KLK6‐regulated miRNA networks activate oncogenic pathways in breast cancer subtypes
Bibliographic record
Abstract
KLK6 is expressed in normal mammary tissues and is aberrantly regulated in breast cancer. At physiological levels of expression, i.e. those found in normal mammary tissues, KLK6 acts as a tumor suppressor in human breast cancer. However, aberrant overexpression of KLK6 (i.e. 50-100-fold higher than normal), a characteristic of a subset of human breast cancers is associated with increased tumorigenicity (Pampalakis et al. Cancer Res 69:3779-3787, 2009). Here, we stably transfected KLK6-non-expressing MDA-MB-231 breast cancer cells with the full-length KLK6 cDNA to overexpress KLK6 at levels comparable to those observed in patients, and investigated potential oncogenic miRNA networks regulated by these abnormally high KLK6 expression levels and increased activity of this serine protease. A number of miRNAs that are upregulated (e.g. miR-146a) or downregulated (e.g. miR-34a) via KLK6-induced alterations in the miRNA biogenesis machinery were identified. Integrated experimental and bioinformatics analyses identified convergent miRNA networks targeting the cell cycle, MYC, MAPK, and other signaling pathways. In large clinical datasets, significant correlations between KLK6 and downstream MAPK and MYC targets at both the RNA and protein levels was confirmed, as well as negative correlation with GATA3. It was also demonstrated that KLK6 overexpression and likely its proteolytic activity is associated with alterations in downstream miRNAs and their targets, and these differ with the molecular subtypes of breast cancer. The data partly explains the different characteristics of breast cancer subtypes. Importantly, we introduce a combined KLK6-CDKN1B+MYC+CDKN1C score for prediction of long-term patient survival outcomes, with higher scores indicating poor survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".