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已发表论文
全面分析揭示了与糖尿病周围神经病变相关的生物标志物及其分子机制
Authors Zhu Q, Fan S, Mou J, Zhao M, Li C, Yang B, Shangguan Y, Chen X, Cai Y
Received 3 June 2025
Accepted for publication 22 September 2025
Published 30 October 2025 Volume 2025:18 Pages 6551—6570
DOI https://doi.org/10.2147/IJGM.S544464
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Jacopo Manso
Qiujin Zhu,1,2,* Shangheng Fan,1,2,* Jingru Mou,1,2 Manlu Zhao,1,2 Changlu Li,1,2 Benben Yang,1,2 Yihan Shangguan,1,2 Xia Chen,3 Yulan Cai1– 3
1Department of Endocrinology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, People’s Republic of China; 2Department of Endocrinology and Metabolism, The Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, People’s Republic of China; 3Department of Endocrinology, Kweichow Moutai Hospital, Renhuai, Guizhou, 564501, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xia Chen; Yulan Cai, Email 736469203@qq.com; caiyulanuiui@163.com
Purpose: Diabetic peripheral neuropathy (DPN) is a common complication of both type 1 and 2 diabetes. DPN lacks accurate early diagnostic indicators, prompting united identification of biomarkers through transcriptomics and Mendelian randomization (MR) to inform DPN prevention and treatment strategies.
Patients and Methods: Differential expression analysis pinpointed DPN-related genes (DE-DPN-RGs) by screening differentially expressed genes (DEGs) across GSE95849 and GSE185011 datasets. MR approach validated DE-DPN-RGs causally linked to DPN as potential biomarkers, with sensitivity analysis and Steiger test reinforcing the findings. These biomarkers’ expressions were verified via RT-qPCR, while their biological roles, pathways influencing DPN progression, and possible therapeutic targets were comprehensively investigated.
Results: 124 DE-DPN-RGs were identified from 5340 DEGs1 and 896 DEGs2, among them, TNF, OSBPL8, IER3, SLC16A3, CREB5, and LRWD1, showing significant causal relationships with DPN. Sensitivity analysis along with the Steiger test validated the reliability of the results, demonstrating their resilience against reverse causation. Furthermore, OSBPL8, SLC16A3, CREB5, and LRWD1 demonstrated significant differential expression between DPN and control groups in both the GSE95849 and GSE185011 datasets, with consistent expression trends across both datasets, thereby warranting their designation as biomarkers. Biomarkers functioned in metabolic reactions of amino acids, rRNA processing, and translation, with potential therapeutic candidates including rosuvastatin, nitrofurfurylhydrazide, and neostigmine bromide. All four biomarkers exhibited significant upregulation in the DPN group as confirmed by RT-qPCR analysis, with the exception of OSBPL8, which displayed a non-significant difference between the groups.
Conclusion: In conclusion, SLC16A3, CREB5, and LRWD1 emerge as promising biomarkers, elucidating roles in DPN pathogenesis and offering potential therapeutic targets.
Keywords: diabetic peripheral neuropathy, mendelian randomization, biomarker, gene set enrichment analysis, drug prediction
1Department of Endocrinology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, People’s Republic of China; 2Department of Endocrinology and Metabolism, The Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, People’s Republic of China; 3Department of Endocrinology, Kweichow Moutai Hospital, Renhuai, Guizhou, 564501, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xia Chen; Yulan Cai, Email 736469203@qq.com; caiyulanuiui@163.com
Purpose: Diabetic peripheral neuropathy (DPN) is a common complication of both type 1 and 2 diabetes. DPN lacks accurate early diagnostic indicators, prompting united identification of biomarkers through transcriptomics and Mendelian randomization (MR) to inform DPN prevention and treatment strategies.
Patients and Methods: Differential expression analysis pinpointed DPN-related genes (DE-DPN-RGs) by screening differentially expressed genes (DEGs) across GSE95849 and GSE185011 datasets. MR approach validated DE-DPN-RGs causally linked to DPN as potential biomarkers, with sensitivity analysis and Steiger test reinforcing the findings. These biomarkers’ expressions were verified via RT-qPCR, while their biological roles, pathways influencing DPN progression, and possible therapeutic targets were comprehensively investigated.
Results: 124 DE-DPN-RGs were identified from 5340 DEGs1 and 896 DEGs2, among them, TNF, OSBPL8, IER3, SLC16A3, CREB5, and LRWD1, showing significant causal relationships with DPN. Sensitivity analysis along with the Steiger test validated the reliability of the results, demonstrating their resilience against reverse causation. Furthermore, OSBPL8, SLC16A3, CREB5, and LRWD1 demonstrated significant differential expression between DPN and control groups in both the GSE95849 and GSE185011 datasets, with consistent expression trends across both datasets, thereby warranting their designation as biomarkers. Biomarkers functioned in metabolic reactions of amino acids, rRNA processing, and translation, with potential therapeutic candidates including rosuvastatin, nitrofurfurylhydrazide, and neostigmine bromide. All four biomarkers exhibited significant upregulation in the DPN group as confirmed by RT-qPCR analysis, with the exception of OSBPL8, which displayed a non-significant difference between the groups.
Conclusion: In conclusion, SLC16A3, CREB5, and LRWD1 emerge as promising biomarkers, elucidating roles in DPN pathogenesis and offering potential therapeutic targets.
Keywords: diabetic peripheral neuropathy, mendelian randomization, biomarker, gene set enrichment analysis, drug prediction