Identification of Subtypes and Diagnostic Markers Related to Necroptosis in Sepsis

Sepsis is a serious systemic infection with a high mortality rate. More and more evidence suggested that necroptosis plays a crucial role in the pathogenesis and progression of sepsis. This study aimed to elucidate the biological function and clinical significance of necroptosis in sepsis, and identify new potential biomarkers to improve the diagnosis and treatment of sepsis. Firstly, we identified 40 differentially expressed necroptosis related genes (DENRGs). Subsequently, a protein interaction (PPI) network of 40 DENRGs was constructed. Based on the key NRGs in the PPI network, the LASSO algorithm was used to screen eight diagnostic-related NRGs in sepsis, and a diagnostic model and risk score were constructed. The ROC analysis results indicated that the eight NRGs diagnostic model has good diagnostic performance (AUC = 0.955). There is a significant difference in risks core between normal samples and sepsis patients. The results of immune infiltration analysis showed that eight diagnostic-related NRGs were significantly correlated with multiple immune cells. Given the clinical significance of necroptosis in sepsis, we identified two molecular subtypes of sepsis based on eight NRGs. The necroptosis score of subtype 1 is significantly lower than that of subtype 2, while the immune score of subtype 1 is significantly higher than that of subtype 2. In summary, we developed and validated a diagnostic model and risk score based on eight NRGs, and identified two completely different subtypes associated with sepsis. Our research may provide new insights into the mechanisms of necroptosis in sepsis and the identification of potential biomarkers.

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Publication Details

Journal
Applied Biochemistry and Biotechnology
Published
2025-02-26
DOI
https://doi.org/10.1007/s12010-025-05201-8
Primary Topic
Cell death mechanisms and regulation
Type
article
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article

Identification of Subtypes and Diagnostic Markers Related to Necroptosis in Sepsis

Bing Zhu, Bing Wang, Xia Xie, Ning Meng et al.
Applied Biochemistry and Biotechnology
Cell death mechanisms and regulation
article

Identification of Subtypes and Diagnostic Markers Related to Necroptosis in Sepsis

Bing Zhu, Bing Wang, Xia Xie, Ning Meng, Sen Peng
article en

Abstract

Sepsis is a serious systemic infection with a high mortality rate. More and more evidence suggested that necroptosis plays a crucial role in the pathogenesis and progression of sepsis. This study aimed to elucidate the biological function and clinical significance of necroptosis in sepsis, and identify new potential biomarkers to improve the diagnosis and treatment of sepsis. Firstly, we identified 40 differentially expressed necroptosis related genes (DENRGs). Subsequently, a protein interaction (PPI) network of 40 DENRGs was constructed. Based on the key NRGs in the PPI network, the LASSO algorithm was used to screen eight diagnostic-related NRGs in sepsis, and a diagnostic model and risk score were constructed. The ROC analysis results indicated that the eight NRGs diagnostic model has good diagnostic performance (AUC = 0.955). There is a significant difference in risks core between normal samples and sepsis patients. The results of immune infiltration analysis showed that eight diagnostic-related NRGs were significantly correlated with multiple immune cells. Given the clinical significance of necroptosis in sepsis, we identified two molecular subtypes of sepsis based on eight NRGs. The necroptosis score of subtype 1 is significantly lower than that of subtype 2, while the immune score of subtype 1 is significantly higher than that of subtype 2. In summary, we developed and validated a diagnostic model and risk score based on eight NRGs, and identified two completely different subtypes associated with sepsis. Our research may provide new insights into the mechanisms of necroptosis in sepsis and the identification of potential biomarkers.

Applied Biochemistry and BiotechnologyVol. 197(6)
Union Hospital (HK)
Openalex Percentile: Top 97%
Cell death mechanisms and regulation
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