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已发表论文
整合的总体和单细胞转录组分析揭示了 IgA 肾病中与线粒体代谢相关的生物标志物并进行了实验验证
Authors Wajid I , Nie X , Liu H, Li D , Ren Y, Wang Q
Received 17 September 2025
Accepted for publication 10 November 2025
Published 19 November 2025 Volume 2025:18 Pages 16209—16230
DOI https://doi.org/10.2147/JIR.S565202
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Wenjian Li
Ibtasam Wajid,1,2,* Xuedan Nie,3,* Hao Liu,2 Dan Li,1 Yeping Ren,2 Qin Wang2
1School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, People’s Republic of China; 2Department of Nephrology, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518000, People’s Republic of China; 3Department of Neurology, South China Hospital, Medical School, Shenzhen University, Shenzhen, Guangdong, 518116, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qin Wang, Department of Nephrology, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518000, People’s Republic of China, Email wangqinmail@vip.163.com
Background: Abnormalities in mitochondrial metabolism have been linked to renal disease; however, the precise mechanism underlying its involvement in immunoglobulin A nephropathy (IgAN) remains incompletely elucidated. The objective of this study was to ascertain the impact of biomarkers associated with mitochondrial metabolism on IgAN.
Methods: The data related to IgAN were obtained from public databases. The mitochondrial metabolism-related biomarkers in IgAN were identified through various bioinformatics approaches, including machine learning algorithms and expression validation. Subsequently, enrichment analysis and immune microenvironment analysis were conducted to explore biomarkers’ potential mechanisms in IgAN. Additionally, single-cell RNA sequencing (scRNA-seq) analysis was performed to identify key cell types and clarify the expression dynamics of biomarkers. The expression levels of the biomarkers were validated via reverse transcription quantitative PCR (RT-qPCR).
Results: CYP27B1 and PCK1 were identified as biomarkers for IgAN. Notably, CYP27B1 and PCK1 were commonly enriched in the pathways of “fatty acid metabolism” and “oxidative phosphorylation”. Moreover, CYP27B1 displayed the highest positive correlation with neutrophils, whereas PCK1 exhibited the highest negative correlation with activated NK cells. Besides, scRNA-seq analysis identified proximal tubular cells (PTCs) as the key cell type. The expression of CYP27B1 remained unchanged throughout PTCs’ differentiation, while the expression of PCK1 gradually increased initially and then decreased in the middle to later stages. Moreover, RT-qPCR analysis revealed a significant reduction in the expression levels of CYP27B1 and PCK1 in the IgAN group (P < 0.001), aligning with the predictions obtained from the database.
Conclusion: This study integrated bulk and scRNA-seq analyses to pinpoint CYP27B1 and PCK1 as biomarkers, with PTCs identified as key cells, providing novel diagnostic approaches for IgAN.
Keywords: immunoglobulin A nephropathy, mitochondrial metabolism, biomarkers, single-cell sequencing analysis
1School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, People’s Republic of China; 2Department of Nephrology, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518000, People’s Republic of China; 3Department of Neurology, South China Hospital, Medical School, Shenzhen University, Shenzhen, Guangdong, 518116, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qin Wang, Department of Nephrology, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518000, People’s Republic of China, Email wangqinmail@vip.163.com
Background: Abnormalities in mitochondrial metabolism have been linked to renal disease; however, the precise mechanism underlying its involvement in immunoglobulin A nephropathy (IgAN) remains incompletely elucidated. The objective of this study was to ascertain the impact of biomarkers associated with mitochondrial metabolism on IgAN.
Methods: The data related to IgAN were obtained from public databases. The mitochondrial metabolism-related biomarkers in IgAN were identified through various bioinformatics approaches, including machine learning algorithms and expression validation. Subsequently, enrichment analysis and immune microenvironment analysis were conducted to explore biomarkers’ potential mechanisms in IgAN. Additionally, single-cell RNA sequencing (scRNA-seq) analysis was performed to identify key cell types and clarify the expression dynamics of biomarkers. The expression levels of the biomarkers were validated via reverse transcription quantitative PCR (RT-qPCR).
Results: CYP27B1 and PCK1 were identified as biomarkers for IgAN. Notably, CYP27B1 and PCK1 were commonly enriched in the pathways of “fatty acid metabolism” and “oxidative phosphorylation”. Moreover, CYP27B1 displayed the highest positive correlation with neutrophils, whereas PCK1 exhibited the highest negative correlation with activated NK cells. Besides, scRNA-seq analysis identified proximal tubular cells (PTCs) as the key cell type. The expression of CYP27B1 remained unchanged throughout PTCs’ differentiation, while the expression of PCK1 gradually increased initially and then decreased in the middle to later stages. Moreover, RT-qPCR analysis revealed a significant reduction in the expression levels of CYP27B1 and PCK1 in the IgAN group (P < 0.001), aligning with the predictions obtained from the database.
Conclusion: This study integrated bulk and scRNA-seq analyses to pinpoint CYP27B1 and PCK1 as biomarkers, with PTCs identified as key cells, providing novel diagnostic approaches for IgAN.
Keywords: immunoglobulin A nephropathy, mitochondrial metabolism, biomarkers, single-cell sequencing analysis