Kallikrein-related peptidase 7 (KLK7) is a proliferative factor that is aberrantly expressed in human colon cancer
Bibliographic record
Abstract
Emerging evidence indicates that serine proteases of the tissue kallikrein-related peptidases family (KLK) are implicated in tumorigenesis. We recently reported the ectopic expression of KLK4 and KLK14 in colonic cancers and their signaling to control cell proliferation. Human tissue kallikrein-related peptidase 7 (KLK7) is often dysregulated in many cancers; however, its role in colon tumorigenesis has not yet been established. In the present study, we analyzed expression of KLK7 in 15 colon cancer cell lines and in 38 human colonic tumors. In many human colon cancer cells, KLK7 mRNA was observed, which leads to KLK7 protein expression and secretion. Furthermore, KLK7 was detected in human colon adenocarcinomas, but it was absent in normal epithelia. KLK7 overexpression in HT29 colon cancer cells upon stable transfection with a KLK7 expression plasmid resulted in increased cell proliferation. Moreover, subcutaneous inoculation of transfected cells into nude mice led to increased tumor growth that was associated with increased tumor cell proliferation as reflected by a positive Ki-67 staining. Our results demonstrate the aberrant expression of KLK7 in colon cancer cells and tissues and its involvement in cell proliferation in vitro and in vivo. Thus, KLK7 may represent a potential therapeutic target for human colon tumorigenesis.
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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".