Evidence map›Paper›PMID 39969135›Full record

ReviewCancer medicine2025

Navigating Metabolic Challenges in Ovarian Cancer: Insights and Innovations in Drug Repurposing.

Sara Mikhael, Georges Daoud

Abstract readReview
In one paragraph

Review in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sara MikhaelDepartment of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.ORCID https://orcid.org/0000-0003-0350-7694
Georges DaoudDepartment of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.ORCID https://orcid.org/0000-0001-8043-3882

Funding

American University of Beirut
6 · The paper itself

Abstract

backgroundOvarian cancer (OC) is the most lethal gynecological malignancy and a major global health concern, often diagnosed at advanced stages with poor survival rates. Despite advancements in treatment, resistance to standard chemotherapy remains a critical challenge with limited treatment options available. In recent years, the role of metabolic reprogramming in OC has emerged as a key factor driving tumor progression, therapy resistance, and poor clinical outcomes.

methodsThis review explores the intricate connections between metabolic syndrome, enhanced glycolysis, and altered lipid metabolism within OC cells, which fuel the aggressive nature of the disease. We discuss how metabolic pathways are rewired in OC to support uncontrolled cell proliferation, survival under hypoxic conditions, and evasion of cell death mechanisms, positioning metabolic alterations as central to disease progression. The review also highlights the potential of repurposed metabolic-targeting drugs, such as metformin and statins, which have shown promise in preclinical studies for their ability to disrupt these altered metabolic pathways.

conclusionDrug repurposing offers a promising strategy to overcome chemoresistance and improve patient outcomes. Future research should focus on unraveling the complex metabolic networks in OC to develop innovative, targeted therapies that can enhance treatment efficacy and patient survival.

Indexed as

Antineoplastic AgentsDrug RepositioningOvarian NeoplasmsDrug Resistance, NeoplasmFemaleGlycolysisHumansLipid MetabolismMetabolic Networks and PathwaysMetforminAntineoplastic AgentsMetformindrug repurposing strategymetabolic reprogrammingmetabolic syndromeovarian cancer

Identifiers

PMID39969135
PMCPMC11837049

What OpenQuestion holds

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Registered trials

None linked

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 OpenQuestion graph.