The role of kinin receptors in cancer and therapeutic opportunities
Bibliographic record
Abstract
Kinins are generated within inflammatory tissue microenvironments, where they exert diverse functions, including cell proliferation, leukocyte activation, cell migration, endothelial cell activation and nociception. These pleiotropic functions depend on signaling through two cross talking receptors, the constitutively expressed kinin receptor 2 (B2R) and the inducible kinin receptor 1 (B1R). We have reviewed evidence, which supports the concept that kinin receptors, especially kinin receptor 1, are promising targets for cancer therapy, since (1) many tumor cells express aberrantly high levels of these receptors; (2) some cancers produce kinins and use them as autocrine factors to stimulate their growth; (3) activation of kinin receptors leads to activation of macrophages, dendritic cells and other cells from the tumor microenvironment; (4) kinins have pro-angiogenic properties; (5) kinin receptors have been implicated in cancer migration, invasion and metastasis; and (6) selective antagonists for either B1R or B2R have shown anti-proliferative, anti-inflammatory, anti-angiogenic and anti-migratory properties. The multiple cross talks between kinin receptors and renin-angiotensin system (RAS) as well as its implications for targeting KKS or RAS for the treatment of malignancies are also discussed. It is expected that B1R antagonists would interfere less with housekeeping functions and therefore would be attractive compounds to treat selected types of cancer. Reliable clinical studies are needed to establish the translatability of these data to human settings and the usefulness of kinin receptor antagonists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".