Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-07-02 DOI:10.1038/s41598-025-96192-3

Weibin Wu, Zheng Peng, Mingyi Chen, Yi Yu, Caisheng Wu, Qiang Xie

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Abstract

Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With the rising prevalence and increasing associated chronic complications, identifying and understanding the molecular mechanisms of diabetes has become an important direction in bioinformatics research. The aim of this study is the identification of diagnostic genes associated with streptozotocin (STZ)-induced diabetic mice. GSE179717 and GSE179718 gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by screening diabetic mice and controls. A total of 45 overlapping genes were recognized as potential DEGs across the two datasets. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the overlapping genes. Protein-protein interaction (PPI) networks were constructed to identify hub genes in the DEGs. After the application of Least absolute shrinkage and selection operator (LASSO), Random Forest analysis, three genes (Scd1, Sirt1 and Hmgb1) were identified as diagnostic genes. Real-time quantitative polymerase chain reaction (RT-qPCR) was supplied to detect the expressions of diagnostic genes in aged diabetic mice. In conclusion, the results of this study suggest that focusing on these genes may provide new targets for the diagnosis and treatment of diabetes.

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整合机器学习和生物信息学方法识别糖尿病小鼠新的诊断基因生物标志物。

糖尿病是一种复杂的代谢紊乱，其发病机制涉及遗传、环境因素和生活方式选择的相互作用。随着糖尿病患病率的上升和慢性并发症的增多，识别和了解糖尿病的分子机制已成为生物信息学研究的一个重要方向。本研究的目的是鉴定与链脲佐菌素（STZ）诱导的糖尿病小鼠相关的诊断基因。从gene expression Omnibus （GEO）数据库下载GSE179717和GSE179718基因表达数据集。通过筛选糖尿病小鼠和对照组，鉴定差异表达基因（DEGs）。在两个数据集中，共有45个重叠基因被识别为潜在的基因变异位点。接下来，对重叠基因进行基因本体（GO）和京都基因与基因组百科全书（KEGG）分析。构建蛋白-蛋白相互作用（PPI）网络来鉴定DEGs中的枢纽基因。应用最小绝对收缩和选择算子（LASSO）、随机森林分析，鉴定出3个基因（Scd1、Sirt1和Hmgb1）作为诊断基因。采用实时定量聚合酶链反应（RT-qPCR）检测老年糖尿病小鼠诊断基因的表达。总之，本研究结果表明，关注这些基因可能为糖尿病的诊断和治疗提供新的靶点。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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