Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease.

IF 2.6 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-08-18 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0329640
Ziyang Liu, Zengyuan Qin, Wenxin Bai, Shasha Wang, Chunling Huang, Na Li, Lei Yan, Yue Gu, Fengmin Shao
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Abstract

Background: Diabetic kidney disease (DKD) is a severe global complication of diabetes, yet its molecular mechanisms remain incompletely understood. This study aimed to investigate the role of protein glycosylation in DKD pathogenesis and its association with gene expression changes, with the goal of identifying diagnostic biomarkers and personalized therapeutic targets.

Methods: Integrated bioinformatics and machine learning approaches were applied to analyze multiple gene expression datasets. Differentially expressed glycosylation-related genes were identified, followed by unsupervised clustering to define molecular subtypes. Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential.

Results: Unsupervised clustering of glycosylation-related genes revealed two distinct DKD molecular subtypes with differential pathway activation (e.g., extracellular matrix remodeling) and immune infiltration patterns. Six hub genes (S100A12, EXT1, SBSPON, ADAMTS1, FMOD, SPTB) were identified as critical to DKD pathogenesis through machine learning. Immune infiltration analysis showed significant differences in macrophage and neutrophil activity between DKD and controls and Immunohistochemical results confirmed the occurrence of immune infiltration. qPCR validation confirmed dysregulation of hub genes in DKD tissues compared to normal samples. ROC analysis demonstrated high diagnostic accuracy for these genes.

Conclusions: This study highlights abnormal protein glycosylation as a key player in DKD and identifies six hub genes with potential as diagnostic biomarkers. The molecular subtypes and immune infiltration patterns provide insights into disease heterogeneity, paving the way for personalized therapies. Future studies should validate these findings in larger cohorts with explicit sample sizes to strengthen clinical applicability.

结合生物信息学和机器学习来阐明蛋白糖基化相关基因在糖尿病肾病发病机制中的作用。
背景:糖尿病肾病(DKD)是一种严重的全球性糖尿病并发症,其分子机制尚不完全清楚。本研究旨在探讨蛋白糖基化在DKD发病机制中的作用及其与基因表达变化的关系,以确定诊断性生物标志物和个性化治疗靶点。方法:采用生物信息学和机器学习相结合的方法对多个基因表达数据集进行分析。鉴定了差异表达的糖基化相关基因,然后通过无监督聚类来确定分子亚型。采用功能富集、免疫细胞浸润分析和机器学习算法(包括枢纽基因的特征选择)。对临床DKD和正常肾脏组织进行qPCR验证,并生成ROC曲线以评估诊断潜力。结果:糖基化相关基因的无监督聚类揭示了两种不同的DKD分子亚型,它们具有不同的途径激活(例如,细胞外基质重塑)和免疫浸润模式。通过机器学习发现6个中心基因(S100A12、EXT1、SBSPON、ADAMTS1、FMOD、SPTB)在DKD发病机制中起关键作用。免疫浸润分析显示巨噬细胞和中性粒细胞活性与对照组有显著差异,免疫组化结果证实免疫浸润的发生。qPCR验证证实,与正常样本相比,DKD组织中的hub基因失调。ROC分析显示这些基因的诊断准确性很高。结论:本研究强调了异常蛋白糖基化是DKD的关键因素,并确定了6个具有诊断生物标志物潜力的枢纽基因。分子亚型和免疫浸润模式提供了对疾病异质性的见解,为个性化治疗铺平了道路。未来的研究应在更大的队列中验证这些发现,明确样本量,以加强临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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