A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes
Abstract Background Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulf...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Genomics |
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| Online Access: | https://doi.org/10.1186/s12920-024-02076-2 |
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| author | Sijun Li Qingdong Zhu Aichun Huang Yanqun Lan Xiaoying Wei Huawei He Xiayan Meng Weiwen Li Yanrong Lin Shixiong Yang |
| author_facet | Sijun Li Qingdong Zhu Aichun Huang Yanqun Lan Xiaoying Wei Huawei He Xiayan Meng Weiwen Li Yanrong Lin Shixiong Yang |
| author_sort | Sijun Li |
| collection | DOAJ |
| description | Abstract Background Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. Methods We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. Results DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. Conclusion Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease. |
| format | Article |
| id | doaj-art-ba9bacc90dbe48dfb8958a17789d94dd |
| institution | Kabale University |
| issn | 1755-8794 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj-art-ba9bacc90dbe48dfb8958a17789d94dd2025-01-12T12:43:36ZengBMCBMC Medical Genomics1755-87942025-01-0118111710.1186/s12920-024-02076-2A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genesSijun Li0Qingdong Zhu1Aichun Huang2Yanqun Lan3Xiaoying Wei4Huawei He5Xiayan Meng6Weiwen Li7Yanrong Lin8Shixiong Yang9Infectious Disease Laboratory, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningDepartment of Tuberculosis, The Fourth People’s Hospital of NanningAdministrative Office, The Fourth People’s Hospital of NanningAbstract Background Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. Methods We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. Results DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. Conclusion Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.https://doi.org/10.1186/s12920-024-02076-2Chronic obstructive pulmonary diseaseDisulfidptosisDisulfidptosis-related genesImmune cellsMachine learning model |
| spellingShingle | Sijun Li Qingdong Zhu Aichun Huang Yanqun Lan Xiaoying Wei Huawei He Xiayan Meng Weiwen Li Yanrong Lin Shixiong Yang A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes BMC Medical Genomics Chronic obstructive pulmonary disease Disulfidptosis Disulfidptosis-related genes Immune cells Machine learning model |
| title | A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes |
| title_full | A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes |
| title_fullStr | A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes |
| title_full_unstemmed | A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes |
| title_short | A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes |
| title_sort | machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis related genes |
| topic | Chronic obstructive pulmonary disease Disulfidptosis Disulfidptosis-related genes Immune cells Machine learning model |
| url | https://doi.org/10.1186/s12920-024-02076-2 |
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