posted on 2024-02-28, 21:03authored byJing Tang, Minjie Mou, Xin Zheng, Jin Yan, Ziqi Pan, Jinsong Zhang, Bo Li, Qingxia Yang, Yunxia Wang, Ying Zhang, Jianqing Gao, Song Li, Hui Yang, Feng Zhu
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 t-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.