Insulin Enhances Migration and Invasion in Prostate Cancer Cells by Up-Regulation of FOXC2
Bibliographic record
Abstract
Androgen deprivation therapy (ADT) is the standard treatment for advanced prostate cancer (PCa), yet many patients relapse with lethal metastatic disease. With this loss of androgens, increased cell plasticity has been observed as an adaptive response to ADT. This includes gain of invasive and migratory capabilities, which may contribute to PCa metastasis. Hyperinsulinemia, which develops as a side-effect of ADT, has been associated with increased tumor aggressiveness and faster treatment failure. We investigated the direct effects of insulin in PCa cells that may contribute to this progression. We measured cell migration and invasion induced by insulin using wound healing and transwell assays in a range of PCa cell lines of variable androgen dependency (LNCaP, 22RV1, DuCaP, and DU145 cell lines). To determine the molecular events driving insulin-induced invasion we used transcriptomics, quantitative real time-PCR, and immunoblotting in three PCa cell lines. Insulin increased invasiveness of PCa cells, upregulating Forkhead Box Protein C2 (FOXC2), and activating key PCa cell plasticity mechanisms including gene changes consistent with epithelial-to-mesenchymal transition (EMT) and a neuroendocrine phenotype. Additionally, analysis of publicly available clinical PCa tumor data showed metastatic prostate tumors demonstrate a positive correlation between insulin receptor expression and the EMT transcription factor FOXC2. The insulin receptor is not suitable to target clinically however, our data shows that actions of insulin in PCa cells may be suppressed by inhibiting downstream signaling molecules, PI3K and ERK1/2. This study identifies for the first time, a mechanism for insulin-driven cancer cell motility and supports the concept that targeting insulin signaling at the level of the PCa tumor may extend the therapeutic efficacy of ADT.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".