已发表论文

糖尿病肾病精氨酸甲基化的生物标志物:生物信息学分析的新见解

 

Authors Guan Y, Yin X, Wang L, Diao Z, Huang H, Wang X 

Received 6 April 2024

Accepted for publication 11 September 2024

Published 13 September 2024 Volume 2024:17 Pages 3399—3418

DOI https://doi.org/10.2147/DMSO.S472412

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Konstantinos Tziomalos

Yiming Guan, Xiayan Yin, Liyan Wang, Zongli Diao, Hongdong Huang,* Xueqi Wang* 

Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Xueqi Wang, Hongdong Huang, Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, People’s Republic of China, Email Xueqiwang32@hotmail.com; Huanghd1126@126.com

Background: Diabetic nephropathy (DN) is a severe complication of diabetes influenced by arginine methylation. This study aimed to elucidate the role of protein arginine methylation-related genes (PRMT-RGs) in DN and identify potential biomarkers.
Methods: Differentially expressed genes in two GEO datasets (GSE30122 and GSE104954) were integrated with 9 PRMT-RGs. Candidate genes were identified using WGCNA and differential expression analysis, then screened using support vector machine-recursive feature elimination and least absolute shrinkage and selection operator. Biomarkers were defined as genes with consistent differential expression across both datasets. Regulatory networks were constructed using the miRNet and Network Analyst databases. Gene set enrichment analysis was performed to identify the signaling pathways in which the biomarkers were enriched in DN. Different immune cells in DN were identified using immune infiltration analysis. Meanwhile, drug prediction and molecular docking identified potential DN therapies. Finally, qRT-PCR and immunohistochemistry validated two biomarkers in STZ-induced DN mice and DN patients.
Results: Two biomarkers (FAM98A and FAM13B) of DN were identified in this study. The molecular regulatory network revealed that FAM98A and FAM13B were co-regulated by 6 microRNAs and 1 transcription factor and were enriched in signaling pathways. Immune infiltration and correlation analyses revealed that FAM98A and FAM13B were involved in developing DN along with PRMT-RGs and immune cells. The expression levels of Fam98a and Fam13b were significantly upregulated in the kidneys of DN mice revealed by qRT-PCR analysis. The expression levels of FAM98A were significantly upregulated in the kidneys of DN patients revealed by immunohistochemistry staining. Molecular docking showed that estradiol and rotenone exerted potential therapeutic effects on DN by targeting FAM98A.
Conclusion: Comprehensive bioinformatics analysis revealed that FAM98A and FAM13B were potential DN biomarkers correlated with PRMT-RGs and immune cells. This study provided useful insights for elucidating the molecular mechanisms and developing targeted therapy for DN.

Keywords: diabetic nephropathy, protein arginine methylation-related genes, biomarkers, bioinformatics analysis