Implications of c-Myc in the pathogenesis and treatment efficacy of urological cancers
Bibliographic record
Abstract
Urological cancers, including prostate, bladder, and renal cancers, are significant causes of death and negatively impact the quality of life for patients. The development and progression of these cancers are linked to the dysregulation of molecular pathways. c-Myc, recognized as an oncogene, exhibits abnormal levels in various types of tumors, and current evidence supports the therapeutic targeting of c-Myc in cancer treatment. This review aims to elucidate the role of c-Myc in driving the progression of urological cancers. c-Myc functions to enhance tumorigenesis and has been documented to increase growth and metastasis in prostate, bladder, and renal cancers. Furthermore, the dysregulation of c-Myc can result in a diminished response to therapy in these cancers. Non-coding RNAs, β-catenin, and XIAP are among the regulators of c-Myc in urological cancers. Targeting and suppressing c-Myc therapeutically for the treatment of these cancers has been explored. Additionally, the expression level of c-Myc may serve as a prognostic factor in clinical settings.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".