Metabolic Plasticity in Prostate Cancer
Bibliographic record
Abstract
OBJECTIVES: This paper examines the life and research of Otto Warburg (1883 to 1970), who identified the so-called Warburg effect. Warburg personal life and scientific career were notable. METHODS: This study summarizes the key aspects of his life, the Warburg effect, and its significance in prostate cancer. RESULTS: Despite being classified as non-Aryan, Warburg continued his research as the director of the Kaiser Wilhelm Institute for Cell Physiology during World War II. He also cohabited openly with a male partner. The Warburg effect is a metabolic hallmark of cancer, where cells preferentially utilize glycolysis over oxidative phosphorylation, even in the presence of oxygen. This metabolic shift confers key advantages to tumor survival, including rapid ATP production, biosynthetic support for proliferation, and resistance to apoptosis. In prostate cancer, the metabolism undergoes a unique transformation. Normal prostate cells are characterized by citrate secretion; however, as malignancy develops, the cells adapt to oxidative metabolism. At the metastatic stage, the Warburg effect becomes more pronounced and is influenced by the tumor microenvironment and interactions with cancer-associated fibroblasts and bone marrow adipocytes. These metabolic changes have significant clinical implications. While FDG-PET scans serve as a diagnostic tool in many cancers, their utility in early-stage prostate cancer is limited owing to its delayed metabolic shift. Metabolic-targeted therapies, such as dichloroacetate (DCA) and glycolysis inhibitors, are emerging as promising strategies to enhance the efficacy of chemotherapy and radiotherapy. CONCLUSIONS: Elucidating the role of metabolic reprogramming in prostate cancer could reveal new avenues for treatment, particularly for castration-resistant and metastatic diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".