Identification of the specific characteristics of neuroendocrine prostate cancer: Immune status, hub genes and treatment
Bibliographic record
Abstract
• We identified CHST1, MPPED2, and RIPPLY3 as the critical genes in the development in prostate cancer and NEPC. • Our results suggested the role of CHST1, MPPED2, and RIPPLY3 as the critical genes in the development in prostate cancer. • We also predicted and validated imatinib as the potential specific drugs to NEPC. Castration-resistant prostate cancer (CRPC) marks the advanced phase of prostate malignancy, manifested through two principal subtypes: castration-resistant adenocarcinoma (CRPC-adeno) and neuroendocrine prostate cancer (NEPC). This study aims to identify unique central regulatory genes, assess the immunological landscape, and explore potential therapeutic strategies specifically tailored to NEPC. We discovered 1444 differentially expressed genes (DEGs) distinguishing between the two cancer types and identified 12 critical hub genes. Notably, CHST1, MPPED2, and RIPPLY3 emerged as closely associated with the immune cell infiltration pattern, establishing them as top candidates. Prognostic analysis highlighted the potential critical roles of CHST1 and MPPED2 in prostate cancer development, findings corroborated through in vitro and in vivo assays. Moreover, we validated the functions and expression levels of CHST1, MPPED2, and RIPPLY3 in NEPC using cell lines, animal models and human tissues. In the final step, we found that imatinib might be the drug specific to NEPC, which was further confirmed by in vitro cell assay. Our results revealed the clinical characteristics, molecular features, immune cell infiltration pattern in CRPC-adeno and NEPC, and identified and confirmed CHST1, MPPED2, and RIPPLY3 as the critical genes in the development in prostate cancer and NEPC. We also predicted and validated imatinib as the potential specific drugs to NEPC.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".