Regulation of pancreatic cancer growth by superoxide
Bibliographic record
Abstract
K-ras mutations have been identified in up to 95% of pancreatic cancers, implying their critical role in the molecular pathogenesis. Expression of K-ras oncogene in an immortalized human pancreatic ductal epithelial cell line, originally derived from normal pancreas (H6c7), induced the formation of carcinoma in mice. We hypothesized that K-ras oncogene correlates with increased non-mitochondrial-generated superoxide (O 2.-), which could be involved in regulating cell growth contributing to tumor progression. In the H6c7 cell line and its derivatives, H6c7er-Kras+ (H6c7 cells expressing K-ras oncogene), and H6c7eR-KrasT (tumorigenic H6c7 cells expressing K-ras oncogene), there was an increase in hydroethidine fluorescence in cell lines that express K-ras. Western blots and activity assays for the antioxidant enzymes that detoxify O 2.- were similar in these cell lines suggesting that the increase in hydroethidine fluorescence was not due to decreased antioxidant capacity. To determine a possible non-mitochondrial source of the increased levels of O 2.-, Western analysis demonstrated the absence of NADPH oxidase-2 (NOX2) in H6c7 cells but present in the H6c7 cell lines expressing K-ras and other pancreatic cancer cell lines. Inhibition of NOX2 decreased hydroethidine fluorescence and clonogenic survival. Furthermore, in the cell lines with the K-ras oncogene, overexpression of superoxide dismutases that detoxify non-mitochondrial sources of O 2.-, and treatment with the small molecule O 2.- scavenger Tempol, also decreased hydroethidine fluorescence, inhibited clonogenic survival and inhibited growth of tumor xenografts. Thus, O 2.- produced by NOX2 in pancreatic cancer cells with K-ras, may regulate pancreatic cancer cell growth.
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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 | 0.000 | 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) | 0.001 | 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".