High frequency of mitochondrial DNA D-loop mutations in Ewing’s sarcoma
Bibliographic record
Abstract
Somatic mutations and polymorphisms in the noncoding displacement (D)-loop of mitochondrial DNA (mtDNA) are present in a variety of human cancers. To investigate whether Ewing's sarcoma (EWS) harbors genetic alterations within the D-loop region and their potential association with EWS carcinogenesis, we analyzed and compared the complete mtDNA D-loop sequences from 17 pairs of tumor tissues and corresponding peripheral blood samples using the direct DNA sequencing method. Our results revealed that 12 of the 17 EWS tumor specimens (70.6%) carried 19 somatic mutations in the D-loop of mtDNA, including 11 single-base substitutions, 3 insertions and 5 deletions. Among the tested 17 patients, we screened a total of 40 germline polymorphisms including one novel sequence variant in the D-loop fragment. Most of these identified mutations and germline variations were clustered within two hypervariable segments (HVS1 and HVS2) as well as the homopolymeric C stretch between nucleotide position 303 and 309. In addition, there was no significant correlation between mtDNA D-loop mutations and various clinicopathological factors of EWS. In conclusion, our study reports for the first time that mtDNA D-loop mutations occur at a high frequency in EWS. These data provide evidence of mtDNA alterations' possible involvement in the initiation and/or progression of this rare malignancy.
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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 | 0.000 |
| 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".