Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features

Autor: Andrea R. Daamen, Prathyusha Bachali, Amrie C. Grammer, Peter E. Lipsky
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: International Journal of Molecular Sciences
Volume 24
Issue 5
Pages: 4905
ISSN: 1422-0067
DOI: 10.3390/ijms24054905
Popis: The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.
Databáze: OpenAIRE
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