Modification of the Association Between Obesity and Lethal Prostate Cancer by TMPRSS2:ERG
Bibliographic record
Abstract
BACKGROUND: TMPRSS2:ERG is a hormonally regulated gene fusion present in about half of prostate tumors. We investigated whether obesity, which deregulates several hormonal pathways, interacts with TMPRSS2:ERG to impact prostate cancer outcomes. METHODS: The study included 1243 participants in the prospective Physicians' Health Study and Health Professionals Follow-Up Study diagnosed with prostate cancer between 1982 and 2005. ERG overexpression (a TMPRSS2:ERG marker) was assessed by immunohistochemistry of tumor tissue from radical prostatectomy or transurethral resection of the prostate. Body mass index (BMI) and waist circumference, measured on average 1.3 years and 5.3 years before diagnosis, respectively, were available from questionnaires. Data on BMI at baseline was also available. We used Cox regression to calculate hazard ratios and 95% confidence intervals (CIs). All statistical tests were two-sided. RESULTS: During a mean follow-up of 12.8 years, 119 men developed lethal disease (distant metastases or prostate cancer death). Among men with ERG-positive tumors, the multivariable hazard ratio for lethal prostate cancer was 1.48 (95% CI = 0.98 to 2.23) per 5-unit increase in BMI before diagnosis, 2.51 (95% CI = 1.26 to 4.99) per 8-inch increase in waist circumference before diagnosis, and 2.22 (95% CI = 1.35 to 3.63) per 5-unit increase in BMI at baseline. The corresponding hazard ratios among men with ERG-negative tumors were 1.10 (95% CI = 0.76 to1.59; P interaction = .24), 1.14 (95% CI = 0.62 to 2.10; P interaction = .09), and 0.78 (95% CI = 0.52 to 1.19; P interaction = .001). CONCLUSIONS: These results suggest that obesity is linked with poorer prostate cancer prognosis primarily in men with tumors harboring the gene fusion TMPRSS2:ERG.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".