Evaluation and prognostic significance of ACAT1 as a marker of prostate cancer progression
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer is the second leading cause of cancer-related death among men in North America. While a majority of prostate cancer cases remain indolent, subsets of patients develop aggressive cancers, which may lead to death. The current methods of detection include digital rectal examination and the serum PSA test. However, due to lack of specificity, neither of these approaches is able to accurately discriminate between indolent and aggressive cancer, which is why there is a need for additional prognostic factors. Previously, we identified enzymes of the ketogenic pathway, particularly ACAT1, to be elevated in aggressive prostate cancer. METHODS: In the current study, we assessed the diagnostic and prognostic potential of ACAT1 by analyzing its expression using immunohistochemistry on a tissue microarray consisting of 251 clinically localized prostate cancer patients who have undergone radical prostatectomy. RESULTS: Using quantitative digital imaging software, we found that ACAT1 expression was significantly greater in cancerous cores compared to adjacent benign cores (P < 0.0001), in Gleason score (GS) ≥8 cancers versus GS≤6 cancers (P < 0.0001), GS≥8 cancers versus GS7 cancers (P = 0.001), as well as pT3/pT4 versus pT2 cancers (P = 0.001). In addition, ACAT1 predicted biochemical recurrence in univariate (HR, 1.81, CI = 1.13-2.9, P = 0.0128), and multivariate models (HR, 1.69, CI = 1.01-2.81, P = 0.0431) including pre-operative PSA level, Gleason score and pathological stage. In univariate time-to-recurrence analysis, ACAT1 expression predicted recurrence in ERG negative cases (P = 0.0025), whereas ERG positive cases did not display any differences. DISCUSSION: Taken together, these findings indicate that ACAT1 expression could serve as a potential prognostic marker in prostate cancer, specifically in differentiating indolent and aggressive forms of cancer.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".