<i>N</i>‐Acylethanolamine acid amidase (NAAA) is dysregulated in colorectal cancer patients and its inhibition reduces experimental cancer growth
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: N-Acylethanolamine acid amidase (NAAA) is a lysosomal enzyme accountable for the breakdown of N-acylethanolamines (NAEs) and its pharmacological inhibition has beneficial effects in inflammatory conditions. The knowledge of NAAA in cancer is fragmentary with an unclarified mechanism, whereas its contribution to colorectal cancer (CRC) is unknown to date. EXPERIMENTAL APPROACH: CRC xenograft and azoxymethane models were used to assess the in vivo effect of NAAA inhibition. Further, the tumour secretome was evaluated by an oncogenic array, CRC cell lines were used for in vitro studies, cell cycle was analysed by cytofluorimetry, NAAA was knocked down with siRNA, human biopsies were obtained from surgically resected CRC patients, gene expression was measured by RT-PCR and NAEs were measured by LC-MS. KEY RESULTS: The NAAA inhibitor AM9053 reduced CRC xenograft tumour growth and counteracted tumour development in the azoxymethane model. NAAA inhibition affected the composition of the tumour secretome inhibiting the expression of EGF family members. In CRC cells, AM9053 reduced proliferation with a mechanism mediated by PPAR-α and TRPV1. AM9053 induced cell cycle arrest in the S phase associated with cyclin A2/CDK2 down-regulation. NAAA knock-down mirrored the effects of NAAA inhibition with AM9053. NAAA expression was down-regulated in human CRC tissues, with a consequential augmentation of NAE levels and dysregulation of some of their targets. CONCLUSION AND IMPLICATIONS: Our results show novel data on the functional importance of NAAA in CRC progression and the mechanism involved. We propose that this enzyme is a valid drug target for the treatment of CRC growth and development.
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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) | 0.000 | 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 | 0.000 |
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".