MIA is a potential biomarker for tumour load in neurofibromatosis type 1
Bibliographic record
Abstract
BACKGROUND: Neurofibromatosis type 1 (NF1) is a frequent genetic disease characterized by multiple benign tumours with increased risk for malignancy. There is currently no biomarker for tumour load in NF1 patients. METHODS: In situ hybridization and quantitative real-time polymerase reaction were applied to investigate expression of cartilage-specific genes in mice bearing conditional inactivation of NF1 in the developing limbs. These mice do not develop tumours but recapitulate aspects of NF1 bone dysplasia, including deregulation of cartilage differentiation. It has been recently shown that NF1 tumours require for their growth the master regulator of cartilage differentiation SOX9. We thus hypothesized that some of the cartilage-specific genes deregulated in an Nf1Prx1 mouse model might prove to be relevant biomarkers of NF1 tumours. We tested this hypothesis by analyzing expression of the SOX9 target gene product melanoma-inhibitory activity/cd-rap (MIA) in tumour and serum samples of NF1 patients. RESULTS: Increased expression of Mia was found in Nf1-deficient cartilage in mice. In humans, MIA was expressed in all NF1-related tumours and its serum levels were significantly higher in NF1 patients than in healthy controls. Among NF1 patients, MIA serum levels were significantly higher in those with plexiform neurofibromas and in those with large number of cutaneous (> 1,000) or subcutaneous (> 100) neurofibromas than in patients without such tumours. Most notably, MIA serum levels correlated significantly with internal tumour burden. CONCLUSIONS: MIA is a potential serum biomarker of tumour load in NF1 patients which could be useful in following the disease course and monitoring the efficacy of therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".