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High-throughput metabolomics identifies new biomarkers for cervical cancer.

Authors :
Li, Xue
Zhang, Liyi
Huang, Xuan
Peng, Qi
Zhang, Shoutao
Tang, Jiangming
Wang, Jing
Gui, Dingqing
Zeng, Fanxin
Source :
Discover Oncology; 3/29/2024, Vol. 15 Issue 1, p1-11, 11p
Publication Year :
2024

Abstract

Background: Cervical cancer (CC) is a danger to women's health, especially in many developing countries. Metabolomics can make the connection between genotypes and phenotypes. It provides a wide spectrum profile of biological processes under pathological or physiological conditions. Method: In this study, we conducted plasma metabolomics of healthy volunteers and CC patients and integratively analyzed them with public CC tissue transcriptomics from Gene Expression Omnibus (GEO). Result: Here, we screened out a panel of 5 metabolites to precisely distinguish CC patients from healthy volunteers. Furthermore, we utilized multi-omics approaches to explore patients with stage I-IIA1 and IIA2-IV4 CC and comprehensively analyzed the dysregulation of genes and metabolites in CC progression. We identified that plasma levels of trimethylamine N-oxide (TMAO) were associated with tumor size and regarded as a risk factor for CC. Moreover, we demonstrated that TMAO could promote HeLa cell proliferation in vitro. In this study, we delineated metabolic profiling in healthy volunteers and CC patients and revealed that TMAO was a potential biomarker to discriminate between I-IIA1 and IIA2-IV patients to indicate CC deterioration. Conclusion: Our study identified a diagnostic model consisting of five metabolites in plasma that can effectively distinguish CC from healthy volunteers. Furthermore, we proposed that TMAO was associated with CC progression and might serve as a potential non-invasive biomarker to predict CC substage. Impact: These findings provided evidence of the important role of metabolic molecules in the progression of cervical cancer disease, as well as their ability as potential biomarkers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
27306011
Volume :
15
Issue :
1
Database :
Complementary Index
Journal :
Discover Oncology
Publication Type :
Academic Journal
Accession number :
176339207
Full Text :
https://doi.org/10.1007/s12672-024-00948-8