Differential expression of the human kallikrein gene 14 (<i>KLK14</i>) in normal and cancerous prostatic tissues
Bibliographic record
Abstract
BACKGROUND: Many members of the human kallikrein gene family are differentially expressed in cancer and a few have potential as diagnostic/prognostic markers. KLK14 is a newly discovered human kallikrein gene that is mainly expressed in the central nervous system and endocrine tissues. Since KLK14 was found to be regulated by steroid hormones in prostate cancer cell lines, we hypothesized that it will be differentially expressed in prostate cancer tissues compared to their normal counterparts. METHODS: Matched prostate tissue samples from the cancerous and non-cancerous parts of the same prostates were obtained from 100 patients who underwent radical prostatectomy. Quantitative analysis of KLK14 expression levels were performed by real-time RT-PCR using SYBR Green I dye on the LightCycler trade mark system. Associations with clinico-pathological parameters were analyzed. RESULTS: KLK14 overexpression in the cancerous compared to non-cancerous tissue was found in 74% of patients (P < 0.001). Mean level of expression was 154 arbitrary units (Au) in cancerous tissues and 14.2 Au in the non-cancerous tissues. The ratio of the cancerous to non-cancerous KLK14 expression values was higher in patients with late stage (stage III) compared to stage II (P = 0.002), and in grade 3 compared to grade 1/2 tumors (P = 0.001). A statistically significant increase was also observed in patients with higher in Gleason score (>6) compared to Gleason score = 6 tumors (P = 0.027). No correlation was found between KLK14 tissue expression levels and serum prostate-specific antigen. CONCLUSIONS: KLK14 expression is significantly higher in cancerous compared to non-cancerous prostatic tissue. The up-regulation of the KLK14 gene in advanced and more aggressive tumors may indicate a possible role for the hK14 protein in tumor spread and opens the possibility of hK14 being a candidate new marker for prostate cancer diagnosis and prognosis.
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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 | 0.001 | 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".