Use of a transgenic mouse model to identify markers of human lung tumors
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer related deaths worldwide. Despite advances in detection technologies, most patients diagnosed with lung cancer already harbor metastatic lesions. Because early detection is one of the primary determinants of patient outcome, a transgenic mouse model of lung cancer was utilized to identify markers of early lung tumors in humans. DNA microarray analysis of lung tumors arising in MMTV-IGF-II transgenic mice showed 9 genes consistently elevated in the murine lung tumors. Western blot analyses confirmed that several of these proteins were elevated in the lung tumors and immunohistochemical analyses identified 3 proteins, microsomal glutathione-S-transferase 1 (Mgst1), cathepsin H and syndecan 1 as being consistently elevated in the murine lung tumors compared to non-tumor bearing transgenic lung tissue and normal lung tissue surrounding the tumor. These 3 proteins were also elevated in human lung adenocarcinoma and squamous cell carcinomas. Importantly, the proteins were elevated in early stage, node negative tumors indicating their ability to detect early lung lesions that would be amenable to surgical resection. Therefore, our findings indicate that Mgst1, cathepsin H and syndecan 1 should be further evaluated as markers capable of identifying patients with early stage lung tumors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".