Shi Hongshuo, Yuan Xin, Liu Guobin, Fan Weijing
Department of Peripheral Vascular Surgery, Institute of Surgery of Traditional Chinese Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China.
J Inflamm Res. 2023 Dec 20;16:6241-6256. doi: 10.2147/JIR.S442388. eCollection 2023.
A diabetic foot ulcer (DFU) is a serious, long-term condition associated with a significant risk of disability and mortality. However, research on its biomarkers is still limited. This study utilizes bioinformatics and machine learning methods to identify immune-related biomarkers for DFU and validates them through external datasets and animal experiments.
This study used bioinformatics and machine learning to analyze microarray data from the Gene Expression Omnibus (GEO) database to identify key genes associated with DFU. Animal experiments were conducted to validate these findings. This research employs the datasets GSE68183 and GSE80178 retrieved from the GEO database as the training dataset for building a gene machine learning model, and after conducting differential analysis on the data, this study used package glmnet and package e1071 to construct LASSO and SVM-RFE machine learning models, respectively. Subsequently, we validated the model using the training set and validation set (GSE134431). We conducted enrichment analysis, including GSEA and GSVA, on the model genes. We also performed immune functional analysis and immune-related analysis on the model genes. Finally, we conducted immunohistochemistry (IHC) validation on the model genes.
This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods. Subsequent validation through external datasets and IHC experiments also confirms GSTM5 as a critical biomarker for DFU. The gene may be associated with T cells regulatory (Tregs) and T cells follicular helper, and it influences the NF-κB, GnRH, and MAPK signaling pathway.
This study identified and validated GSTM5 as a biomarker for DFU. This finding may potentially provide a target for immune therapy for DFU.
糖尿病足溃疡（DFU）是一种严重的长期病症，与残疾和死亡的重大风险相关。然而，关于其生物标志物的研究仍然有限。本研究利用生物信息学和机器学习方法来识别DFU的免疫相关生物标志物，并通过外部数据集和动物实验对其进行验证。
本研究使用生物信息学和机器学习分析来自基因表达综合数据库（GEO）的微阵列数据，以识别与DFU相关的关键基因。进行动物实验以验证这些发现。本研究采用从GEO数据库检索的数据集GSE68183和GSE80178作为构建基因机器学习模型的训练数据集，在对数据进行差异分析后，本研究分别使用glmnet包和e1071包构建LASSO和SVM-RFE机器学习模型。随后，我们使用训练集和验证集（GSE134431）对模型进行验证。我们对模型基因进行了富集分析，包括GSEA和GSVA。我们还对模型基因进行了免疫功能分析和免疫相关分析。最后，我们对模型基因进行了免疫组织化学（IHC）验证。
本研究使用机器学习和生物信息学方法将谷胱甘肽S转移酶M5（GSTM5）鉴定为DFU中潜在的免疫相关关键靶点。随后通过外部数据集和IHC实验进行的验证也证实GSTM5是DFU的关键生物标志物。该基因可能与调节性T细胞（Tregs）和滤泡辅助性T细胞有关，并影响核因子κB（NF-κB）、促性腺激素释放激素（GnRH）和丝裂原活化蛋白激酶（MAPK）信号通路。
本研究鉴定并验证了GSTM5作为DFU的生物标志物。这一发现可能为DFU的免疫治疗提供一个靶点。
