Frequency of mutations in BRAF, NRAS, and KIT in different populations and histological subtypes of melanoma: a systemic review
Bibliographic record
Abstract
The presence of mutations of BRAF, NRAS, and KIT genes is recognized as playing a role during carcinogenesis. Our study aims to evaluate and review other studies that present the frequency of mutations of BRAF, NRAS, and KIT genes for different populations, and analyse correlation to their clinical-pathological characteristics and to the demographics of melanoma. Thirty-two articles were selected from a collection of published literature studying 6299 patients. The parameters for correlation to different variables were calculated by odds ratio, for random and single effects. 38.5% of patients present BRAF gene mutations, 16.4% in NRAS, and 10% in KIT. Mutations of the BRAF gene were correlated to superficial spreading melanoma (odds ratio = 1.31), localization in the torso (odds ratio = 1.42) and presence of metastases. Mutations in NRAS were correlated to nodular melanoma (odds ratio = 1.57), localized in the limbs (odds ratio = 1.31). Mutations of the KIT gene were correlated to mucosal melanoma (odds ratio = 1.59). Populations in Brazil, the US, Sweden, Italian, and Australia were found to be correlated to mutations of BRAF and melanoma. Populations in Italy, Sweden, Spain, and the US were found to be correlated to mutations of NRAS. Populations in Japan, China, Turkey, Canada, and Russia were found to be correlated to mutations of KIT. Data correlated to the presence of melanoma and population type is due to the amount of studies performed across of globe.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".