The Role of the IGF System in Cancer Growth and Metastasis: Overview and Recent Insights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IGF-I receptor (IGF-IR) signaling and functions are mediated through the activities of a complex molecular network of positive (e.g., type I IGF) and negative (e.g., the type II IGF receptor, IGF-IIR) effectors. Under normal physiological conditions, the balance between the expression and activities of these molecules is tightly controlled. Changes in this delicate balance (e.g., overexpression of one effector) may trigger a cascade of molecular events that can ultimately lead to malignancy. In recent years, evidence has been mounting that the IGF axis may be involved in human cancer progression and can be targeted for therapeutic intervention. Here we review old and more recent evidence on the role the IGF system in malignancy and highlight experimental and clinical studies that provide novel insights into the complex mechanisms that contribute to its oncogenic potential. Controversies arising from conflicting evidence on the relevance of IGF-IR and its ligands to human cancer are discussed. Our review highlights the importance of viewing the IGF axis as a complex multifactorial system and shows that changes in the expression levels of any one component of the axis, in a given malignancy, should be interpreted with caution and viewed in a wider context that takes into account the expression levels, state of activation, accessibility, and functionality of other interacting components. Because IGF targeting for anticancer therapy is rapidly becoming a clinical reality, an understanding of this complexity is timely because it is likely to have an impact on the design, mode of action, and clinical outcomes of newly developed drugs.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it