Genetic Polymorphisms Involved in Mitochondrial Metabolism and Pancreatic Cancer Risk
Bibliographic record
Abstract
Abstract Background: The mitochondrial metabolism has been associated with pancreatic ductal adenocarcinoma (PDAC) risk. Recent evidence also suggests the involvement of the genetic variability of the mitochondrial function in several traits involved in PDAC etiology. However, a systematic investigation of the genetic variability of mitochondrial genome (mtSNP) and of all the nuclear genes involved in its functioning (n-mtSNPs) has never been reported. Methods: We conducted a two-phase association study of mtSNPs and n-mtSNPs to assess their effect on PDAC risk. We analyzed 35,297 n-mtSNPs and 101 mtSNPs in up to 55,870 individuals (12,884 PDAC cases and 42,986 controls). In addition, we also conducted a gene-based analysis on 1,588 genes involved in mitochondrial metabolism using Multi-marker Analysis of GenoMic Annotation (MAGMA) software. Results: In the discovery phase, we identified 49 n-mtSNPs and no mtSNPs associated with PDAC risk (P < 0.05). In the second phase, none of the findings were replicated. In the gene-level analysis, we observed that three genes (TERT, SUGCT, and SURF1) involved in the mitochondrial metabolism showed an association below the Bonferroni-corrected threshold of statistical significance (P = 0.05/1588 = 3.1 × 10−5). Conclusions: Even though the mitochondrial metabolism might be involved in PDAC etiology, our results, obtained in a study with one of the largest sample sizes to date, show that neither n-mtSNPs nor mtSNPs are associated with PDAC risk. Impact: This large case–control study does not support a role of the genetic variability of the mitochondrial function in PDAC risk.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".