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Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.

ABSTRACT: One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set (GSE111368) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set (GSE101702); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.

SUBMITTER: Liu S

PROVIDER: S-EPMC7875920 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.

Liu Shuai S Huang Zhisheng Z Deng Xiaoyan X Zou Xiaohui X Li Hui H Mu Shengrui S Cao Bin B

Journal of cellular and molecular medicine 20210114 3

One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set (GSE111368) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysi ...[more]

PMID: 33448094

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Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.

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Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation.

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