Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosisopen access
- Authors
- Nguyen Phuoc Long; Nguyen Ky Anh; Nguyen Thi Hai Yen; Nguyen Ky Phat; Park, Seongoh; Vo Thuy Anh Thu; Cho, Yong-Soon; Shin, Jae-Gook; Oh, Jee Youn; Kim, Dong Hyun
- Issue Date
- 4-8월-2022
- Publisher
- NATURE PORTFOLIO
- Citation
- SCIENTIFIC REPORTS, v.12, no.1
- Indexed
- SCIE
SCOPUS
- Journal Title
- SCIENTIFIC REPORTS
- Volume
- 12
- Number
- 1
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/145840
- DOI
- 10.1038/s41598-022-17521-4
- ISSN
- 2045-2322
- Abstract
- Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O-) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865-0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.
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