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pmid:34326834

SDF4 Is a Prognostic Factor for 28-Days Mortality in Patients With Sepsis via Negatively Regulating ER Stress.

Tingting Zhu · Qun Su · Cuili Wang · Lingling Shen · Hongjun Chen · Shi Feng · Xiaofeng Peng · Siyu Chen · Yucheng Wang · Hong Jiang · Jianghua Chen

Frontiers in immunology · 2021 doi:10.3389/fimmu.2021.659193

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Experiment

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Data

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title
SDF4 Is a Prognostic Factor for 28-Days Mortality in Patients With Sepsis via Negatively Regulating ER Stress.
doi
10.3389/fimmu.2021.659193
pmid
34326834
authors array · 11 items
[0]
Tingting Zhu
[1]
Qun Su
[2]
Cuili Wang
[3]
Lingling Shen
[4]
Hongjun Chen
[5]
Shi Feng
[6]
Xiaofeng Peng
[7]
Siyu Chen
[8]
Yucheng Wang
[9]
Hong Jiang
[10]
Jianghua Chen
journal
Frontiers in immunology
year
2021
abstract
Sepsis is a heterogeneous syndrome induced by infection and results in high mortality. Even though more than 100 biomarkers for sepsis prognosis were evaluated, prediction of patient outcomes in sepsis continues to be driven by clinical signs because of unsatisfactory specificity and sensitivity of these biomarkers. This study aimed to elucidate the key candidate genes involved in sepsis response and explore their downstream effects based on weighted gene co-expression network analysis (WGCNA). The dataset GSE63042 with sepsis outcome information was obtained from the Gene Expression Omnibus (GEO) database and then consensus WGCNA was conducted. We identified the hub gene SDF4 (stromal cell derived factor 4) from the M6 module, which was significantly associated with mortality. Subsequently, two datasets (GSE54514 and E-MTAB-4421) and cohort validation (n=89) were performed. Logistic regression analysis was used to build a prediction model and the combined score resulting in a satisfactory prognosis value (area under the ROC curve=0.908). The model was subsequently tested by another sepsis cohort (n=70, ROC= 0.925). We next demonstrated that endoplasmic reticulum (ER) stress tended to be more severe in patients PBMCs with negative outcomes compared to those with positive outcomes and SDF4 was related to this phenomenon. In addition, our results indicated that adenovirus-mediated Sdf4 overexpression attenuated ER stress in cecal ligation and puncture (CLP) mice lung. In summary, our study indicates that incorporation of SDF4 can improve clinical parameters predictive value for the prognosis of sepsis, and decreased expression levels of SDF4 contributes to excessive ER stress, which is associated with worsened outcomes, whereas overexpression of SDF4 attenuated such activation.
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A logistic regression model was conducted to create a combined predictive score. The publicly available gene expression profile GSE63042 was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). In order to verify the expression of hub genes with larger sample size, two datasets (GSE54514 and E-MTAB-4421) (28, 29) with sepsis prognosis information were downloaded from GEO and ArrayExpress (https://www.ebi.ac.uk/arrayexpress/), respectively.
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sections[18] "Statistical Analyses" paragraph 2
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A logistic regression model was conducted to create a combined predictive score.
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sections[3] "RNA Sequencing Data and Data Pre-Processing" paragraph 2
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The publicly available gene expression profile GSE63042 was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).
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sections[8] "Dataset Validation" paragraph 2
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In order to verify the expression of hub genes with larger sample size, two datasets (GSE54514 and E-MTAB-4421) (28, 29) with sepsis prognosis information were downloaded from GEO and ArrayExpress (https://www.ebi.ac.uk/arrayexpress/), respectively.
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Publicly available datasets were analyzed in this study. This data can be found here: GSE63042, GSE54514 and E-MTAB-4421.
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Quantification of SDF4 in Predicting Sepsis Outcome
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Results of multivariate logistic regression modeling.
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Quantification of SDF4 in Predicting Sepsis Outcome
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Results of multivariate logistic regression modeling.
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A logistic regression model was conducted to create a combined predictive score. The publicly available gene expression profile GSE63042 was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). In order to verify the expression of hub genes with larger sample size, two datasets (GSE54514 and E-MTAB-4421) (28, 29) with sepsis prognosis information were downloaded from GEO and ArrayExpress (https://www.ebi.ac.uk/arrayexpress/), respectively.
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Even though more than 100 biomarkers for sepsis prognosis were evaluated, prediction of patient outcomes in sepsis continues to be driven by clinical signs because of unsatisfactory specificity and sensitivity of these biomarkers. This study aimed to elucidate the key candidate genes involved in sepsis response and explore their downstream effects based on weighted gene co-expression network analysis (WGCNA). The dataset GSE63042 with sepsis outcome information was obtained from the Gene Expression Omnibus (GEO) database and then consensus WGCNA was conducted. We identified the hub gene SDF4 (stromal cell derived factor 4) from the M6 module, which was significantly associated with mortality. Subsequently, two datasets (GSE54514 and E-MTAB-4421) and cohort validation (n=89) were performed. Logistic regression analysis was used to build a prediction model and the combined score resulting in a satisfactory prognosis value (area under the ROC curve=0.908). The model was subsequently tested by another sepsis cohort (n=70, ROC= 0.925). We next demonstrated that endoplasmic reticulum (ER) stress tended to be more severe in patients PBMCs with negative outcomes compared to those with positive outcomes and SDF4 was related to this phenomenon. In addition, our results indicated that adenovirus-mediated Sdf4 overexpression attenuated ER stress in cecal ligation and puncture (CLP) mice lung. In summary, our study indicates that incorporation of SDF4 can improve clinical parameters predictive value for the prognosis of sepsis, and decreased expression levels of SDF4 contributes to excessive ER stress, which is associated with worsened outcomes, whereas overexpression of SDF4 attenuated such activation.", "claims": [{"id": "claim-method", "statement": "A logistic regression model was conducted to create a combined predictive score. 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