MEK reduces cancer-specific PpIX accumulation through the RSK-ABCB1 and HIF-1α-FECH axes
Bibliographic record
Abstract
Abstract The efficacy of aminolevulinic acid (5-ALA)-based photodynamic diagnosis (5-ALA-PDD) and photodynamic therapy (5-ALA-PDT) is dependent on the 5-ALA-induced cancer-specific accumulation of protoporphyrin IX (PpIX). We previously reported that inhibition of oncogenic Ras/MEK increases PpIX accumulation in cancer cells by reducing PpIX efflux through ATP-binding cassette sub-family B member 1 (ABCB1) as well as PpIX conversion to heme by ferrochelatase (FECH). Here, we sought to identify the downstream pathways of Ras/MEK involved in the regulation of PpIX accumulation via ABCB1 and FECH. First, we demonstrated that Ras/MEK activation reduced PpIX accumulation in RasV12-transformed NIH3T3 cells and HRAS transgenic mice. Knockdown of p90 ribosomal S6 kinases (RSK) 2, 3, or 4 increased PpIX accumulation in the RasV12-transformed NIH3T3 cells. Further, treatment with an RSK inhibitor reduced ABCB1 expression and increased PpIX accumulation. Moreover, HIF-1α expression was reduced when the RasV12-transformed NIH3T3 cells were treated with a MEK inhibitor, demonstrating that HIF-1α is a downstream element of MEK. HIF-1α inhibition decreased the activity of FECH and increased PpIX accumulation. Finally, we demonstrated the involvement of RSKs and HIF-1α in the regulation of PpIX accumulation in human cancer cell lines (DLD-1, SNB-75, Hs 578T, and MDA MB 231). These results demonstrate that the RSK-ABCB1 and HIF-1α-FECH axes are the downstream pathways of Ras/MEK involved in the regulation of PpIX accumulation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".