An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers
PurposeDiabetic foot ulcers (DFUs), a serious diabetes complication, greatly increase disability and mortality, underscoring the need for effective diagnostic markers.MethodsWe used GSE199939 and GSE134431 datasets from the Gene Expression Omnibus (GEO) database, removed batch effects, and identifie...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1620749/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849706434020769792 |
|---|---|
| author | Yingnan Li Ning Xiao Zhuoqun Wang Wenhai Wang Fengjiao Li Jiren Wang |
| author_facet | Yingnan Li Ning Xiao Zhuoqun Wang Wenhai Wang Fengjiao Li Jiren Wang |
| author_sort | Yingnan Li |
| collection | DOAJ |
| description | PurposeDiabetic foot ulcers (DFUs), a serious diabetes complication, greatly increase disability and mortality, underscoring the need for effective diagnostic markers.MethodsWe 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.ResultsWe 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.ConclusionThis research highlights DCT, PMEL, and KIT as diagnostic biomarkers for DFUs, which are linked to melanin production and the MAPK/PI3K-Akt signaling pathways. |
| format | Article |
| id | doaj-art-22e9d62aaf064cb3a4ef96bfa47f2f1d |
| institution | DOAJ |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-22e9d62aaf064cb3a4ef96bfa47f2f1d2025-08-20T03:16:12ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-06-011610.3389/fgene.2025.16207491620749An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcersYingnan Li0Ning Xiao1Zhuoqun Wang2Wenhai Wang3Fengjiao Li4Jiren Wang5Hand and Foot Surgery and Burn and Plastic Surgery, Jilin Province FAW General Hospital, Changchun, Jilin, ChinaOffice of Clinical Trial Institutions, Jilin Province FAW General Hospital, Changchun, Jilin, ChinaDepartment of Neurology, Jilin Province FAW General Hospital, Changchun, Jilin, ChinaDepartment of Cardiology, Jilin Province FAW General Hospital, Changchun, Jilin, ChinaDepartment of Anesthesiology, Jilin Province FAW General Hospital, Changchun, Jilin, ChinaHand and Foot Surgery and Burn and Plastic Surgery, Jilin Province FAW General Hospital, Changchun, Jilin, ChinaPurposeDiabetic foot ulcers (DFUs), a serious diabetes complication, greatly increase disability and mortality, underscoring the need for effective diagnostic markers.MethodsWe 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.ResultsWe 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.ConclusionThis research highlights DCT, PMEL, and KIT as diagnostic biomarkers for DFUs, which are linked to melanin production and the MAPK/PI3K-Akt signaling pathways.https://www.frontiersin.org/articles/10.3389/fgene.2025.1620749/fulldiabetic foot ulcersWGCNAPPI networkmelanin synthesisdiagnosis |
| spellingShingle | Yingnan Li Ning Xiao Zhuoqun Wang Wenhai Wang Fengjiao Li Jiren Wang An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers Frontiers in Genetics diabetic foot ulcers WGCNA PPI network melanin synthesis diagnosis |
| title | An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers |
| title_full | An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers |
| title_fullStr | An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers |
| title_full_unstemmed | An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers |
| title_short | An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers |
| title_sort | analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers |
| topic | diabetic foot ulcers WGCNA PPI network melanin synthesis diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1620749/full |
| work_keys_str_mv | AT yingnanli ananalysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT ningxiao ananalysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT zhuoqunwang ananalysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT wenhaiwang ananalysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT fengjiaoli ananalysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT jirenwang ananalysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT yingnanli analysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT ningxiao analysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT zhuoqunwang analysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT wenhaiwang analysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT fengjiaoli analysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers AT jirenwang analysisofgeneexpressionprofilesthroughmachinelearninguncoversthenewdiagnosticsignaturefordiabeticfootulcers |