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基于与纤维化和丙酸代谢相关的基因开发用于糖尿病肾病的生物标志物及其功能验证。

To Develop Biomarkers for Diabetic Nephropathy Based on Genes Related to Fibrosis and Propionate Metabolism and Their Functional Validation.

机构信息

Department of Nephrology, The First Affiliated Hospital of Kunming Medical University 650032, Kunming, China.

Department of Nephrology, The Second People's Hospital of Yunnan Province 650021, Kunming, China.

出版信息

J Diabetes Res. 2024 Oct 16;2024:9066326. doi: 10.1155/2024/9066326. eCollection 2024.

DOI:10.1155/2024/9066326
PMID:39444490
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11498995/
Abstract

Propionate metabolism is important in the development of diabetes, and fibrosis plays an important role in diabetic nephropathy (DN). However, there are no studies on biomarkers related to fibrosis and propionate metabolism in DN. Hence, the current research is aimed at evaluating biomarkers associated with fibrosis and propionate metabolism and to explore their effect on DN progression. The GSE96804 (DN : control = 41 : 20) and GSE104948 (DN : control = 7 : 18) DN-related datasets and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were acquired from the public database. First, DN differentially expressed genes (DN-DEGs) between the DN and control samples were sifted out via differential expression analysis. The PMRG scores of the DN samples were calculated based on PMRGs. The samples were divided into the high and low PMRG score groups according to the median scores. The PM-DEGs between the two groups were screened out. Second, the intersection of DN-DEGs, PM-DEGs, and FRGs was taken to yield intersected genes. Random forest (RF) and recursive feature elimination (RFE) analyses of the intersected genes were performed to sift out biomarkers. Then, single gene set enrichment analysis was conducted. Finally, immunoinfiltrative analysis was performed, and the transcription factor (TF)-microRNA (miRNA)-mRNA regulatory network and the drug-gene interaction network were constructed. There were 2633 DN-DEGs between the DN and control samples and 515 PM-DEGs between the high and low PMRG score groups. In total, 10 intersected genes were gained after taking the intersection of DN-DEGs, PM-DEGs, and FRGs. Seven biomarkers, namely, SLC37A4, ACOX2, GPD1, angiotensin-converting enzyme 2 (ACE2), SLC9A3, AGT, and PLG, were acquired via RF and RFE analyses, and they were found to be involved in various mechanisms such as glomerulus development, fatty acid metabolism, and peroxisome. The seven biomarkers were positively correlated with neutrophils. Moreover, 8 TFs, 60 miRNAs, and 7 mRNAs formed the TF-miRNA-mRNA regulatory network, including USF1-hsa-mir-1296-5p-AGT and HIF1A-hsa-mir-449a-5p-ACE2. The drug-gene network contained UROKINASE-PLG, ATENOLOL-AGT, and other interaction relationship pairs. Via bioinformatic analyses, the risk of fibrosis and propionate metabolism-related biomarkers in DN were explored, thereby providing novel ideas for research related to DN diagnosis and treatment.

摘要

丙酸代谢在糖尿病的发展中很重要，纤维化在糖尿病肾病 (DN) 中起着重要作用。然而，目前还没有关于与纤维化和丙酸代谢相关的生物标志物的研究。因此，本研究旨在评估与纤维化和丙酸代谢相关的生物标志物，并探讨它们对 DN 进展的影响。从公共数据库中获取了 GSE96804（DN：对照=41：20）和 GSE104948（DN：对照=7：18）DN 相关数据集以及 924 个丙酸代谢相关基因（PMRGs）和 656 个纤维化相关基因（FRGs）。首先，通过差异表达分析筛选出 DN 和对照样本之间的 DN 差异表达基因（DN-DEGs）。基于 PMRGs 计算 DN 样本的 PMRG 评分。根据中位数将样本分为高和低 PMRG 评分组。筛选出两组之间的 PM-DEGs。其次，取 DN-DEGs、PM-DEGs 和 FRGs 的交集得到交集基因。对交集基因进行随机森林（RF）和递归特征消除（RFE）分析，筛选出生物标志物。然后进行单基因集富集分析。最后，进行免疫浸润分析，并构建转录因子（TF）-microRNA（miRNA）-mRNA 调控网络和药物-基因相互作用网络。DN 和对照样本之间有 2633 个 DN-DEGs，高和低 PMRG 评分组之间有 515 个 PM-DEGs。总共获得了 10 个交集基因，分别是 SLC37A4、ACOX2、GPD1、血管紧张素转换酶 2（ACE2）、SLC9A3、AGT 和 PLG。通过 RF 和 RFE 分析获得了 7 个生物标志物，即 SLC37A4、ACOX2、GPD1、血管紧张素转换酶 2（ACE2）、SLC9A3、AGT 和 PLG，它们被发现参与了肾小球发育、脂肪酸代谢和过氧化物酶体等多种机制。这 7 个生物标志物与中性粒细胞呈正相关。此外，形成了包括 USF1-hsa-mir-1296-5p-AGT 和 HIF1A-hsa-mir-449a-5p-ACE2 在内的 8 个 TF、60 个 miRNA 和 7 个 mRNA 的 TF-miRNA-mRNA 调控网络。药物-基因网络包含 UROKINASE-PLG、ATENOLOL-AGT 等相互作用关系对。通过生物信息学分析，探讨了与 DN 相关的纤维化和丙酸代谢相关生物标志物的风险，为 DN 的诊断和治疗相关研究提供了新的思路。

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