Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning

Abstract

Objectives: DFU is a serious chronic disease with high disability and fatality rates, yet there is no completely effective therapy. While ferroptosis is integrated to inflammation and infection, its involvement in DFU is still unclear. The study aimed to identify ferroptosis-related genes in DFU, providing potential therapeutic targets.Methods: In the GEO database, two DFU microarray datasets (GSE147890 and GSE80178) were collected. WGCNA was conducted to identify the modular genes most involved in DFU. Subsequently, enrichment analysis and PPI analysis were performed. To yield the DFU-associated ferroposis genes, the ferroposis genes were retrieved from the FerrDb database and overlapped with the modular genes. Eventually, an optimal DFU prediction model was created by combining multiple machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to detect ferroposis genes most closely associated with DFU. The accuracy of the model was verified by utilizing external datasets (GSE7014) based on ROC curves.Results: WGCNA yielded seven modules in all, and 1223 DFU-related modular genes were identified. GO analysis revealed that inflammatory response, decidualization, and protein binding were the most highly enriched terms. These module genes were also enriched in the ErbB signaling, IL-17 signaling, MAPK signaling, growth hormone synthesis, secretion and action, and tight junction KEGG pathways. Twenty-five DFU-associated ferroposis genes were obtained by cross-linking with modular genes, which could distinguish DFU patients from controls. Ultimately, the prediction model based on machine learning algorithms was well established, with high AUC values (0.79 of LASSO, 0.80 of SVM, 0.75 of Boruta, 0.70 of XGBoost). MAFG and MAPK3 were identified by the prediction model as the most highly associated ferroposis-genes in DFU. Furthermore, the external dataset (GSE29221) validation revealed that MAPK3 (AUC = 0.81) had superior AUC values than MAFG (AUC = 0.62).Conclusion: As the most related ferroptosis-genes with DFU, MAFG and MAPK3 may be employed as potential therapeutic targets for DFU patients. Moreover, MAPK3, with higher accuracy, could be the more potential ferroptosis-related biomarker for further experimental validation.

The role of machine learning in advancing diabetic foot: a review

“…Employing advanced machine learning techniques, such as Lasso regression and random forest algorithms, the research offers new insights into the metabolic shifts characterizing the transition from T2DM to T2DM with DF. Moreover, a seminal investigation conducted by Wang et al., ML algorithms were employed to scrutinize genes associated with ferroptosis in the context of DFUs ( 21 ). The study culminated in the identification of 25 ferroptosis-related genes that could effectively discriminate between patients with DFUs and control subjects.…”

“…Emerging evidence underscores the instrumental role of ML in advancing DF research. Utilizing ML algorithms, clinicians can now leverage biomarkers for the early diagnosis of DF, thereby initiating timely interventions ( 19 – 21 ). Furthermore, ML has been employed to predict the healing trajectory of DF ulcers, assess amputation risks, and formulate personalized treatment regimens ( 22 , 23 ).…”

The role of programmed cell death in diabetic foot ulcers

“…In addition, studies have shown that MAFG and MAPK3 act as death‐related genes that are relevant to DFUs and may become potential therapeutic targets for DFU patients. The higher accuracy of MAPK3 suggests that it may be a promising iron death‐related biomarker 77 . Noncoding RNAs have also been reported to play a role in iron death in DFU patients.…”

Identification of core genes and pathways in vacuum sealing drainage for the treatment of diabetic ulcers via bioinformatics and histological

“…[15] The detected DEGs were used with a DAVID online tool to perform GO annotations. [16] KEGG pathway analysis of the DEGs was carried out with the aid of the KOBAS online analysis database (http://kobas.cbi.pku.edu.cn). Using data from the integrated microarray VSD for the treatment of ulcers, we examined the DEGs that were significantly up-and downregulated in this study.…”