Androgen Receptor, Resistensi Terapi, dan Pendekatan Terapeutik Baru pada Kanker Prostat
Bibliographic record
Abstract
Androgen receptor (AR) merupakan penggerak utama perkembangan kanker prostat dan tetap aktif meskipun kadar testosteron telah mencapai kondisi kastrasi. Resistensi terapi muncul melalui amplifikasi AR, mutasi ligand-binding domain (LBD), varian splice seperti AR-V7, bypass pathways (misalnya glucocorticoid receptor), serta aktivasi jalur non-AR seperti PI3K/AKT dan NF-κB. Tinjauan sistematis ini menganalisis mekanisme resistensi berbasis AR, peran jalur non-AR, serta efektivitas pendekatan terapeutik baru pada castration-resistant prostate cancer (CRPC). Systematic review dilakukan sesuai PRISMA 2020. PICO digunakan untuk merumuskan pertanyaan riset. Pencarian dilakukan pada PubMed, Scopus, Web of Science, dan Embase menggunakan kata kunci seperti “prostate cancer”, “castration-resistant prostate cancer”, “androgen receptor”, “AR-V7”, “antiandrogen resistance”, dan “novel AR-targeted therapy” dengan rentang publikasi 2015–2025. Screening dilakukan oleh dua reviewer independen. Kualitas studi dianalisis menggunakan RoB-2 dan Newcastle–Ottawa Scale. Dari 1.972 artikel yang teridentifikasi pada tahap awal, sebanyak 33 studi memenuhi kriteria inklusi dan disertakan dalam sintesis akhir. Lima tema utama ditemukan: (1) reaktivasi AR melalui amplifikasi, mutasi, dan AR-V7; (2) aktivasi jalur non-AR; (3) resistensi terhadap antiandrogen generasi baru; (4) terapi inovatif seperti PROTACs, AR-V7 degraders, N-terminal domain inhibitors, dan RNA interference; (5) pendekatan drug repurposing & strategi re-sensitisasi seperti Bipolar Androgen Therapy (BAT). Resistensi CRPC didorong oleh interaksi kompleks antara perubahan AR, jalur kompensasi non-AR, dan adaptasi epigenetik. Terapi multimodal berbasis biomarker menjadi arah masa depan pengelolaan CRPC.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".