Bidirectional Crosstalk between Leptin and Insulin-like Growth Factor-I Signaling Promotes Invasion and Migration of Breast Cancer Cells via Transactivation of Epidermal Growth Factor Receptor
Bibliographic record
Abstract
Obesity is an independent risk factor for breast cancer, and obese breast cancer patients exhibit a higher risk for larger tumor burden and increased metastasis. Obesity, as associated with metabolic syndrome, results in an increase in circulating insulin-like growth factor (IGF), which acts as a mitogen. In addition, higher plasma level of adipocytokine leptin is associated with obesity. In the present study, we show that cotreatment with leptin and IGF-I significantly increases proliferation as well as invasion and migration of breast cancer cells. We found a novel bidirectional crosstalk between leptin and IGF-I signaling; IGF-I induced phosphorylation of leptin receptor (Ob-Rb) and leptin induced phosphorylation of IGF-I receptor (IGF-IR), whereas cotreatment induced synergistic phosphorylation and association of Ob-Rb and IGF-IR along with activation of downstream effectors, Akt and extracellular signal-regulated kinase. Leptin increased phosphorylation of IGF signaling molecules insulin-receptor substrate (IRS)-1 and IRS-2. Interestingly, we found that leptin and IGF-I cotreatment synergistically transactivated epidermal growth factor receptor (EGFR), depending on the proteolytic release of EGFR ligands, as the broad-spectrum matrix metalloproteinase inhibitor GM6001 could inhibit this effect. Using clinically relevant EGFR inhibitors, erlotinib and lapatinib, we found that inhibition of EGFR activation effectively inhibited leptin- and IGF-I-induced invasion and migration of breast cancer cells. Taken together, these data suggest a novel bidirectional crosstalk between leptin and IGF-I signaling that transactivates EGFR and promotes the metastatic properties as well as invasion and migration of breast cancer cells. Our findings indicate the possibility of using EGFR inhibitors erlotinib and lapatinib to counter the procancerous effects of leptin and IGF-I in breast 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) | 0.000 | 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 | 0.000 |
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".