Abstract
Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor–host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.
Highlights
Ovarian cancer (OC) is the third most common gynecologic malignancy worldwide and the fifth cause of cancer death among women [1], with a number of diagnosed new cases equal to 300,000 in2018 [2]
The significance of the selected metabolomic variables as diagnostic biomarkers needs to be confirmed by external validation with an independent set of samples [31], and by the investigation of the biochemical mechanisms associated with the identified metabolic biomarker [33]
Central carbon metabolism involves biochemical pathways encompassing the glycolysis, tricarboxylic acid (TCA) cycle and pentose phosphate pathway, which serve to convert glucose into metabolic precursors. Such a series of metabolic reactions is profoundly deregulated in cancers; this is linked to the well-known Warburg effect, that consists of the shift from the TCA cycle to aerobic glycolysis for energy production [81,92,93]
Summary
Ovarian cancer (OC) is the third most common gynecologic malignancy worldwide and the fifth cause of cancer death among women [1], with a number of diagnosed new cases equal to 300,000 in. The metabolomics profile, unlike the genomics profile, reflects the biochemical events that occur in the organism as a result of the complex interactions among age, sex, gene transcription, protein expression, physio-pathological conditions including gut microbiome activity and environmental effects It offers a closer description of the patient’s disease phenotype, which is useful for diagnostic purposes, and to understand the clinical outcome variability that is at the basis of precision medicine [12,13]. Metabolomics can reveal specific host metabolic alterations induced by the cancer, mainly to sustain its growth [14] These alterations can be detected in biological fluids such as blood, plasma, urine and ascites, and knowledge of these systemic changes is considered a valid approach to discover new diagnostic and prognostic biomarkers [15]. The most critical steps of metabolomics workflow are analyzed in an attempt to explain the potential pitfalls of metabolomics-based biomarkers research that often generate inconsistent results among studies and limit the effective translation of these findings to a clinical setting
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