Expression of RBM5‐related factors in primary breast tissue
Bibliographic record
Abstract
The aim of this study was to examine the expression of the RBM5 tumor suppressor, in relation to RBM6 and RBM10, to obtain a better understanding of the potential role played by these RBM5-related factors in the regulation of RBM5 tumor-suppressor activity. Paired non-tumor and tumor samples were obtained from 73 breast cancer patients. RNA and protein expression were examined by semi-quantitative reverse transcription-polymerase chain reaction and immunoblot, respectively. Data were analyzed using various statistical methods to test for correlations amongst the RBM5-related factors, and between the factors and various pathological parameters. Most notably, RBM5, RBM10v1, and HER2 protein expression levels were elevated in tumor tissue (P < 0.0001). RBM5 and RBM10v1 protein expression were significantly positively correlated (P < 0.001), as were RBM5 and HER2 protein expression (P < 0.01), in both non-tumor and tumor tissue, whereas RBM10v1 and HER2 protein expression were only marginally correlated, in non-tumor tissue (P < 0.05). Interestingly, RBM5 and RBM10v1 protein expression were both deregulated in relation to RNA expression in tumor tissue. RBM10v2 and RBM6 RNA were highly significantly positively correlated in relation to various factors relating to poor prognosis (P < 0.0001). To our knowledge, this study is the first to examine RBM5 expression at both the RNA and protein level in primary breast tumor tissue, and the first to examine expression of all RBM5-related factors in a comprehensive manner. The results provide a graphic illustration that RBM5-related factors are significantly differentially expressed in breast cancer, and suggest complex inter-related regulatory networks involving alternative splicing, oncogenic expression, and tissue-specific function.
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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".