Integrative proteomic analysis reveals the potential diagnostic marker and drug target for the Type-2 diabetes mellitus.

IF 1.6 Q4 ENDOCRINOLOGY & METABOLISM
Journal of Diabetes and Metabolic Disorders Pub Date : 2025-01-22 eCollection Date: 2025-06-01 DOI:10.1007/s40200-025-01562-3
Zhen Jia, Ning Jiang, Lin Lin, Bing Li, Xuewei Liang
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Abstract

Objective: The escalating prevalence of Type-2 diabetes mellitus (T2DM) poses a significant global health challenge. Utilizing integrative proteomic analysis, this study aimed to identify a panel of potential protein markers for T2DM, enhancing diagnostic accuracy and paving the way for personalized treatment strategies.

Methods: Proteome profiles from two independent cohorts were integrated: cohort 1 composed of 10 T2DM patients and 10 healthy controls (HC), and cohort 2 comprising 87 T2DM patients and 60 healthy controls. Differential expression analysis, functional enrichment analysis, receiver operating characteristic (ROC) analysis, and classification error matrix analysis were employed.

Results: Comparative proteomic analysis identified the differential expressed proteins (DEPs) and changes in biological pathways associated with T2DM. Further combined analysis refined a group of protein panel (including CA1, S100A6, and DDT), which were significantly increased in T2DM in both two cohorts. ROC analysis revealed the area under curve (AUC) values of 0.94 for CA1, 0.87 for S100A6, and 0.97 for DDT; the combined model achieved an AUC reaching 1. Classification error matrix analysis demonstrated the combined model could reach an accuracy of 1 and 0.875 in the 60% training set and 40% testing set.

Conclusions: This study incorporates different cohorts of T2DM, and refines the potential markers for T2DM with high accuracy, offering more reliable markers for clinical translation.

Supplementary information: The online version contains supplementary material available at 10.1007/s40200-025-01562-3.

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综合蛋白质组学分析揭示了2型糖尿病潜在的诊断标志物和药物靶点。
目的:2型糖尿病(T2DM)的患病率不断上升,对全球健康构成了重大挑战。利用综合蛋白质组学分析,本研究旨在确定T2DM的一组潜在蛋白质标志物,提高诊断准确性并为个性化治疗策略铺平道路。方法:整合两个独立队列的蛋白质组图谱:队列1由10名T2DM患者和10名健康对照(HC)组成,队列2由87名T2DM患者和60名健康对照组成。采用差异表达分析、功能富集分析、受试者工作特征(ROC)分析和分类误差矩阵分析。结果:比较蛋白质组学分析确定了与T2DM相关的差异表达蛋白(DEPs)和生物学途径的变化。进一步的联合分析细化了一组蛋白面板(包括CA1、S100A6和DDT),在两个队列中,T2DM患者的蛋白面板均显著升高。ROC分析显示,CA1的曲线下面积(AUC)为0.94,S100A6为0.87,DDT为0.97;组合模型的AUC达到1。分类误差矩阵分析表明,在60%的训练集和40%的测试集上,组合模型的准确率分别为1和0.875。结论:本研究纳入了T2DM的不同队列,精细化了T2DM的潜在标记物,准确度较高,为临床转化提供了更可靠的标记物。补充资料:在线版本提供补充资料,网址为10.1007/s40200-025-01562-3。
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
Journal of Diabetes and Metabolic Disorders
Journal of Diabetes and Metabolic Disorders Medicine-Internal Medicine
CiteScore
4.80
自引率
3.60%
发文量
210
期刊介绍: Journal of Diabetes & Metabolic Disorders is a peer reviewed journal which publishes original clinical and translational articles and reviews in the field of endocrinology and provides a forum of debate of the highest quality on these issues. Topics of interest include, but are not limited to, diabetes, lipid disorders, metabolic disorders, osteoporosis, interdisciplinary practices in endocrinology, cardiovascular and metabolic risk, aging research, obesity, traditional medicine, pychosomatic research, behavioral medicine, ethics and evidence-based practices.As of Jan 2018 the journal is published by Springer as a hybrid journal with no article processing charges. All articles published before 2018 are available free of charge on springerlink.Unofficial 2017 2-year Impact Factor: 1.816.
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