Evidence map›Paper›PMID 40202553›Full record

ReviewMolecular biology reports2025

The perspective of targeting cancer cell metabolism: combination therapy approaches.

Arezu Karimpur-Zahmatkesh, Mohammad Khalaj-Kondori

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biology reports, 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. A different perspective on the cancer stem cell theory.Medical oncology (Northwood, London, England) · 2026
    Review
  2. Article
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.

Arezu Karimpur-ZahmatkeshDepartment of Animal Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran.
Mohammad Khalaj-KondoriDepartment of Animal Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. khalaj@tabrizu.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer cells are considered the most adaptable for their metabolic status, which supports growth, survival, rapid proliferation, invasiveness, and metastasis in a nutrient-deficient microenvironment. Since the discovery of altered glucose metabolism (aerobic glycolysis), which is generally known as a part of metabolic reprogramming and an innate trait of cancer cells, in 1930 via Dr. Otto Warburg, numerous studies have endeavored to recognize various aspects of cancer cell metabolism and find new methods for efficiently eradicating described cells by targeting their energy metabolism. In this way, the outcomes have mainly been promising. Accordingly, outlining the related results will indeed assist us in making a definitive path for developing targeted therapy strategies based on cancer cell-altered metabolism. The present study reviews the key features of cancer cell metabolism and treatment strategies based on them. It emphasizes the importance of targeting cancer cell dysregulated metabolic pathways that influence the cell energy supply and manage cancer cell growth and survival. This trial also introduces a multimodal therapeutic strategy hypothesis, a potential next-generation combination therapy approach, and suggests interdisciplinary research to recognize the complexities of cancer metabolism and exploit them for designing more efficacious cancer therapeutic strategies.

Indexed as

Energy MetabolismNeoplasmsAnimalsCell ProliferationCombined Modality TherapyGlucoseGlycolysisHumansMolecular Targeted TherapyTumor MicroenvironmentGlucoseCancerCombination therapyMetabolic pathwayMultimodal approachTargeted therapy

Identifiers

What OpenQuestion holds

Textmetadata
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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.