, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, Guangdong, 528000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Wencong Ding, The Department of Nephrology and Hemopurification Center, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, Guangdong, 528000, People’s Republic of China, Email freeonh@163.com
Background: N6-methyladenosine (m6A) RNA methylation, a pivotal epigenetic modification, has been implicated in the pathogenesis and progression of diverse diseases. This study sought to elucidate the functional contributions of m6A-related genes to the pathogenesis of chronic kidney disease (CKD) using a strategy that integrated machine learning and experimental validation, with the goal of identifying robust diagnostic biomarkers and novel molecular subtypes.
Methods: Leveraging publicly available datasets, transcriptomic results of 53 patients with chronic kidney disease (CKD) as well as 8 healthy control individuals were collected. Differential expression analysis of m6A-related genes was performed, followed by the construction and comparison of random forest (RF) and support vector machine (SVM) models to predict CKD risk and identify diagnostic biomarkers. The key biomarkers were validated in the CKD model mice established by unilateral ureteral obstruction (UUO) using RT-qPCR and immunofluorescence analysis. Immune cell infiltration was assessed via ssGSEA analysis, and molecular subtypes were delineated through consensus clustering.
Results: We identified 20 differentially expressed m6A-related genes in CKD. The RF model demonstrated superior performance in risk prediction and prioritized five key genes (CBLL1, ELAVL1, RBM15B, YTHDF1, METTL3) for constructing a diagnostic nomogram. Experimental validation confirmed the upregulation of CBLL1, ELAVL1, RBM15B, and YTHDF1, and the downregulation of METTL3 in CKD mice. Furthermore, we identified two distinct m6A-associated molecular subtypes (Clusters A and B) with divergent immune landscapes. Cluster B was characterized by a pro-inflammatory phenotype, featuring elevated Th17 cell infiltration and a reduced proportion of Th2 cells.
Conclusion: Beyond advancing the mechanistic understanding of m6A in CKD, this study provides a translatable risk prediction model and delineates distinct immune subtypes, offering valuable foundations for future clinical stratification, diagnostic refinement, and the development of personalized immunomodulatory therapies.
Keywords: m6A methylation, chronic kidney disease, random forest, nomogram, subgroup
The Department of Nephrology and Hemopurification Center, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, Guangdong, 528000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Wencong Ding, The Department of Nephrology and Hemopurification Center, Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Foshan, Guangdong, 528000, People’s Republic of China, Email freeonh@163.com
Background: N6-methyladenosine (m6A) RNA methylation, a pivotal epigenetic modification, has been implicated in the pathogenesis and progression of diverse diseases. This study sought to elucidate the functional contributions of m6A-related genes to the pathogenesis of chronic kidney disease (CKD) using a strategy that integrated machine learning and experimental validation, with the goal of identifying robust diagnostic biomarkers and novel molecular subtypes.
Methods: Leveraging publicly available datasets, transcriptomic results of 53 patients with chronic kidney disease (CKD) as well as 8 healthy control individuals were collected. Differential expression analysis of m6A-related genes was performed, followed by the construction and comparison of random forest (RF) and support vector machine (SVM) models to predict CKD risk and identify diagnostic biomarkers. The key biomarkers were validated in the CKD model mice established by unilateral ureteral obstruction (UUO) using RT-qPCR and immunofluorescence analysis. Immune cell infiltration was assessed via ssGSEA analysis, and molecular subtypes were delineated through consensus clustering.
Results: We identified 20 differentially expressed m6A-related genes in CKD. The RF model demonstrated superior performance in risk prediction and prioritized five key genes (CBLL1, ELAVL1, RBM15B, YTHDF1, METTL3) for constructing a diagnostic nomogram. Experimental validation confirmed the upregulation of CBLL1, ELAVL1, RBM15B, and YTHDF1, and the downregulation of METTL3 in CKD mice. Furthermore, we identified two distinct m6A-associated molecular subtypes (Clusters A and B) with divergent immune landscapes. Cluster B was characterized by a pro-inflammatory phenotype, featuring elevated Th17 cell infiltration and a reduced proportion of Th2 cells.
Conclusion: Beyond advancing the mechanistic understanding of m6A in CKD, this study provides a translatable risk prediction model and delineates distinct immune subtypes, offering valuable foundations for future clinical stratification, diagnostic refinement, and the development of personalized immunomodulatory therapies.
Keywords: m6A methylation, chronic kidney disease, random forest, nomogram, subgroup