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Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

1
College of Pharmacy, Seoul National University, Seoul 08826, Korea
2
College of Pharmacy, Ewha Womans University, Seoul 03760, Korea
3
Department of Biomedical Sciences, College of Medicine, Inha University, Incheon 22212, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Metabolites 2019, 9(10), 199; https://doi.org/10.3390/metabo9100199
Received: 13 August 2019 / Revised: 9 September 2019 / Accepted: 17 September 2019 / Published: 21 September 2019
(This article belongs to the Special Issue Metabolomics Analysis for Biomarker Discovery and Human Health)
Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference. View Full-Text
Keywords: steroidomics; cancer; biomarker; diagnosis; prognosis; systematic review; functional analysis steroidomics; cancer; biomarker; diagnosis; prognosis; systematic review; functional analysis
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MDPI and ACS Style

Anh, N.H.; Long, N.P.; Kim, S.J.; Min, J.E.; Yoon, S.J.; Kim, H.M.; Yang, E.; Hwang, E.S.; Park, J.H.; Hong, S.-S.; Kwon, S.W. Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis. Metabolites 2019, 9, 199.

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