Identification of early and intermediate biomarkers for ARDS mortality by multi-omic approaches
Abstract The lack of successful clinical trials in acute respiratory distress syndrome (ARDS) has highlighted the unmet need for biomarkers predicting ARDS mortality and for novel therapeutics to reduce ARDS mortality. We utilized a systems biology multi-“omics” approach to identify predictive bioma...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/8dbbd029da3342b6ac531298d917c949 |
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| Sumario: | Abstract The lack of successful clinical trials in acute respiratory distress syndrome (ARDS) has highlighted the unmet need for biomarkers predicting ARDS mortality and for novel therapeutics to reduce ARDS mortality. We utilized a systems biology multi-“omics” approach to identify predictive biomarkers for ARDS mortality. Integrating analyses were designed to differentiate ARDS non-survivors and survivors (568 subjects, 27% overall 28-day mortality) using datasets derived from multiple ‘omics’ studies in a multi-institution ARDS cohort (54% European descent, 40% African descent). ‘Omics’ data was available for each subject and included genome-wide association studies (GWAS, n = 297), RNA sequencing (n = 93), DNA methylation data (n = 61), and selective proteomic network analysis (n = 240). Integration of available “omic” data identified a 9-gene set (TNPO1, NUP214, HDAC1, HNRNPA1, GATAD2A, FOSB, DDX17, PHF20, CREBBP) that differentiated ARDS survivors/non-survivors, results that were validated utilizing a longitudinal transcription dataset. Pathway analysis identified TP53-, HDAC1-, TGF-β-, and IL-6-signaling pathways to be associated with ARDS mortality. Predictive biomarker discovery identified transcription levels of the 9-gene set (AUC-0.83) and Day 7 angiopoietin 2 protein levels as potential candidate predictors of ARDS mortality (AUC-0.70). These results underscore the value of utilizing integrated “multi-omics” approaches in underpowered datasets from racially diverse ARDS subjects. |
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