Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Cheng, Lixin; Nan, Chuanchuan; Kang, Lin; Zhang, Ning; Liu, Sheng; Chen, Huaisheng; Hong, Chengying; Chen, Youlian; Liang, Zhen; Liu, Xueyan

doi:10.1186/s12967-020-02372-2

Cited by 44 publications

(20 citation statements)

References 50 publications

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“…WGCNA has also been used to analyze correlation patterns in sepsis. These research efforts focused on inflammatory cells in vitro ( 21 ), long non-coding RNA ( 22 ), transcription factors and miRNA ( 23 ) based on samples with or without sepsis, but did not distinguish between patients with positive outcomes from those with negative outcomes. A recent study identified two transcription factors, CEBPB and ETV6, associated with mortality outcome via WGCNA ( 24 ).…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Wang

et al. 2021

Front. Immunol.

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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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Section: Introductionmentioning

confidence: 99%

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

Zhu

Wang

et al. 2021

Front. Immunol.

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“…Many studies have reported the genome-wide expression profiling of sepsis [13,16,17]. The differential gene expression has been well characterized between sepsis versus non-sepsis inflammatory responses [18], survivors versus non-survivors [19,20], and sepsis of viral versus bacterial causes [21]. Two endotypes of sepsis were identified [20,22,23].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

Zhang

Pan

et al. 2020

eBioMedicine

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Background Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. Methods The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets ( n = 780) to evaluate its prognostic and predictive performance. Findings A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort ( n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 – 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 – 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 – 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211). Interpretation Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership. Funding The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

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“…In the present study, MALAT1 was not co-expressed with numerous DEGs but analysis of hybridisation energy showed that it can regulate even 85 of them. It suggests that MALAT1 can act at various molecular level not only transcript blocking [41]. Thus, our observa-tion and previous literature reports persuade us to continue researching MALAT1 in the obesity regulation context because the interactions are still not clear.…”

Section: Validation Of Rna-seq Resultsmentioning

confidence: 59%

Long-non Coding RNAs Related to Fat Deposition in Pigs Included lncRNA Corresponding to Human MALAT1

Piórkowska¹,

Żukowski²,

Ropka‐Molik³

et al. 2021

Preprint

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Obesity is a problem in the last decades since the development of different technologies forced the submission of a faster pace of life, resulting in nutrition style changes. In turn, domestic pigs are an excellent animal model in recognition of adiposity-related processes, corresponding to the size of individual organs, the distribution of body fat in the organism, and similar metabolism. The present study applied the next-generation sequencing method to identify adipose tissue (AT) transcriptomic signals related to increased fat content by identifying differentially expressed genes (DEGs), included long-non coding RNA molecules. The Freiburg RNA tool was applied to recognise predicting hybridisation energy of RNA-RNA interactions. The results indicated several long non-coding RNAs (lncRNAs) whose expression was significantly positively or negatively associated with fat deposition. lncRNAs play an essential role in regulating gene expression by sponging miRNA, binding transcripts, facilitating translation, or coding other smaller RNA regulatory elements. In the pig fat tissue of obese group, increased expression of lncRNAs corresponding to human MALAT1 was observed that previously recognised in the obesity-related context. Moreover, hybridisation energy analyses pinpointed numerous potential interactions between identified differentially expressed lncRNAs, and obesity-related genes and miRNAs expressed in AT.

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Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Cited by 44 publications

References 50 publications

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

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

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

Long-non Coding RNAs Related to Fat Deposition in Pigs Included lncRNA Corresponding to Human MALAT1

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