Bibliographic record
Abstract
Cancer cells undergo metabolic reprogramming to adapt to the harsh tumor microenvironment, allowing for their rapid proliferation and high survival rates. Metabolic reprogramming is one of the top ten characteristics of tumors and plays a key role in promoting tumorigenesis and progression. Glucose metabolism disorders are the most representative metabolic features. Unlike normal cells that rely on oxidative phosphorylation under aerobic conditions, cancer cells predominantly utilize aerobic glycolysis, a phenomenon known as the Warburg effect. This shift in metabolism in cancer cells not only enhances ATP production but also generates metabolites like lactate that may lead to an acidic tumor microenvironment (TME), which could further promote the proliferation and invasion of cancer cells. Metabolites like lactate, glutamine, arginine, tryptophan, and cholesterol not only provide energy and build blocks for cancer cells but also reprogram immune cell responses, facilitating immune evasion. This review explores the complex interactions between cancer cell metabolism and the immune system, highlighting how metabolites of cancer cells modulate immune responses and contribute to cancer progression. Understanding these interactions can provide insights into potential therapeutic strategies that target both cancer metabolism and immune evasion mechanisms.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".