50-gene risk profiles in peripheral blood predict COVID-19 outcomes: A retrospective, multicenter cohort study

Guardela, Brenda M. Juan; Sun, Jiehuan; Zhang, Tong; Xu, Bing; Balnis, Joseph; Huang, Yong; Fan, Shwu; Molyneaux, Philip L.; Maher, Toby M.; Noth, Imre; Michaud, Gaetane; Jaitovich, Ariel; Herazo-Maya, José D.

doi:10.1016/j.ebiom.2021.103439

Cited by 23 publications

(21 citation statements)

References 31 publications

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“…COVID-19 gene expression prognosis studies are limited (33,34). Scoring algorithm of molecular subphenotypes (SAMS) have been used to identify 50-gene risk profiles for COVID-19 which discriminate between mild and severe disease (33) The importance of genes in a classification of ICU admission with Random Forest.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…COVID-19 gene expression prognosis studies are limited ( 33 , 34 ). Scoring algorithm of molecular subphenotypes (SAMS) have been used to identify 50-gene risk profiles for COVID-19 which discriminate between mild and severe disease ( 33 ). Such profiles were able to predict ICU admission, the need for mechanical ventilation and mortality with an AUC of 0.77, 0.75 and 0.74 respectively.…”

Section: Discussionmentioning

confidence: 99%

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Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19

et al. 2022

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BackgroundThe COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.MethodsGene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.ResultsThe best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.ConclusionsGene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…COVID-19 gene expression prognosis studies are limited ( 33 , 34 ). Scoring algorithm of molecular subphenotypes (SAMS) have been used to identify 50-gene risk profiles for COVID-19 which discriminate between mild and severe disease ( 33 ). Such profiles were able to predict ICU admission, the need for mechanical ventilation and mortality with an AUC of 0.77, 0.75 and 0.74 respectively.…”

Section: Discussionmentioning

confidence: 99%

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Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19

et al. 2022

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“…When COVID-19 first came out in 2019, people found there was a strong link between IPF and COVID-19 ( Uzel et al, 2020 ; Zhang C. et al, 2021 ). As a study showed that fifty-gene profiles in peripheral blood can predict the outcomes of IPF and COVID-19 patients ( Juan Guardela et al, 2021 ). We supposed that interaction between IPF and COVID-19 may be through some key genes, so the present study was intended to explore some hub genes both involved in IPF and COVID-19.…”

Section: Discussionmentioning

confidence: 99%

A novel gene signature based on the hub genes of COVID-19 predicts the prognosis of idiopathic pulmonary fibrosis

et al. 2022

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Background: Increasing evidence has demonstrated that there was a strong correlation between COVID-19 and idiopathic pulmonary fibrosis (IPF). However, the studies are limited, and the real biological mechanisms behind the IPF progression were still uncleared.Methods: GSE70866 and GSE 157103 datasets were downloaded. The weight gene co-expression network analysis (WGCNA) algorithms were conducted to identify the most correlated gene module with COVID-19. Then the genes were extracted to construct a risk signature in IPF patients by performing Univariate and Lasso Cox Regression analysis. Univariate and Multivariate Cox Regression analyses were used to identify the independent value for predicting the prognosis of IPF patients. What’s more, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were conducted to unveil the potential biological pathways. CIBERSORT algorithms were performed to calculate the correlation between the risk score and immune cells infiltrating levels.Results: Two hundred thirty three differentially expressed genes were calculated as the hub genes in COVID-19. Fourteen of these genes were identified as the prognostic differentially expressed genes in IPF. Three (MET, UCHL1, and IGF1) of the fourteen genes were chosen to construct the risk signature. The risk signature can greatly predict the prognosis of high-risk and low-risk groups based on the calculated risk score. The functional pathway enrichment analysis and immune infiltrating analysis showed that the risk signature may regulate the immune-related pathways and immune cells.Conclusion: We identified prognostic differentially expressed hub genes related to COVID-19 in IPF. A risk signature was constructed based on those genes and showed great value for predicting the prognosis in IPF patients. What’s more, three genes in the risk signature may be clinically valuable as potential targets for treating IPF patients and IPF patients with COVID-19.

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“…It results in inflammation, fibrosis, lung damage, and edema. Hyper-inflammation by SARS-CoV-2 induces fibroblast activation, increased extracellular matrix, and collagen deposition leading to PF, similar to the features of interstitial lung disease (ILD) [ 13 , 14 ]. It is known that risk factors for PF include old age, comorbidities, ICU admission, and MV [ 7 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Usefulness of KL-6 for Predicting Clinical Outcomes in Hospitalized COVID-19 Patients

et al. 2022

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Background: Krebs von den Lungen 6 (KL-6) is a novel biomarker for interstitial lung disease, and it reflects acute lung injury. We explored the usefulness of KL-6 to predict clinical outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients. Methods: In a total of 48 hospitalized COVID-19 patients, KL-6 levels were measured using the HISCL KL-6 assay (Sysmex, Kobe, Japan) with the HISCL 5000 automated analyzer (Sysmex). Clinical outcomes (intensive care unit [ICU] admission, ventilator use, extracorporeal membrane oxygenation [ECMO] use, and 30-day mortality) were analyzed according to KL-6 percentiles. Age, initial KL-6 level, Charlson comorbidity index (CCI), and critical disease were compared using the receiver operating characteristic (ROC) curve and Kaplan–Meier methods for clinical outcomes. Results: KL-6 quartiles were associated with ICU admission, ventilator use, and ECMO use (all p < 0.05), except 30-day mortality (p = 0.187). On ROC curve analysis, initial KL-6 level predicted ICU admission, ventilator use, and ECMO use significantly better than age, CCI, and critical disease (all p < 0.05); age, initial KL-6 level, CCI, and critical disease predicted 30-day mortality comparably. On Kaplan–Meier survival analysis, hazard ratios (95% confidence interval) were 4.8 (1.2–19.3) for age, 4.7 (1.1–21.6) for initial KL-6 level, 3.9 (0.9–16.2) for CCI, and 2.1 (0.5–10.3) for critical disease. Conclusions: This study demonstrated that KL-6 could be a useful biomarker to predict clinical outcomes in hospitalized COVID-19 patients. KL-6 may contribute to identifying COVID-19 patients requiring critical care, including ICU admission and ventilator and/or ECMO use.

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50-gene risk profiles in peripheral blood predict COVID-19 outcomes: A retrospective, multicenter cohort study

Cited by 23 publications

References 31 publications

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19

A novel gene signature based on the hub genes of COVID-19 predicts the prognosis of idiopathic pulmonary fibrosis

Usefulness of KL-6 for Predicting Clinical Outcomes in Hospitalized COVID-19 Patients

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