Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning
Background Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosis and prognosis indicator for patient with sepsis....
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PeerJ Inc.
2025-02-01
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| author | Mi Liu Xingxing Gao Hongfa Wang Yiping Zhang Xiaojun Li Renlai Zhu Yunru Sheng |
| author_facet | Mi Liu Xingxing Gao Hongfa Wang Yiping Zhang Xiaojun Li Renlai Zhu Yunru Sheng |
| author_sort | Mi Liu |
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| description | Background Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosis and prognosis indicator for patient with sepsis. Methods Fourteen PCD patterns were analyzed for model construction. Seven transcriptome datasets and a single cell sequencing dataset were collected from the Gene Expression Omnibus database. Results A total of 289 PCD-related differentially expressed genes were identified between sepsis patients and healthy individuals. The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. The area under curve of the diagnostic model reached 100.0% in the training set and 100.0%, 99.9%, 98.9%, 99.5% and 98.6% in five validation sets. Furthermore, we verified the diagnostic genes in sepsis patients from our center via qPCR experiment. Single cell sequencing analysis revealed that NLRC4, TXN and S100A9 were mainly expressed on myeloid/monocytes and dendritic cells. Immune infiltration analysis revealed that multiple immune cells involved in the development of sepsis. Correlation and gene set enrichment analysis (GSEA) analysis revealed that the three biomarkers were significantly associated with immune cells infiltration. Conclusions We developed and validated a diagnostic model for sepsis based on three PCD-related genes. Our study might provide potential peripheral blood diagnostic candidate biomarkers for patients with sepsis. |
| format | Article |
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| language | English |
| publishDate | 2025-02-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj-art-361cf3a2e96c45e88206572aeb39345f2025-08-20T02:55:07ZengPeerJ Inc.PeerJ2167-83592025-02-0113e1907710.7717/peerj.19077Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learningMi Liu0Xingxing Gao1Hongfa Wang2Yiping Zhang3Xiaojun Li4Renlai Zhu5Yunru Sheng6Center for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hang Zhou, ChinaDepartment of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hang Zhou, ChinaCenter for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hang Zhou, ChinaCenter for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hang Zhou, ChinaCenter for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hang Zhou, ChinaCenter for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hang Zhou, ChinaCenter for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hang Zhou, ChinaBackground Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosis and prognosis indicator for patient with sepsis. Methods Fourteen PCD patterns were analyzed for model construction. Seven transcriptome datasets and a single cell sequencing dataset were collected from the Gene Expression Omnibus database. Results A total of 289 PCD-related differentially expressed genes were identified between sepsis patients and healthy individuals. The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. The area under curve of the diagnostic model reached 100.0% in the training set and 100.0%, 99.9%, 98.9%, 99.5% and 98.6% in five validation sets. Furthermore, we verified the diagnostic genes in sepsis patients from our center via qPCR experiment. Single cell sequencing analysis revealed that NLRC4, TXN and S100A9 were mainly expressed on myeloid/monocytes and dendritic cells. Immune infiltration analysis revealed that multiple immune cells involved in the development of sepsis. Correlation and gene set enrichment analysis (GSEA) analysis revealed that the three biomarkers were significantly associated with immune cells infiltration. Conclusions We developed and validated a diagnostic model for sepsis based on three PCD-related genes. Our study might provide potential peripheral blood diagnostic candidate biomarkers for patients with sepsis.https://peerj.com/articles/19077.pdfSepsisProgrammed cell deathDiagnosisMachine learningNomogram |
| spellingShingle | Mi Liu Xingxing Gao Hongfa Wang Yiping Zhang Xiaojun Li Renlai Zhu Yunru Sheng Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning PeerJ Sepsis Programmed cell death Diagnosis Machine learning Nomogram |
| title | Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning |
| title_full | Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning |
| title_fullStr | Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning |
| title_full_unstemmed | Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning |
| title_short | Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning |
| title_sort | leveraging diverse cell death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning |
| topic | Sepsis Programmed cell death Diagnosis Machine learning Nomogram |
| url | https://peerj.com/articles/19077.pdf |
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