The <scp>H</scp>ippo pathway in chemotherapeutic drug resistance
Bibliographic record
Abstract
Chemotherapy is one of the major treatments for cancer patients. Although chemotherapeutic drugs can sometimes effectively suppress tumor growth in cancer patients, a significant proportion of patients are either intrinsically resistant or later develop resistance to primary chemotherapy, leading to disease relapse and patient mortality. The best way to conquer the resistance is the better understanding of the molecular network in cancer cells in response to drugs. Therefore, identification of signaling pathways and molecules involved in drug resistance is essential for successful treatment of cancers. The Hippo pathway is an emerging signaling pathway that plays important roles in tumorigenesis, stem cell self-renewal and differentiation, organ size control as well as many other biological processes. Therefore, exploring novel roles of the Hippo pathway in various biological functions has become one of the cutting-edge research areas in cancer and other biomedical research. Recently, we and others have provided new evidence that the Hippo pathway is involved in the resistance of different types of cancer cells to various chemotherapeutic drugs. In this review, we will discuss the specific roles of the Hippo pathway in chemotherapy, potential applications for studying this network in response to drugs as well as the future direction in identification of therapeutic targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".