Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning
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, pr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.944425/full |
| _version_ | 1811258522283802624 |
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| author | Xingkai Wang Guidong Jiang Junwei Zong Junwei Zong Decheng Lv Ming Lu Xueling Qu Xueling Qu Shouyu Wang Shouyu Wang |
| author_facet | Xingkai Wang Guidong Jiang Junwei Zong Junwei Zong Decheng Lv Ming Lu Xueling Qu Xueling Qu Shouyu Wang Shouyu Wang |
| author_sort | Xingkai Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T18:15:32Z |
| format | Article |
| id | doaj.art-02fa10dde0544ef5baa89829a2cfd7bb |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-12T18:15:32Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-02fa10dde0544ef5baa89829a2cfd7bb2022-12-22T03:21:39ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-09-011310.3389/fgene.2022.944425944425Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learningXingkai Wang0Guidong Jiang1Junwei Zong2Junwei Zong3Decheng Lv4Ming Lu5Xueling Qu6Xueling Qu7Shouyu Wang8Shouyu Wang9Department of Orthopedic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaDalian Medical University, Dalian, ChinaDepartment of Orthopedic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaCollege of Integrative Medicine, Dalian Medical University, Dalian, ChinaDepartment of Orthopedic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaTrauma and Tissue Repair Surgery Department, Dalian Municipal Central Hospital, Dalian, ChinaPelvic Floor Repair Center, The Affiliated Dalian Maternity Hospital of Dalian Medical University, Dalian, ChinaPelvic floor repair center, Dalian Women and Children Medical Center (Group), Dalian, ChinaDepartment of Orthopedic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaCollege of Integrative Medicine, Dalian Medical University, Dalian, ChinaObjectives: 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.https://www.frontiersin.org/articles/10.3389/fgene.2022.944425/fullDFUWGCNAferroptosismachine learningprediction model |
| spellingShingle | Xingkai Wang Guidong Jiang Junwei Zong Junwei Zong Decheng Lv Ming Lu Xueling Qu Xueling Qu Shouyu Wang Shouyu Wang Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning Frontiers in Genetics DFU WGCNA ferroptosis machine learning prediction model |
| title | Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning |
| title_full | Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning |
| title_fullStr | Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning |
| title_full_unstemmed | Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning |
| title_short | Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning |
| title_sort | revealing the novel ferroptosis related therapeutic targets for diabetic foot ulcer based on the machine learning |
| topic | DFU WGCNA ferroptosis machine learning prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.944425/full |
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