Oxidative stress accelerates intestinal tumorigenesis by enhancing 8-oxoguanine-mediated mutagenesis in MUTYH-deficient mice
Bibliographic record
Abstract
Oxidative stress–induced DNA damage and its repair systems are related to cancer etiology; however, the molecular basis triggering tumorigenesis is not well understood. Here, we aimed to explore the causal relationship between oxidative stress, somatic mutations in pre-tumor-initiated normal tissues, and tumor incidence in the small intestines of MUTYH-proficient and MUTYH-deficient mice. MUTYH is a base excision repair enzyme associated with human colorectal cancer. Mice were administered different concentrations of potassium bromate (KBrO3; an oxidizing agent)–containing water for 4 wk for mutagenesis studies or 16 wk for tumorigenesis studies. AllMutyh−/−mice treated with >0.1% KBrO3developed multiple tumors, and the average tumor number increased dose dependently. Somatic mutation analysis ofMutyh−/−/rpsLtransgenic mice revealed that G:C > T:A transversion was the only mutation type correlated positively with KBrO3dose and tumor incidence. These mutations preferentially occurred at 5′G in GG and GAA sequences inrpsL. This characteristic mutation pattern was also observed in the genomic region ofMutyh−/−tumors using whole-exome sequencing. It closely corresponded to signature 18 and SBS36, typically caused by 8-oxo-guanine (8-oxoG). 8-oxoG-induced mutations were sequence context dependent, yielding a biased amino acid change leading to missense and stop-gain mutations. These mutations frequently occurred in critical amino acid codons of known cancer drivers,ApcorCtnnb1, known for activating Wnt signal pathway. Our results indicate that oxidative stress contributes to increased tumor incidence by elevating the likelihood of gaining driver mutations by increasing 8-oxoG-mediated mutagenesis, particularly under MUTYH-deficient conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".