Human Kallikrein Gene 5 (KLK5) Expression by Quantitative PCR: An Independent Indicator of Poor Prognosis in Breast Cancer
Bibliographic record
Abstract
BACKGROUND: KLK5 is a newly discovered human kallikrein gene. Many kallikrein genes have been found to be differentially expressed in various malignancies, and prostate-specific antigen (PSA; encoded by the KLK3 gene) is the best tumor marker for prostate cancer. Like the genes that encode PSA and other kallikreins, the KLK5 gene was found to be regulated by steroid hormones in the BT-474 breast cancer cell line. METHODS: We studied KLK5 expression in 179 patients with different stages and grades of epithelial breast carcinoma by quantitative reverse transcription-PCR (RT-PCR), using LightCycler((R)) technology. An optimal cutoff point equal to the detection limit (65th percentile) was used. KLK5 values were then compared with other established prognostic factors in terms of disease-free (DFS) and overall survival (OS). RESULTS: High KLK5 expression was found more frequently in pre-/perimenopausal (P = 0.026), node-positive (P = 0.029), and estrogen receptor-negative (P = 0.038) patients. In univariate analysis, KLK5 overexpression was a significant predictor of reduced DFS (P <0.001) and OS (P <0.001). Cox multivariate analysis indicated that KLK5 was an independent prognostic factor for DFS and OS. KLK5 remained an independent prognostic variable in the subgroups of patients with large tumors (>2 cm) and positive nodes. Hazard ratios derived from Cox analysis and related to DFS and OS were 2.48 (P = 0.005) and 2.37 (P = 0.009), respectively, for the node-positive group and 3.03 (P = 0.002) and 2.94 (P = 0.002), respectively, for patients with tumor sizes >2 cm. KLK5 expression was also associated with statistically significantly shorter DFS (P = 0.006) and OS (P = 0.004) in the subgroup of patients with grade I and II tumors. CONCLUSIONS: KLK5 expression as assessed by quantitative RT-PCR is an independent and unfavorable prognostic marker for breast carcinoma.
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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 | 0.000 |
| 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".