Integrated Genomic Analysis of Mitochondrial RNA Processing in Human Cancers
Bibliographic record
Abstract
Abstract Alterations to mitochondrial function and mutations in mitochondrial genes have been reported for a wide variety of cancers, however the mitochondrial transcriptome remains largely unexplored in cancer despite an emerging appreciation of the role that post-transcriptional regulation plays in the etiology of these diseases. Here, we quantify and assess changes to mitochondrial RNA processing in human cancers using integrated genomic analysis of RNA Sequencing and genotyping data from 1226 samples across 12 different cancer types. We find significant changes to m 1 A and m 1 G post-transcriptional methylation rates at functionally important positions in mitochondrial tRNAs in tumor tissues across all cancers. Pathways of RNA processing are strongly associated with methylation rates in normal tissues (P=2.85×10 -27 ), yet these associations are lost in tumors. Furthermore, we report 18 gene-by-disease-state interactions where altered methylation rates occur under cancer status conditional on genotype, implicating genes associated with mitochondrial function or cancer (e.g. CACNA2D2, LMO2 and FLT3 ) and suggesting that nuclear genetic variation can potentially modulate an individual’s ability to maintain unaltered rates of mitochondrial RNA processing under cancer status. Finally, we report a significant association between the magnitude of methylation rate changes in tumors and patient survival outcomes. These results highlight mitochondrial post-transcriptional events as a clinically relevant mechanism and as a theme for the further investigation of cancer processes, biomarkers and therapeutic interventions.
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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 | 0.000 |
| 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".