Pseudogenes in breast cancer pathogenesis and their clinical utility
Bibliographic record
Abstract
Breast cancer remains as one of the leading causes of mortality and morbidity among women worldwide, underscoring the need for a better understanding of breast cancer etiopathogenesis, effective new therapeutics and clinically actionable biomarkers. Pseudogenes, traditionally seen as nonfunctional sequences of DNA, are increasingly recognized to have important functions in physiology and disease, including in various cancers. Recently accumulating and expanding body of data has emphasized the potential role of pseudogenes in multiple aspects of breast cancer pathogenesis including their function as oncogenes or tumor suppressors to influence cellular proliferation, migration, invasion, apoptosis, angiogenesis, immune modulation, and maintenance of cancer stem cell properties. Moreover, expression of many pseudogenes has been shown to correlate with patient survival parameters and clinicopathologic characteristics of the tumor, making them excellent candidates as prognostic biomarkers. Pseudogene expression can also modulate responses of breast cancer to chemotherapeutic agents and antihormonal treatments, as well as radiotherapy, indicating their potential as treatment response biomarkers. Given their functions in breast cancer biology, pseudogenes can serve as novel targets for treatment. Here, the roles of pseudogenes in breast cancer are discussed in detail.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".