Epidermal Growth Factor Receptor Regulates Aberrant Expression of Insulin-Like Growth Factor-Binding Protein 3
Bibliographic record
Abstract
Epidermal growth factor receptor (EGFR) is frequently overexpressed in esophageal carcinoma and its precursor lesions. To gain insights into how EGFR overexpression affects cellular functions in primary human esophageal cells, we performed gene expression profiling and identified insulin-like growth factor-binding protein (IGFBP)-3 as the most up-regulated gene. IGFBP-3 regulates cell proliferation through both insulin-like growth factor-dependent and independent mechanisms. We found that IGFBP-3 mRNA and protein expression was increased in EGFR-overexpressing primary and immortalized human esophageal cells. IGFBP-3 was also up-regulated in EGFR-overexpressing cells in organotypic culture and in EGFR transgenic mice. Furthermore, IGFBP-3 mRNA was overexpressed in 80% of primary esophageal squamous cell carcinomas and 60% of primary esophageal adenocarcinomas. Concomitant up-regulation of EGFR and IGFBP-3 was observed in 60% of primary esophageal squamous cell carcinomas. Immunohistochemistry revealed cytoplasmic localization of IGFBP-3 in the preponderance of preneoplastic and neoplastic esophageal lesions. IGFBP-3 was also overexpressed in esophageal cancer cell lines at both mRNA (60%) and protein (40%) levels. IGFBP-3 secreted by cancer cells was capable of binding to insulin-like growth factor I. Functionally, epidermal growth factor appeared to regulate IGFBP-3 expression in esophageal cancer cell lines. Finally, suppression of IGFBP-3 by small interfering RNA augmented cell proliferation, suggesting that IGFBP-3 may inhibit tumor cell proliferation as a negative feedback mechanism. In aggregate, we have identified for the first time that IGFBP-3 is an aberrantly regulated gene through the EGFR signaling pathway and it may modulate EGFR effects during carcinogenesis.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".