通过机器学习对基因表达谱进行分析，发现了糖尿病足溃疡的新诊断特征。

An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers.

作者信息

Li Yingnan, Xiao Ning, Wang Zhuoqun, Wang Wenhai, Li Fengjiao, Wang Jiren

机构信息

Hand and Foot Surgery and Burn and Plastic Surgery, Jilin Province FAW General Hospital, Changchun, Jilin, China.

Office of Clinical Trial Institutions, Jilin Province FAW General Hospital, Changchun, Jilin, China.

出版信息

Front Genet. 2025 Jun 24;16:1620749. doi: 10.3389/fgene.2025.1620749. eCollection 2025.

DOI:10.3389/fgene.2025.1620749

PMID:40630119

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12234326/

Abstract

PURPOSE

Diabetic foot ulcers (DFUs), a serious diabetes complication, greatly increase disability and mortality, underscoring the need for effective diagnostic markers.

METHODS

We used GSE199939 and GSE134431 datasets from the Gene Expression Omnibus (GEO) database, removed batch effects, and identified differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules, followed by the integration of the protein-protein interaction (PPI) network to screen key genes, which were further optimized using LASSO regression. The gene set enrichment analysis (GSEA) analyzed key gene-related pathways, CIBERSORT assessed immune infiltration, and potential target drugs were predicted using the DGIdb database.

RESULTS

We identified 403 DEGs in DFUs, intersected them with 2,342 genes from a DFU-related WGCNA module to find 193 overlapping genes, and screened candidates via PPI network. LASSO regression finalized , , and as the key genes. GSEA analysis showed these three genes may influence the MAPK and PI3K-Akt pathways and were positively correlated with Dendritic. cells.resting. Drug target prediction identified 85 potential drugs for , six for , and six for .

CONCLUSION

This research highlights , , and as diagnostic biomarkers for DFUs, which are linked to melanin production and the MAPK/PI3K-Akt signaling pathways.

摘要

目的

糖尿病足溃疡（DFUs）是一种严重的糖尿病并发症，会大幅增加残疾率和死亡率，这凸显了对有效诊断标志物的需求。

方法

我们使用了来自基因表达综合数据库（GEO）的GSE199939和GSE134431数据集，消除批次效应，并鉴定差异表达基因（DEGs）。使用加权基因共表达网络分析（WGCNA）来识别共表达模块，随后整合蛋白质-蛋白质相互作用（PPI）网络以筛选关键基因，并使用套索回归对其进一步优化。基因集富集分析（GSEA）分析关键基因相关途径，CIBERSORT评估免疫浸润，并使用DGIdb数据库预测潜在的靶向药物。

结果

我们在DFUs中鉴定出403个DEGs，将它们与来自DFU相关WGCNA模块的2342个基因进行交集分析，以找到193个重叠基因，并通过PPI网络筛选候选基因。套索回归确定、和为关键基因。GSEA分析表明这三个基因可能影响MAPK和PI3K-Akt途径，并且与静息树突状细胞呈正相关。药物靶点预测确定了针对的85种潜在药物、针对的6种潜在药物和针对的6种潜在药物。