Inhibition of DDX3X alleviates persistent inflammation, immune suppression and catabolism syndrome in a septic mice model

Liu, Yukun; Zhang, Yongsheng; Wang, Chuntao; Liu, Qinxin; Li, Tianyu; Wang, Wei; Yang, Fan; Li, Zhanfei; Bai, Xiangjun; Wang, Yuchang

doi:10.1016/j.intimp.2023.109779

Cited by 5 publications

(3 citation statements)

References 38 publications

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“… 36 Previous studies have shown that inhibiting DDX60 can significantly increase the level of CD4+ T cells, alleviate oxidative stress, and prolong the survival of septic mice. 37 , 38 The Transporter Associated with Antigen Processing 2 (TAP2) gene is a major histocompatibility complex (MHC) gene situated between HLA-DQ and HLA-DP. 39 , 40 TAP2 has been identified as a reliable marker for patients with a Mars1 endotype, consistently associated with 28-day mortality in sepsis cohorts, 13 suggesting that TAP2 might be beneficial for diagnosing sepsis.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Screening and Validation of PANoptosis-Related Gene Signatures in Sepsis

Xu,

Zhu,

Luo

et al. 2024

JIR

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Background Sepsis is a syndrome marked by life-threatening organ dysfunction and a disrupted host immune response to infection. PANoptosis is a recent conceptual development, which emphasises the interconnectedness among multiple programmed cell deaths in various diseases. Nevertheless, the role of PANoptosis in sepsis is still unclear. Methods We utilized the GSE65682 dataset to identify PANoptosis-related genes (PRGs) and associated immune characteristics in sepsis, classified sepsis samples based on PRGs using the ConsensusClusterPlus method and applied the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify cluster-specific hub genes. Based on PANoptosis -specific DEGs, we compared results from machine learning models and the best-performing model was selected. Predictive efficiency was validated through external dataset, nomogram, survival analysis, quantitative real-time PCR, and western blot. Results The expression levels of PRGs were generally dysregulated in sepsis patients compared with normal samples, and higher PRGs expression correlated with increased immune cell infiltration. In addition, two distinct PANoptosis-related clusters were defined, and functional analysis indicated that DEGs associated with these clusters were primarily linked to immune-related pathways. The SVM model was selected as best-performing model, with lower residuals and the highest area under the curve (AUC = 0.967), which was then validated in an external dataset (AUC = 0.989) and through in vivo experiments. Additional validation through nomogram and survival analysis further confirmed its substantial predictive efficacy. Conclusion Our findings exposed the intricate association between PANoptosis and sepsis, offering important insights on sepsis diagnosis and potential therapeutic targets.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Screening and Validation of PANoptosis-Related Gene Signatures in Sepsis

Xu,

Zhu,

Luo

et al. 2024

JIR

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“…By combining multiple variables and their respective weights, the nomogram provides a visualized model for risk prediction ( 23 , 24 ), enabling clinicians to make effective predictions of the probability of PIICS occurrence in individual patients. This can aid in identifying high-risk patients and implementing targeted interventions, such as immunomodulatory therapy or nutritional support ( 25 ), to mitigate the progression of PIICS and its associated complications.…”

Section: Discussionmentioning

confidence: 99%

“…The top three body regions with the most severe injuries were the head and neck (26.5%), abdomen (20.1%), and chest (20%). The median NRS 2002 and ISS scores were 3 (1-3) and 25 (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33), respectively.…”

Section: Descriptive Datamentioning

confidence: 99%

Development and validation of a nomogram for predicting persistent inflammation, immunosuppression, and catabolism syndrome in trauma patients

Xu,

Kang,

Wang

et al. 2023

Front. Med.

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BackgroundPersistent Inflammation, Immunosuppression, and Catabolism Syndrome (PIICS) is a significant contributor to adverse long-term outcomes in severe trauma patients.ObjectiveThe objective of this study was to establish and validate a PIICS predictive model in severe trauma patients, providing a practical tool for early clinical prediction.Patients and methodsAdult severe trauma patients with an Injury Severity Score (ISS) of ≥16, admitted between October 2020 and December 2022, were randomly divided into a training set and a validation set in a 7:3 ratio. Patients were classified into PIICS and non-PIICS groups based on diagnostic criteria. LASSO regression was used to select appropriate variables for constructing the prognostic model. A logistic regression model was developed and presented in the form of a nomogram. The performance of the model was evaluated using calibration and ROC curves.ResultsA total of 215 patients were included, consisting of 155 males (72.1%) and 60 females (27.9%), with a median age of 51 years (range: 38–59). NRS2002, ISS, APACHE II, and SOFA scores were selected using LASSO regression to construct the prognostic model. The AUC of the ROC analysis for the predictive model in the validation set was 0.84 (95% CI 0.72–0.95). The Hosmer-Lemeshow test in the validation set yielded a χ2 value of 14.74, with a value of p of 0.098.ConclusionAn accurate and easily implementable PIICS risk prediction model was established. It can enhance risk stratification during hospitalization for severe trauma patients, providing a novel approach for prognostic prediction.

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New insights into the function of the NLRP3 inflammasome in sarcopenia: mechanism and therapeutic strategies

Zou,

Tang,

Yang

et al. 2024

Metabolism

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Inhibition of DDX3X alleviates persistent inflammation, immune suppression and catabolism syndrome in a septic mice model

Cited by 5 publications

References 38 publications

Machine Learning Screening and Validation of PANoptosis-Related Gene Signatures in Sepsis

Machine Learning Screening and Validation of PANoptosis-Related Gene Signatures in Sepsis

Development and validation of a nomogram for predicting persistent inflammation, immunosuppression, and catabolism syndrome in trauma patients

New insights into the function of the NLRP3 inflammasome in sarcopenia: mechanism and therapeutic strategies

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