The application of metabolomics in ovarian cancer management: A systematic review Original paper
Researched by:
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Divine Aleru
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Divine Aleru
Read MoreI am a biochemist with a deep curiosity for the human microbiome and how it shapes human health, and I enjoy making microbiome science more accessible through research and writing. With 2 years experience in microbiome research, I have curated microbiome studies, analyzed microbial signatures, and now focus on interventions as a Microbiome Signatures and Interventions Research Coordinator.
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Women’s Health
Women’s Health
Women’s health, a vital aspect of medical science, encompasses various conditions unique to women’s physiological makeup. Historically, women were often excluded from clinical research, leading to a gap in understanding the intricacies of women’s health needs. However, recent advancements have highlighted the significant role that the microbiome plays in these conditions, offering new insights and potential therapies. MicrobiomeSignatures.com is at the forefront of exploring the microbiome signature of each of these conditions to unravel the etiology of these diseases and develop targeted microbiome therapies.
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Ovarian Cancer
Ovarian Cancer
OverviewOvarian cancer (OC) remains one of the most deadly cancers affecting women, with an estimated 320,000 new cases diagnosed worldwide annually, making it the eighth most commonly diagnosed cancer. It also accounts for over 200,000 deaths each year, reflecting its high lethality. The disease is often diagnosed at advanced stages (stage III and IV) due […]
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Divine Aleru
Read More
Researched by:
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Divine Aleru
ID
Divine Aleru
Read More
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Divine Aleru
What was studied?
This systematic review focused on the application of metabolomics in ovarian cancer management, exploring how metabolomic analysis can be used for diagnosing, prognosing, and monitoring the treatment and recurrence of ovarian cancer. The review compiled and evaluated findings from 32 global studies that applied metabolomics to biofluids and tumor tissues in ovarian cancer patients. The review also assessed 18 targeted metabolomics studies for further validation. It aimed to highlight metabolite profiles and metabolic pathways that could potentially be used as clinical biomarkers for ovarian cancer detection and management.
Who was studied?
The review analyzed studies involving ovarian cancer patients, including those with both primary and recurrent cancers. The included studies had participants from diverse regions such as China, the USA, the UK, and Poland, totaling 3,634 participants. Of these, 1,724 had ovarian cancer, and 1,910 served as controls or had benign ovarian conditions. The studies employed various metabolomic techniques, including liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR), to analyze biofluids like serum and urine, as well as tumor tissues from ovarian cancer patients.
Most important findings
The review found significant alterations in metabolites linked to ovarian cancer, especially phospholipids and amino acids. The most commonly reported findings were a down-regulation of lysophosphatidylcholine (LPC) and phosphatidylcholine (PC), and up-regulation of lysophosphatidylethanolamine (LPE) and ceramides. Amino acids such as histidine, citrulline, alanine, and methionine were also found to be down-regulated in ovarian cancer tissues. In addition, the combination of multiple metabolites in diagnostic panels showed improved sensitivity and specificity for diagnosing ovarian cancer, with lipid metabolism being a key area of focus. Dysregulated pathways, such as glutamine metabolism, were also linked to the aggressive nature of ovarian cancer, offering insight into prognosis and recurrence.
Key implications
The findings suggest that metabolomics has significant potential as a diagnostic and prognostic tool for ovarian cancer. The identification of specific metabolic signatures, particularly in phospholipid and amino acid metabolism, can aid in the early detection of ovarian cancer, which is typically diagnosed at an advanced stage. Furthermore, combining multiple metabolites into diagnostic panels could increase diagnostic accuracy, making it a promising approach for clinical use. However, further research is needed to validate these findings in larger and more diverse populations, as well as to integrate metabolomics with existing clinical biomarkers for better patient management.