<scp>OCT</scp>‐2 Is Associated With Pro‐Metastatic Epigenomic Properties of Triple‐Negative Breast Cancer Cells
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a malignant type of breast cancer. Owing to the lack of expression of receptors that serve as molecular targets for standard therapy for breast cancer, conventional cytotoxic chemotherapy is the primary treatment option for TNBC. However, TNBC exhibits a high degree of genomic heterogeneity, rendering it resistant to chemotherapy. Therefore, there is an urgent need to identify novel therapeutic targets for the treatment of TNBC. Advances in massively parallel sequencing technology have enabled the identification of cancer cell-specific gene expression patterns and epigenetic alterations that regulate their expression. Cancer cell-specific super-enhancers (SEs) have been identified as effective therapeutic targets for cancer. In this study, we identified the functional roles of epigenetic changes and their regulatory mechanisms in TNBC cells. TNBC cell-specific SEs were formed near several genes that contribute to malignant cancer cell acquisition. We found that the transcription factor OCT-2 (encoded by POU2F2) was responsible for the formation of SEs and the expression of genes encoded in the vicinity of the SE regions. Overexpression of POU2F2 enhances the metastasis of TNBC cells in mice, and its expression is highly correlated to poor prognosis of TNBC patients. Our findings provide a new insight into cancer cell-specific epigenetic changes induced by OCT-2, which trigger the progression of TNBC, and suggest possible candidates that could be targeted for the treatment of TNBC.
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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) | 0.001 | 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".