Highlighting function of Wnt signalling in urological cancers: Molecular interactions, therapeutic strategies, and (nano)strategies
Bibliographic record
Abstract
• Urological cancers include prostate, bladder and renal tumors that affect many individuals around the world. • Dysregulation of Wnt has been associated with tumorigenesis. • Wnt upregulation In PCa enhances carcinogenesis and can mediate proliferation nd metastasis. • Wnt upregulation in bladder cancer (BC) can enhance malignancy and mediate chemoresistance. • Overexpression of Wnt has been correlated with increase renal cancer progression. Cancer is a complex, multistep process characterized by abnormal cell growth and metastasis as well as the capacity of the tumor cells in therapy resistance development. The urological system is particularly susceptible to a group of malignancies known as urological cancers, where an accumulation of genetic alterations drives carcinogenesis. In various human cancers, Wnt singalling is dysregulated; following nuclear transfer of β-catenin, it promotes tumor progression and affects genes expression. Elevated levels of Wnt have been documented in urological cancers, where its overexpression enhances growth and metastasis. Additionally, increased Wnt singalling contributes to chemoresistance in urological cancers, leading to reduced sensitivity to chemotherapy agents like cisplatin, doxorubicin, and paclitaxel. Wnt upregulation can change radiotherapy response of urological cancers. The regulation of Wnt involves various molecular pathways, including Akt, miRNAs, lncRNAs, and circRNAs, all of which play roles in carcinogenesis. Targeting and silencing Wnt or its associated pathways can mitigate tumorigenesis in urological cancers. Anti-cancer compounds such as curcumin and thymoquinone have shown efficacy in suppressing tumorigenesis through the downregulation of Wnt singalling. Notably, nanoparticles have proven effective in treating urological cancers, with several studies in prostate cancer (PCa) using nanoparticles to downregulate Wnt and suppress tumor growth. Future research should focus on developing small molecules that inhibit Wnt singalling to further suppress tumorigenesis and advance the treatment of urological cancers. Moreover, Wnt can be used as reliable biomarker for the diagnosis and prognosis of urological cancers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".