SynthesisInternational journal of molecular sciences2023
The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review.
Synthesis in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 6 citations in OpenAlex.
- Unveiling the association between plasma lipid species and pan-cancers: A bidirectional 2-sample Mendelian randomization study.Medicine · 2026Article
- Multi-Omics Profiling in a Symptomatic Cohort Identifies Coordinated Biomarker Signatures in Ovarian Cancer Serum.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical Evaluation of a Multi-Omic Diagnostic Model for Early-Stage Ovarian Cancer Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population.Cancer research communications · 2025Article
- The strategic role of lipidomics in biomarker identification and diagnosis of gynecological diseases.Frontiers in endocrinology · 2025Review
- Is high-density lipoprotein cholesterol a prognostic marker in epithelial ovarian cancer?Revista da Associacao Medica Brasileira (1992) · 2025Observational
- Delineating lipidomic landscapes in human and mouse ovaries: Spatial signatures and chemically-induced alterations via MALDI mass spectrometry imaging: Spatial ovarian lipidomics.Environment international · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.