Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure
Abstract Background Sepsis is frequently combined with acute liver failure (ALF), a critical determinant in the mortality of septic patients. Pyroptosis is a significant form of programmed cell death that plays an important role in the inflammatory response. Research has been conducted to elucidate...
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BMC
2025-08-01
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| Series: | Hereditas |
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| Online Access: | https://doi.org/10.1186/s41065-025-00522-4 |
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| author | Jing Yan Yifeng Pan Chaoqi Chen Lijian Liu Jinjing Tan Juan Li Liqun Li Sheng Xie |
| author_facet | Jing Yan Yifeng Pan Chaoqi Chen Lijian Liu Jinjing Tan Juan Li Liqun Li Sheng Xie |
| author_sort | Jing Yan |
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| description | Abstract Background Sepsis is frequently combined with acute liver failure (ALF), a critical determinant in the mortality of septic patients. Pyroptosis is a significant form of programmed cell death that plays an important role in the inflammatory response. Research has been conducted to elucidate the relationship between pyroptosis, sepsis, and ALF, but the mechanism of action remains unclear. Methods Datasets relating to sepsis and ALF were obtained from the Gene Expression Omnibus (GEO). The intersection of differentially expressed genes (DEGs) and pyroptosis-related genes for sepsis and ALF was identified. Simultaneously, a gene diagnosis model for sepsis and ALF was developed using machine learning, and the model’s accuracy was assessed through the plotting of the ROC curves and confusion matrix. The Hub genes identified by the model with an area under the curve (AUC) value ≥ 0.7 were used for the investigation of immune cell infiltration to explain the molecular mechanism of sepsis combined with ALF. The precise mechanism of action of these model genes in sepsis combined with ALF was evaluated through animal experiments. Results Machine learning revealed that GABARAP and ITCH may serve as diagnostic biomarkers for pyroptosis in sepsis combined with ALF. The examination of immune cell infiltration indicated that immune dysregulation is present in both sepsis and ALF and preliminarily suggested that GABARAP and ITCH may be pivotal in cellular immunity responses, particularly those mediated by T cells. Animal experiments further validated that in the process of sepsis combined with ALF, the expression level of GABARAP is elevated, while the expression level of ITCH is diminished. Conclusions We found GABARAP and ITCH may serve as diagnostic biomarkers for pyroptosis in sepsis combined with ALF, suggesting their potential involvement in the initiation and advancement of sepsis combined with ALF through cellular immunomodulatory pathways. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-5ff168e3fdec4c87b57b39c9a0798fcc |
| institution | Kabale University |
| issn | 1601-5223 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Hereditas |
| spelling | doaj-art-5ff168e3fdec4c87b57b39c9a0798fcc2025-08-20T03:43:27ZengBMCHereditas1601-52232025-08-01162111810.1186/s41065-025-00522-4Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failureJing Yan0Yifeng Pan1Chaoqi Chen2Lijian Liu3Jinjing Tan4Juan Li5Liqun Li6Sheng Xie7Graduate School of Guangxi University of Chinese MedicineThe Eighth Clinical Medical College of Guangzhou University of Chinese MedicineGraduate School of Guangxi University of Chinese MedicineThe First Affiliated Hospital of Guangxi University of Chinese MedicineThe First Affiliated Hospital of Guangxi University of Chinese MedicineGraduate School of Guangxi University of Chinese MedicineThe First Affiliated Hospital of Guangxi University of Chinese MedicineThe First Affiliated Hospital of Guangxi University of Chinese MedicineAbstract Background Sepsis is frequently combined with acute liver failure (ALF), a critical determinant in the mortality of septic patients. Pyroptosis is a significant form of programmed cell death that plays an important role in the inflammatory response. Research has been conducted to elucidate the relationship between pyroptosis, sepsis, and ALF, but the mechanism of action remains unclear. Methods Datasets relating to sepsis and ALF were obtained from the Gene Expression Omnibus (GEO). The intersection of differentially expressed genes (DEGs) and pyroptosis-related genes for sepsis and ALF was identified. Simultaneously, a gene diagnosis model for sepsis and ALF was developed using machine learning, and the model’s accuracy was assessed through the plotting of the ROC curves and confusion matrix. The Hub genes identified by the model with an area under the curve (AUC) value ≥ 0.7 were used for the investigation of immune cell infiltration to explain the molecular mechanism of sepsis combined with ALF. The precise mechanism of action of these model genes in sepsis combined with ALF was evaluated through animal experiments. Results Machine learning revealed that GABARAP and ITCH may serve as diagnostic biomarkers for pyroptosis in sepsis combined with ALF. The examination of immune cell infiltration indicated that immune dysregulation is present in both sepsis and ALF and preliminarily suggested that GABARAP and ITCH may be pivotal in cellular immunity responses, particularly those mediated by T cells. Animal experiments further validated that in the process of sepsis combined with ALF, the expression level of GABARAP is elevated, while the expression level of ITCH is diminished. Conclusions We found GABARAP and ITCH may serve as diagnostic biomarkers for pyroptosis in sepsis combined with ALF, suggesting their potential involvement in the initiation and advancement of sepsis combined with ALF through cellular immunomodulatory pathways. Clinical trial number Not applicable.https://doi.org/10.1186/s41065-025-00522-4SepsisAcute liver failurePyroptosisImmune cell infiltrationAnimal experimental validation |
| spellingShingle | Jing Yan Yifeng Pan Chaoqi Chen Lijian Liu Jinjing Tan Juan Li Liqun Li Sheng Xie Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure Hereditas Sepsis Acute liver failure Pyroptosis Immune cell infiltration Animal experimental validation |
| title | Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure |
| title_full | Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure |
| title_fullStr | Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure |
| title_full_unstemmed | Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure |
| title_short | Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure |
| title_sort | integrating bioinformatics analysis machine learning and experimental validation to identify pyroptosis related genes in the diagnosis of sepsis combined with acute liver failure |
| topic | Sepsis Acute liver failure Pyroptosis Immune cell infiltration Animal experimental validation |
| url | https://doi.org/10.1186/s41065-025-00522-4 |
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