Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Analytical chemistry (Washington) Ročník 96; číslo 12; s. 4745
Hlavní autoři: Tang, Jing, Mou, Minjie, Zheng, Xin, Yan, Jin, Pan, Ziqi, Zhang, Jinsong, Li, Bo, Yang, Qingxia, Wang, Yunxia, Zhang, Ying, Gao, Jianqing, Li, Song, Yang, Hui, Zhu, Feng
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 26.03.2024
ISSN:1520-6882, 1520-6882
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's -test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1520-6882
1520-6882
DOI:10.1021/acs.analchem.3c03796