Somatic mitochondrial <scp>DNA</scp> mutations in Chinese patients with osteosarcoma
Bibliographic record
Abstract
Somatic mutations in mitochondrial DNA (mtDNA) have been long proposed to drive the pathogenesis and progression of human malignancies. Previous investigations have revealed a high frequency of somatic mutations in the D-loop control region of mtDNA in osteosarcoma. However, little is known with regard to whether or not somatic mutations also occur in the coding regions of mtDNA in osteosarcoma. To test this possibility, in the present study we screened somatic mutations over the full-length mitochondrial genome of 31 osteosarcoma tumour tissue samples, and corresponding peripheral blood samples from the same cohort of patients. We detected a sum of 11 somatic mutations in the mtDNA coding regions in our series. Nine of them were missense or frameshift mutations that have the potential to hamper mitochondrial respiratory function. In combination with our earlier observations on the D-loop fragment, 71.0% (22/31) of patients with osteosarcoma carried at least one somatic mtDNA mutation, and a total of 40 somatic mutations were identified. Amongst them, 29 (72.5%) were located in the D-loop region, two (5%) were in the sequences of the tRNA genes, two (5%) were in the mitochondrial ATP synthase subunit 6 gene and seven (17.5%) occurred in genes encoding components of the mitochondrial respiratory complexes. In addition, somatic mtDNA mutation was not closely associated with the clinicopathological characteristics of osteosarcoma. Together, these findings suggest that somatic mutations are highly prevalent events in both coding and non-coding regions of mtDNA in osteosarcoma. Some missense and frameshift mutations are putatively harmful to proper mitochondrial activity and might play vital roles in osteosarcoma carcinogenesis.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".