Evidence map›Paper›PMID 42582071›Full record

ReviewFrontiers in oncology2026

Host systemic metabolism and cancer metabolic vulnerabilities: mechanisms and therapeutic opportunities.

Rashid Mir, Jameel Barnawi, Naseh A Algehainy, Mohammed M Jalal, Malik A Altayar, Reema M Almotairi, Faris J Tayeb, Mamdoh S Moawadh, Ruqaiah I Bedaiwi, Kholoud S Almasoudi and 3 more

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Rashid MirPrince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia.
Jameel BarnawiDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Naseh A AlgehainyDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Mohammed M JalalDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Malik A AltayarDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Reema M AlmotairiPrince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia.
Faris J TayebDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Mamdoh S MoawadhDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Ruqaiah I BedaiwiDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Kholoud S AlmasoudiDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Ibrahim Altedlawi AlbalawiDepartment of Surgical Oncology, Faculty of Medicine, University of Tabuk, Tabuk, Saudi Arabia.
Abdulrahman AlasmariDepartment of Clinical Biochemistry, College of Medicine, University of Bisha, Bisha, Tabuk, Saudi Arabia.
Mohammad Muzaffar MirDepartment of Clinical Biochemistry, College of Medicine, University of Bisha, Bisha, Tabuk, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Central molecular mediators-including hypoxia-inducible factors (HIF-1α/HIF-2α), MYC, wild-type and mutant p53, NF-κB, STAT3, SREBPs, NRF2, and KRAS-orchestrate these pathways by linking nutrient availability to oncogenic signalling, epigenetic reprogramming, and immune-metabolic crosstalk within the tumour microenvironment. Key metabolic enzymes including HK2, PKM2, LDH-A, IDH1/2, GLS1, and FASN serve as direct effectors and therapeutic targets. Mitochondrial dynamics-biogenesis (PGC-1α), fission (DRP1), fusion (MFN1/2, OPA1), and mitophagy (PINK1-Parkin)-constitute a critical regulatory layer. The bidirectional epigenetic-metabolic axis, mediated by acetyl-CoA, SAM, α-ketoglutarate, 2-hydroxyglutarate, and lysine lactylation, amplifies oncogenic transcriptional programs and locks cells into malignant states. Central to this review is the thesis that metabolic plasticity-the capacity of cancer cells to dynamically switch between and co-opt multiple metabolic programs-is the primary driver of tumour progression, immune evasion, and resistance to therapy. Understanding and targeting this plasticity represents the central translational challenge of cancer metabolic oncology. Methods: A comprehensive narrative literature review was conducted across PubMed, Scopus, and Web of Science (2015-2025) using terms including metabolic reprogramming, Warburg effect, oncometabolites, mitochondrial dynamics, epigenetic metabolism, immunometabolism, and metabolic therapeutics. Peer-reviewed primary research and comprehensive reviews were evaluated. Limitations include restriction to English-language literature (2015-2025), potential publication bias toward high-impact journals, and the rapidly evolving nature of the field. Conclusion: Metabolic reprogramming is governed by an interconnected network of transcription factors, signalling cascades, epigenetic regulators, mitochondrial dynamics, and TME-immune crosstalk. FDA-validated targets include IDH1/2 (ivosidenib, enasidenib, vorasidenib-August 2024), HIF-2α (belzutifan), and mTOR (everolimus). An expanding clinical pipeline encompasses GLS1, MCT1, OXPHOS Complex I, FASN, and metabolic immune checkpoints. Future advances require single-cell/spatial metabolomics, AI-driven patient stratification, and rational combination strategies that preempt adaptive metabolic escape. Future advances require AI-driven genome-scale metabolic modelling for patient stratification, single-cell and spatial metabolomics to resolve intra-tumoral metabolic heterogeneity, and rational combination strategies targeting multiple metabolic nodes simultaneously to preempt adaptive resistance. Integration of circadian pharmacology, host metabolic comorbidity management (obesity, diabetes, gut microbiome modulation), and TME metabolic normalisation into cancer treatment frameworks will drive the next generation of precision metabolic oncology.

Indexed as

cancer therapeuticsepigenetic regulationHIF-1αmetabolic reprogrammingmitochondrial dynamicsoncometabolitestumour microenvironmentwarburg effect

Identifiers

PMID42582071
PMCPMC13457070

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.