Bibliographic record
Abstract
Prostate cancer (PCa), the third leading cause of cancer-related mortality in Canada, is a heterogeneous disease, making it clinically challenging to distinguish indolent from aggressive cases. Additionally, treatment options in advanced disease are very limited and often have dismal outcomes. Thus, there is an immense need to identify novel prognostic markers and therapy targets. Genomic gain at chromosome 16p13.3 was recently shown to be associated with distant metastases and recurrence of PCa. The Δ3, Δ2-Enoyl-CoA Delta Isomerase 1 (ECI1), a metabolic enzyme encoded from this genomic region, is essential for fatty acids β-oxidation which is the primary source of energy of PCa cells. Unpublished data showed that high ECI1 expression was associated with early relapse after radical prostatectomy and its ectopic overexpression in cancer cells increased tumorigenic behavior. It is unknown how ECI1 contributes to PCa tumorigenicity and whether it could impact benign cells or be clinically relevant in more advanced cases. Hereby, we hypothesized that ECI1 could promote tumor initiation, result in aggressive cancer phenotype through multiple effectors and ultimately lead to worse survival.Stable ectopic ECI1 overexpression in benign prostate RWPE-1 cells enhanced cell growth (P < 0.01), clonogenicity (single cell survival) (P < 0.001), and motility (P < 0.001). The latter effect was reversed with siRNA mediated ECI1 knockdown affirming the specificity of the observed phenotype.Gene expression microarray conducted on LNCaP cells following transient ECI1 knockdown and PC-3 cells with ectopic ECI1 overexpression revealed hundreds of differentially expressed genes (False Discovery Rate < 10%). Gene Ontology and Gene Set Enrichment Analysis reported biological processes related to regulation of cell cycle, migration, and apoptosis. Data from both cell lines was cross-referenced and highlighted 124 and 32 common genes positively and negatively regulated with ECI1, respectively. Out of those, 22%, 5%, and 13% were linked to critical tumorigenic functions such as proliferation, motility, and apoptosis, respectively. With few exceptions, genes with oncogenic effects were found among the positively regulated genes. Out of the 156 genes, 56.4% were previously linked to cancer in literature, and consistent with our phenotype, 83.3% of tumor promoting genes were associated with EC1 overexpression.Immunohistochemistry performed on clinical samples obtained from TURP (transurethral resection of the prostate) procedure showed that, although no statistical significance was found between ECI1 expression and PCa specific survival (P = 0.1), ECI1 overexpression status emerged as a predictor of 10-year overall survival (P =0.039). Multivariate analysis showed that ECI1 maintained its prognostic significance after adjusting for age and Gleason grade.These findings further support the role of ECI1 in PCa pathobiology and demonstrate that ECI1 can impact benign prostate cells and might be involved in tumor initiation. The gene expression data provide multiple candidates that could explain the observed phenotype and provide basis for future research in PCa biology. Our results suggest PCa cases can be usefully classified according to their ECI1 expression, which can ultimately improve prognostication and management
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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 | 0.000 |
| 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) | 0.002 | 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".