Identification of iron metabolism-related genes as diagnostic signatures in sepsis by blood transcriptomic analysis
Abstract: Iron metabolism is considered to play the principal role in sepsis, but the key iron metabolism-related genetic signatures are unclear. In this study, we analyzed and identified the genetic signatures related to the iron-metabolism in sepsis by using a bioinformatics analysis of four transcriptomic datasets from the GEO database. A total of 21 differentially expressed iron metabolism-related signatures were identified including 9 transporters, 8 enzymes, and 4 regulatory factors. Among them, lipocalin 2 was found to have the highest diagnostic value as its expression showed significant differences in all the comparisons including sepsis vs healthy controls, sepsis vs non-sepsis diseases, and mild forms vs severe forms of sepsis. Besides, the cytochrome P450 gene CYP1B1 also showed diagnostic values for sepsis from the non-sepsis diseases. The CYP4V2, LTF, and GCLM showed diagnostic values for distinguishing the severe forms from mild forms of sepsis. Our analysis identified 21 sepsis-associated iron metabolism-related genetic signatures, which may represent diagnostic and therapeutic biomarkers of sepsis, and will improve our understanding of the molecular mechanism underlying the occurrence of sepsis.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Identification of iron metabolism-related genes as diagnostic signatures in sepsis by blood transcriptomic analysis ; volume:18 ; number:1 ; year:2023 ; extent:14
Open life sciences ; 18, Heft 1 (2023) (gesamt 14)
- Creator
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Li, Huijun
Wang, Xu
Yang, Qing
Cheng, Liming
Zeng, Hao-Long
- DOI
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10.1515/biol-2022-0549
- URN
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urn:nbn:de:101:1-2023021013025180077461
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:02 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Li, Huijun
- Wang, Xu
- Yang, Qing
- Cheng, Liming
- Zeng, Hao-Long
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