Notch Signaling in Acute Inflammation and Sepsis

Gallenstein, Nadia; Tichy, Lucas; Weigand, Markus; Schenz, Judith

doi:10.3390/ijms24043458

Cited by 24 publications

(13 citation statements)

References 254 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, we also screened the ATM gene as a candidate factor for constructing the model in the present study. Besides, Notch signaling is crucial for the differentiation and homeostasis of immune cells, which contributes to the modulation of organ-speci c immune responses [24,25]. Another study con rmed that the status of NOTCH could be considered a potential predictor of ICI response [26].…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Evaluating Efficacy of Immunotherapy in Non-small-cell Lung Cancer Patients

Zeng

Yuan

et al. 2023

Preprint

View full text Add to dashboard Cite

The predictive accuracy to treatment effect of immune therapy is still poor. Thus, we aimed to develop a predictive model based on gene mutations to assess the immunotherapeutic efficacy in non-small cell lung cancer. Then, 335 NSCLC patients treated with ICIs were included in our study. The least absolute shrinkage and selection operator Cox regression model, multivariable analysis, and Kaplan-Maire test were used in this study. In the end, we constructed a predictive model based on a 42-gene signature. Patients were classified into low-risk and high-risk groups based on risk scores generated from this model. Compared with patients in the high-risk group, patients in the low-risk group had better survival. The results were confirmed in an external validation cohort. Moreover, patients with high TMB and in the high-risk group could not benefit from ICIs. A predictive model of evaluating efficacy of immune therapy was developed and validated. The model is based on multiple genetic information and has clinical translational value.

show abstract

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Evaluating Efficacy of Immunotherapy in Non-small-cell Lung Cancer Patients

Zeng

Yuan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…[16] Some studies have suggested that acquired immune system dysregulation and metabolic disorders both play important roles in the process of sepsis. [17][18][19] In this study, we performed bioinformatics analysis based on relevant microarray datasets to identify several potential hub genes of sepsis. The results suggest that FFAR3, FGF13, FAM89A, and G0S2 could significantly distinguish between septic blood samples and control samples.…”

Section: Discussionmentioning

confidence: 99%

Uncovering hub genes in sepsis through bioinformatics analysis

Liu,

Qiu,

Yang

et al. 2023

Medicine

View full text Add to dashboard Cite

In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment.

show abstract

“…We do not have a clear explanation for this correlation. However, we may note that although the pro-or anti-inflammatory functions of Notch signaling were not shown, it has been shown to modulate inflammatory conditions such as sepsis, and therefore may significantly impact the course of disease [37]. This, however, does not concern DNER.…”

Section: Cytokines Of Interestmentioning

confidence: 96%

GlycA and CRP Are Genetically Correlated: Insight into the Genetic Architecture of Inflammageing

Kasher,

Freidin,

Williams

et al. 2024

Biomolecules

View full text Add to dashboard Cite

Inflammageing is a condition of perpetual low-grade inflammation induced by ageing. Inflammageing may be predicted by the C-reactive protein (CRP) or by a recently described biomarker which measures N-glycosylated side chains of the carbohydrate component of several acute-phase proteins known as GlycA. The objective of this study was to examine in depth the genetic relationships between CRP and GlycA as well as between each of them and other selected cytokines, which may shed light on the mechanisms of inflammageing. Using the Olink 96 Inflammation panel, data on inflammatory mediators for 1518 twins from the TwinsUK dataset were acquired. Summary statistics for genome-wide association studies for several cytokines as well as CRP and GlycA were collected from public sources. Extensive genetic correlation analyses, colocalization and genetic enrichment analyses were carried out to detect the shared genetic architecture between GlycA and CRP. Mendelian randomization was carried out to assess potential causal relationships. GlycA predicted examined cytokines with a magnitude twice as great as that of CRP. GlycA and CRP were significantly genetically correlated (Rg = 0.4397 ± 0.0854, p-value = 2.60 × 10−7). No evidence of a causal relationship between GlycA and CRP, or between these two biomarkers and the cytokines assessed was obtained. However, the aforementioned relationships were explained well by horizontal pleiotropy. Five exonic genetic variants annotated to five genes explain the shared genetic architecture observed between GlycA and CRP: IL6R, GCKR, MLXIPL, SERPINA1, and MAP1A. GlycA and CRP possess a shared genetic architecture, but the relationship between them appears to be modest, which may imply the promotion of differing inflammatory pathways. GlycA appears to be a more robust predictor of cytokines compared to CRP.

show abstract

Notch Signaling in Acute Inflammation and Sepsis

Cited by 24 publications

References 254 publications

A Predictive Model for Evaluating Efficacy of Immunotherapy in Non-small-cell Lung Cancer Patients

A Predictive Model for Evaluating Efficacy of Immunotherapy in Non-small-cell Lung Cancer Patients

Uncovering hub genes in sepsis through bioinformatics analysis

GlycA and CRP Are Genetically Correlated: Insight into the Genetic Architecture of Inflammageing

Contact Info

Product

Resources

About