Bioinformatics to Identify Biomarkers of Diabetic Nephropathy based on Sphingolipid Metabolism and their Molecular Mechanisms.

IF 2.4 Q3 ENDOCRINOLOGY & METABOLISM Current diabetes reviews Pub Date : 2024-01-01 DOI:10.2174/0115733998297749240418071555
Yaxian Ning, Xiaochun Zhou, Gouqin Wang, Lili Zhang, Jianqin Wang
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Abstract

Background: Diabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life of diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship with DN is unclear. Therefore, screening biomarkers related to sphingolipid metabolism is crucial for treating DN.

Methods: To identify Differentially Expressed Genes (DEGs) in the GSE142153 dataset, we conducted a differential expression analysis (DN samples versus control samples). The intersection genes were obtained by overlapping DEGs and Sphingolipid Metabolism-Related Genes (SMRGs). Furthermore, The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to filter biomarkers. We further analyzed the Gene Set Enrichment analysis (GSEA) and the immunoinfiltrational analysis based on biomarkers.

Results: We identified 2,186 DEGs associated with DN. Then, five SMR-DEGs were obtained. Subsequently, biomarkers associated with sphingolipid metabolism (S1PR1 and SELL) were identified by applying machine learning and expression analysis. In addition, GSEA showed that these biomarkers were correlated with cytokine cytokine receptor interaction'. Significant variations in B cells, DCs, Tems, and Th2 cells between the two groups suggested that these cells might have a role in DN.

Conclusion: Overall, we obtained two sphingolipid metabolism-related biomarkers (S1PR1 and SELL) associated with DN, which laid a theoretical foundation for treating DN.

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基于鞘脂代谢及其分子机制的生物信息学识别糖尿病肾病的生物标记物
背景:糖尿病(DM)经常导致糖尿病肾病(DN),这对糖尿病患者的生活质量有很大的负面影响。鞘脂代谢与糖尿病有关,但与糖尿病肾病的关系尚不清楚。因此,筛选与鞘脂代谢相关的生物标志物对治疗 DN 至关重要:为了识别 GSE142153 数据集中的差异表达基因(DEGs),我们进行了差异表达分析(DN 样本与对照样本)。交叉基因由 DEGs 和鞘脂代谢相关基因(SMRGs)重叠而成。此外,我们还使用了最小绝对收缩和选择操作器(LASSO)和支持向量机递归特征消除(SVM-RFE)算法来筛选生物标记物。我们进一步分析了基于生物标志物的基因组富集分析(Gene Set Enrichment Analysis,GSEA)和免疫浸润分析:结果:我们发现了 2,186 个与 DN 相关的 DEGs。结果:我们发现了 2,186 个与 DN 相关的 DEGs。随后,通过应用机器学习和表达分析,确定了与鞘脂代谢相关的生物标志物(S1PR1 和 SELL)。此外,GSEA 显示这些生物标志物与细胞因子-细胞因子受体相互作用相关。两组间 B 细胞、DCs、Tems 和 Th2 细胞的显著差异表明,这些细胞可能在 DN 中发挥作用:总之,我们获得了两个与 DN 相关的鞘脂代谢相关生物标志物(S1PR1 和 SELL),这为治疗 DN 奠定了理论基础。
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来源期刊
Current diabetes reviews
Current diabetes reviews ENDOCRINOLOGY & METABOLISM-
CiteScore
6.30
自引率
0.00%
发文量
158
期刊介绍: Current Diabetes Reviews publishes frontier reviews on all the latest advances on diabetes and its related areas e.g. pharmacology, pathogenesis, complications, epidemiology, clinical care, and therapy. The journal"s aim is to publish the highest quality review articles dedicated to clinical research in the field. The journal is essential reading for all researchers and clinicians who are involved in the field of diabetes.
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