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

**Authors:** Yingnan Li, Ning Xiao, Zhuoqun Wang, Wenhai Wang, Fengjiao Li, Jiren Wang

PMC · DOI: 10.3389/fgene.2025.1620749 · Frontiers in Genetics · 2025-06-24

## TL;DR

This study uses machine learning to identify three genes as potential diagnostic markers for diabetic foot ulcers, linked to key biological pathways.

## Contribution

The study introduces a novel diagnostic gene signature (DCT, PMEL, KIT) for diabetic foot ulcers derived from machine learning and pathway analysis.

## Key findings

- 403 differentially expressed genes were identified in diabetic foot ulcers.
- DCT, PMEL, and KIT were selected as key diagnostic genes through LASSO regression.
- The genes are associated with the MAPK and PI3K-Akt pathways and melanin production.

## Abstract

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

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.

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 DCT, PMEL, and KIT 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 KIT, six for DCT, and six for PMEL.

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

## Linked entities

- **Genes:** DCT (dopachrome tautomerase) [NCBI Gene 1638], PMEL (premelanosome protein) [NCBI Gene 6490], KIT (KIT proto-oncogene, receptor tyrosine kinase) [NCBI Gene 3815]

## Full-text entities

- **Genes:** PIK3CB (phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta) [NCBI Gene 5291] {aka P110BETA, PI3K, PI3KBETA, PIK3C1}, PMEL (premelanosome protein) [NCBI Gene 6490] {aka D12S53E, HMB-45, HMB45, ME20, ME20-M, ME20M}, DCT (dopachrome tautomerase) [NCBI Gene 1638] {aka OCA8, TRP-2, TYRP2}, KIT (KIT proto-oncogene, receptor tyrosine kinase) [NCBI Gene 3815] {aka C-Kit, CD117, MASTC, PBT, SCFR}, AKT1 (AKT serine/threonine kinase 1) [NCBI Gene 207] {aka AKT, PKB, PKB-ALPHA, PRKBA, RAC, RAC-ALPHA}
- **Diseases:** diabetes (MESH:D003920), DFUs (MESH:D017719)
- **Chemicals:** melanin (MESH:D008543)

## Full text

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## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12234326/full.md

## References

47 references — full list in the complete paper: https://tomesphere.com/paper/PMC12234326/full.md

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Source: https://tomesphere.com/paper/PMC12234326