Metabolic Reprogramming as a Therapeutic Target in Cancer: A Qualitative Systematic Review (QualSR) of Natural Compounds Modulating Glucose and Glutamine Pathways
Bibliographic record
Abstract
Background: Despite advances in gene-targeted and immunotherapies, many aggressive cancers—including glioblastoma and triple-negative breast cancer—remain refractory to treatment. Mounting evidence implicates metabolic reprogramming, especially dysregulation of glucose and glutamine metabolism, as a core hallmark of tumor progression. Natural compounds with metabolic-modulatory effects have emerged as promising adjuncts in oncology. Research Question and Objectives: This review investigates the following question: How can metabolic-targeted therapies—particularly those modulating the Warburg effect and glutamine metabolism—improve cancer treatment outcomes, and what role do natural compounds play in this strategy? The objectives were to (1) evaluate the therapeutic potential of metabolic interventions targeting glucose and glutamine metabolism, (2) assess natural compounds with metabolic regulatory activity, (3) examine integration of metabolic-targeted therapies with conventional treatments, and (4) identify metabolic vulnerabilities in resistant malignancies. Methods: A qualitative systematic review (QualSR) was conducted following PRISMA guidelines. A total of 87 peer-reviewed studies published between 2000 and 2024 were included. Inclusion criteria required clearly defined mechanistic or clinical endpoints and, for clinical trials, sample sizes ≥ 30. Data extraction focused on tumor response, survival, metabolic modulation, and safety profiles. Results: Curcumin significantly reduced serum TNF-α and IL-6 (both p = 0.001) and improved antioxidant capacity (p = 0.001). EGCG downregulated ERα (p = 0.002) and upregulated tumor suppressors p53 and p21 (p = 0.001, p = 0.02). High-dose intravenous vitamin C combined with chemoradiotherapy yielded a 44.4% pathologic complete response rate in rectal cancer. Berberine suppressed Akt/mTOR signaling and glutamine transporter SLC1A5 across tumor types (q < 10−10). However, poor bioavailability (e.g., EGCG t½ = 3.4 ± 0.3 h) and systemic toxicity limit their standalone clinical application. Conclusions: Metabolic-targeted therapies—particularly natural compounds acting on glucose and glutamine pathways—offer a viable adjunct to standard cancer therapies. Clinical translation will require biomarker-driven patient stratification, improved delivery systems, and combination trials to optimize the therapeutic impact in treatment-resistant cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".