Regulation of human tissue kallikrein-related peptidase expression by steroid hormones in 32 cell lines
Bibliographic record
Abstract
Human tissue kallikrein-related peptidases(KLK), which are secreted serine proteases, are encoded by 15 genes located on chromosome 19q 13.4. Previous studies have shown that KLK expression is regulated by steroid hormones and many KLKs are dysregulated in hormone dependent malignancies. Some KLKs are proposed biomarkers for these cancers. We have characterized KLK hormonal regulation patterns using a large number of human cell lines. KLK levels were quantified in supernatants from 32 cell lines, each subjected to four hormonal stimulations (dexamethasone, norgestrel, dihydrotestosterone or estradiol), using ELISAs. Cell lines included breast, prostate, ovarian, lung, pancreatic, colon, and cervical cancer cells, T-lymphocytes, keratinocytes and non-cancerous epithelial breast cell line. KLKs were regulated in several cell lines not previously studied, such as keratinocytes (KLK 5, 6, and 7), ovarian cancer (KLK 5 and 9) and cervical cancer (KLK 3, 5, 6, 7,8, 10, 11, and 13) cells. Many KLKs were regulated by the synthetic glucocorticoid dexamethasone; specifically, KLK 5, 6, 8, 10, and 11 were upregulated in several breast cancer lines and downregulated in several cervical cancer lines. Knowledge of KLK hormonal regulation patterns will help to shed further light on their potential use as biomarkers and therapeutic targets for hormone-related malignancies.
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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 | 0.001 |
| 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".