Bruton’s Tyrosine Kinase: A Double-Edged Sword in Cancer and Aging
Bibliographic record
Abstract
Bruton’s tyrosine kinase (BTK) is a key signaling molecule involved in both hematological malignancies and solid tumors. In B-cell malignancies such as chronic lymphocytic leukemia (CLL) and non-Hodgkin lymphoma (NHL), BTK mediates B-cell receptor signaling, promoting tumor survival and proliferation, leading to the development of BTK inhibitors like ibrutinib that improve patient outcomes. In solid tumors, BTK isoforms, particularly p65BTK, contribute to tumor growth and therapy resistance, with inhibition showing promise in cancers like colorectal, ovarian, and non-small cell lung cancer. BTK also influences the tumor microenvironment by modulating immune cells such as myeloid-derived suppressor cells and tumor-associated macrophages, aiding immune evasion. BTK inhibition can enhance anti-tumor immunity and reduce inflammation-driven tumor progression. Additionally, BTK contributes to tumor angiogenesis, with inhibitors like ibrutinib showing anti-angiogenic effects. Beyond cancer, BTK is linked to aging, where its modulation may reduce senescent cell accumulation and preserve cognitive function. This review explores BTK’s dual role, focusing on its oncogenic effects and potential impact on aging processes. We also discuss the use of BTK inhibitors in cancer treatment and their potential to address age-related concerns, providing a deeper understanding of BTK as a therapeutic target and mediator in the complex relationship between cancer and aging.
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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) | 0.001 | 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".