Deregulation of the IGF axis in cancer: epidemiological evidence and potential therapeutic interventions.
Bibliographic record
Abstract
The IGF system performs a fundamental role in the regulation of cellular proliferation, differentiation and apoptosis. These diverse biological actions are mediated primarily by IGF association with the type I IGF receptor (IGF-IR), which is in turn regulated by a group of high-affinity IGF-binding proteins (IGFBP-1 to -6). All of the IGFBPs can have growth-inhibitory effects by competitively binding IGFs and preventing their association with the IGF-IR. IGFBP-3 is the most abundant binding protein in the circulation and controls the actions of the IGFs by regulating their distribution and bioavailability to target tissues. Disruptions in the balance of IGF system components leading to excessive proliferation and survival signals have been implicated in the development of different tumor types. Epidemiological evidence indicates that increased levels of IGF-I, reduced levels of IGFBP-3 or an increased ratio of IGF-I to IGFBP-3 in the circulation are associated with an increased risk for the development of several common cancers, including those of the breast, prostate, lung and colon. The results of preclinical studies indicate that a diversity of interventions which antagonize IGF-IR signaling or augment IGFBP-3 function inhibit tumor cell growth in models of human cancers. A more comprehensive understanding of the interplay between cellular targets of the IGF system and antineoplastic agents will facilitate the development of novel strategies for the prevention and treatment of cancer.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".