Somatic frameshift mutations in the Bloom syndrome BLM gene are frequent in sporadic gastric carcinomas with microsatellite mutator phenotype
Bibliographic record
Abstract
BACKGROUND: Genomic instability has been reported at microsatellite tracts in few coding sequences. We have shown that the Bloom syndrome BLM gene may be a target of microsatelliteinstability (MSI) in a short poly-adenine repeat located in its coding region. To further characterize the involvement of BLM in tumorigenesis, we have investigated mutations in nine genes containing coding microsatellites in microsatellite mutator phenotype (MMP) positive and negative gastric carcinomas (GCs). METHODS: We analyzed 50 gastric carcinomas (GCs) for mutations in the BLM poly(A) tract aswell as in the coding microsatellites of the TGFbeta1-RII, IGFIIR, hMSH3, hMSH6, BAX, WRN, RECQL and CBL genes. RESULTS: BLM mutations were found in 27% of MMP+ GCs (4/15 cases) but not in any of the MMP negative GCs (0/35 cases). The frequency of mutations in the other eight coding regions microsatellite was the following: TGFbeta1-RII (60 %), BAX (27%), hMSH6 (20%),hMSH3 (13%), CBL (13%), IGFIIR (7%), RECQL (0%) and WRN (0%). Mutations in BLM appear to be more frequently associated with frameshifts in BAX and in hMSH6and/or hMSH3. Tumors with BLM alterations present a higher frequency of unstable mono- and trinucleotide repeats located in coding regions as compared with mutator phenotype tumors without BLM frameshifts. CONCLUSIONS: BLM frameshifts are frequent alterations in GCs specifically associated with MMP+tumors. We suggest that BLM loss of function by MSI may increase the genetic instability of a pre-existent unstable genotype in gastric tumors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".