Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Guangming Traditional Chinese Medicine Hospital Pudong New Area, Shanghai, China.
BMC Genomics. 2024 Jan 30;25(1):125. doi: 10.1186/s12864-024-10038-2.
Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis.
We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset.
In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929).
This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.
糖尿病足溃疡（DFU）是糖尿病最常见和最严重的并发症之一，其主要的病理机制包括血管变化、神经病变和感染。谷氨酰胺（Gln）代谢已被发现在糖尿病并发症中起着关键作用。本研究旨在通过生物信息学和机器学习分析，鉴定和验证与 DFU 相关的潜在 Gln 代谢生物标志物。
我们从基因表达综合数据库（GEO）下载了两个与 DFU 患者相关的微阵列数据集，即 GSE134431、GSE68183 和 GSE80178。从 GSE134431 数据集中，我们获得了 DFU 与正常对照组之间差异表达的谷氨酰胺代谢相关基因（deGlnMRGs）。我们分析了 deGlnMRGs 与免疫细胞浸润状态之间的相关性。我们还探讨了 GlnMRGs 分子聚类与免疫细胞浸润状态之间的关系。值得注意的是，WGCNA 用于识别特定聚类中差异表达的基因（DEGs）。此外，我们进行了 GSVA 以注释富集基因。随后，我们构建并筛选了最佳机器学习模型。最后，我们使用 nomogram、校准曲线、决策曲线分析（DCA）以及 GSE134431、GSE68183 和 GSE80178 数据集验证了预测的准确性。
在 DFU 和正常对照组中，我们均证实了 deGlnMRGs 的存在和激活的免疫反应。从 GSE134431 数据集中，我们获得了 20 个 deGlnMRGs，包括 CTPS1、NAGS、SLC7A11、GGT1、GCLM、RIMKLA、ARG2、ASL、ASNS、ASNSD1、PPAT、GLS2、GLUD1、MECP2、ASS1、PRODH、CTPS2、ALDH5A1、DGLUCY 和 SLC25A12。此外，我们在 DFU 中鉴定出了两个聚类。免疫浸润分析表明，这两个聚类中存在免疫异质性。此外，我们基于 5 个基因（R3HCC1、ZNF562、MFN1、DRAM1 和 PTGDS）建立了一个支持向量机（SVM）模型，该模型在外部验证数据集 GSE134431、GSE68183 和 GSE80178 上表现出优异的性能（AUC=0.929）。
本研究鉴定了与 DFU 相关的 5 个谷氨酰胺代谢基因，揭示了 DFU 潜在的新生物标志物和治疗靶点。此外，免疫炎症细胞的浸润在 DFU 的进展中起着关键作用。
