Bibliographic record
Abstract
Metabolic reprogramming is recognized as an essential hallmark in carcinogenesis. By investigating the cancer-specific alterations in metabolism, several common cancer phenotypes, such as accumulated somatic mutations due to gene instability, irregulated nutrient consumption, uncontrolled growth and proliferation, and aberrational mitochondrial activities, becomes the interest of study. In this article, the overall profile of cancer metabolic activities including glucose and glutamine metabolism, macromolecules synthesis, aerobic glycolysis, pentose phosphate pathways, and mitochondrial activity, as well as two important signaling pathways (PI3K/AKT/mTOR and p53) regarding cancer metabolism are discussed. During cancer progression, the proto-oncogenes are amplified, and the tumor suppressor genes are repressed due to gene instability when cancer over-proliferated. The epigenetic changes affecting cellular signaling pathways and then triggering alterations in biosynthesis and bioenergetics to support cancer growth and proliferation with sufficient building blocks and energy. The article aims to give an overview of those cancer-associated metabolisms and show a profile of cancer-related metabolites and mutated enzymes. It also highlights the interconnections between metabolic activities, the interactions between signaling pathways and cancerous metabolism, and oncometabolites and aberrational enzymes that could potentially promote carcinogenesis; hence, become therapeutic targets for treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".