Downregulating activated epidermal growth factor receptor has no effect on RBM5 expression.
Bibliographic record
Abstract
BACKGROUND: We were interested in determining how the tumor suppressor gene RBM5 is regulated in lung cancers. Previous studies suggested that the gene expression is related to histological subtype and smoking exposure, since in small cell lung cancers the RBM5 gene is deleted whereas in non-small cell lung carcinomas (NSCLC) RBM5 expression is reduced. Of particular interest was the recent finding that in lung adenocarcinomas, a histological subtype of NSCLC, smoking exposure correlated with mutational activity in the transforming growth factor alpha (TGF-a) signaling pathway. Lung adenocarcinomas from smokers were associated with activating KRAS mutations, whereas lung adenocarcinomas from never-smokers were associated with activating epidermal growth factor receptor (EGFR) mutations. We hypothesized that inhibition of RBM5 in lung adenocarcinomas is achieved indirectly via these activating mutations. The objective of the research described herein was to determine if EGFR activation and RBM5 expression are negatively correlated. METHODS: EGFR expression in the lung adenocarcinoma cell line NCI-H1975 was inhibited using small interfering RNA. RBM5 expression was examined by real-time quantitative polymerase chain reaction and Western blotting. RESULTS: Reduced EGFR expression did not correlate with any change in RBM5 expression at either the RNA or protein level. CONCLUSION: These results suggest that RBM5 expression is not directly regulated by EGFR in non-smoker related lung adenocarinomas, and that some other mechanism operates to inhibit either the expression or function of this potential tumour suppressor in lung cancers that retain the RBM5 gene.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".