期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 6, 页码 3246-3254出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3107874
关键词
Biomarkers; Immune system; Logistics; Proteins; Biological system modeling; Bioinformatics; Standards; Sepsis; transcriptome; signature; immune genes; diagnosis
类别
资金
- Basic and Applied Basic Research Programs Foundation of Guangdong Province [2019A1515110097]
High-throughput sequencing has enabled the identification of diagnostic biomarkers for sepsis, using a novel method called Recurrent Logistic Regression. This method identified a panel of five immune-related genes that can accurately distinguish sepsis patients from normal controls. These findings have potential implications for clinical diagnostic tests and biological mechanistic studies.
High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies.
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