Integrins and Cell Metabolism: An Intimate Relationship Impacting Cancer
Bibliographic record
Abstract
Integrins are important regulators of cell survival, proliferation, adhesion and migration. Once activated, integrins establish a regulated link between the extracellular matrix and the cytoskeleton. Integrins have well-established functions in cancer, such as in controlling cell survival by engagement of many specific intracellular signaling pathways and in facilitating metastasis. Integrins and associated proteins are regulated by control of transcription, membrane traffic, and degradation, as well as by a number of post-translational modifications including glycosylation, allowing integrin function to be modulated to conform to various cellular needs and environmental conditions. In this review, we examine the control of integrin function by cell metabolism, and the impact of this regulation in cancer. Within this context, nutrient sufficiency or deprivation is sensed by a number of metabolic signaling pathways such as AMP-activated protein kinase (AMPK), mammalian target of rapamycin (mTOR) and hypoxia-inducible factor (HIF) 1, which collectively control integrin function by a number of mechanisms. Moreover, metabolic flux through specific pathways also controls integrins, such as by control of integrin glycosylation, thus impacting integrin-dependent cell adhesion and migration. Integrins also control various metabolic signals and pathways, establishing the reciprocity of this regulation. As cancer cells exhibit substantial changes in metabolism, such as a shift to aerobic glycolysis, enhanced glucose utilization and a heightened dependence on specific amino acids, the reciprocal regulation of integrins and metabolism may provide important clues for more effective treatment of various 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".