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已发表论文
糖尿病肾病中免疫相关生物标志物及免疫微环境的综合分析与实验验证
Authors Zhou W, Zeng Z, Li X, Yang M
Received 3 June 2025
Accepted for publication 25 September 2025
Published 3 October 2025 Volume 2025:18 Pages 13711—13726
DOI https://doi.org/10.2147/JIR.S541886
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Quan Zhang
Weini Zhou,1,* Ziyang Zeng,2,* Xunjia Li,3– 5 Mei Yang1
1Department of Endocrinology, The People’s Hospital of Chongqing Liang Jiang New Area, The Affiliated Liang Jiang Hospital of Chongqing Medical University, Chongqing, 401121, People’s Republic of China; 2Department of Anesthesiology, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China; 3Department of Nephrology, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China; 4The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, People’s Republic of China; 5Department of Research and Development, Chongqing Precision Medical Industry Technology Research Institute, Chongqing, 400000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Mei Yang, Department of Endocrinology, The People’s Hospital of Chongqing Liang Jiang New Area, The Affiliated Liang Jiang Hospital of Chongqing Medical University, Chongqing, 401121, People’s Republic of China, Email YM657599236@163.com Xunjia Li, Department of Nephrology, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China, Email lixunjia@cqctcm.edu.cn
Background: The molecular mechanism of diabetic nephropathy (DN) is still not fully understood. There is ample evidence that the immune system plays a crucial role in the progression of DN. Further exploration of immune-related genes (IRGs) for DN diagnosis is therefore of significant clinical value.
Methods: Gene expression data from DN patients were obtained from the GEO database, and a weighted gene co-expression network analysis (WGCNA) was constructed. The overlapping IRGs derived by the least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RF) algorithms were identified as DN diagnostic biomarkers. A nomogram model was established to evaluate the diagnostic ability of feature biomarkers. The expression levels of the screened IRGs were validated in vitro using qRT-PCR. Type 2 diabetes mellitus (T2DM) mouse model with DN was also established to confirm the consistency with bioinformatic predictions.
Results: Three IRG-related DN characteristic diagnostic biomarkers (CCL9, EDN1 and HSPA1L) were identified. After verifying the DN diagnostic capability with nomogram model, pathway enrichment analysis, immunoinfiltration characteristics and correlation analysis were used to comprehensively analyze the potential effects of selected IRGs on DN. The differential expressions of screened IRGs were further confirmed by cell line and T2DM mouse model.
Conclusion: Our findings nominate CCL9, EDN1, and HSPA1L as key mediators of DN progression and unveil their potential as diagnostic biomarkers. Although prospective validation in human cohorts is a prerequisite for clinical translation, these IRGs represent a compelling foundation for a precision medicine tool. This tool could transform patient management by facilitating pre-symptomatic diagnosis and informing tailored interventions to halt DN development.
Keywords: diabetic nephropathy, immune related genes, diagnostic biomarker, immune infiltration, machine learning
1Department of Endocrinology, The People’s Hospital of Chongqing Liang Jiang New Area, The Affiliated Liang Jiang Hospital of Chongqing Medical University, Chongqing, 401121, People’s Republic of China; 2Department of Anesthesiology, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China; 3Department of Nephrology, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China; 4The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, People’s Republic of China; 5Department of Research and Development, Chongqing Precision Medical Industry Technology Research Institute, Chongqing, 400000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Mei Yang, Department of Endocrinology, The People’s Hospital of Chongqing Liang Jiang New Area, The Affiliated Liang Jiang Hospital of Chongqing Medical University, Chongqing, 401121, People’s Republic of China, Email YM657599236@163.com Xunjia Li, Department of Nephrology, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China, Email lixunjia@cqctcm.edu.cn
Background: The molecular mechanism of diabetic nephropathy (DN) is still not fully understood. There is ample evidence that the immune system plays a crucial role in the progression of DN. Further exploration of immune-related genes (IRGs) for DN diagnosis is therefore of significant clinical value.
Methods: Gene expression data from DN patients were obtained from the GEO database, and a weighted gene co-expression network analysis (WGCNA) was constructed. The overlapping IRGs derived by the least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RF) algorithms were identified as DN diagnostic biomarkers. A nomogram model was established to evaluate the diagnostic ability of feature biomarkers. The expression levels of the screened IRGs were validated in vitro using qRT-PCR. Type 2 diabetes mellitus (T2DM) mouse model with DN was also established to confirm the consistency with bioinformatic predictions.
Results: Three IRG-related DN characteristic diagnostic biomarkers (CCL9, EDN1 and HSPA1L) were identified. After verifying the DN diagnostic capability with nomogram model, pathway enrichment analysis, immunoinfiltration characteristics and correlation analysis were used to comprehensively analyze the potential effects of selected IRGs on DN. The differential expressions of screened IRGs were further confirmed by cell line and T2DM mouse model.
Conclusion: Our findings nominate CCL9, EDN1, and HSPA1L as key mediators of DN progression and unveil their potential as diagnostic biomarkers. Although prospective validation in human cohorts is a prerequisite for clinical translation, these IRGs represent a compelling foundation for a precision medicine tool. This tool could transform patient management by facilitating pre-symptomatic diagnosis and informing tailored interventions to halt DN development.
Keywords: diabetic nephropathy, immune related genes, diagnostic biomarker, immune infiltration, machine learning