Stanniocalcin 2 expression is associated with a favourable outcome in male breast cancer
Bibliographic record
Abstract
Breast cancer can occur in either gender; however, it is rare in men, accounting for <1% of diagnosed cases. In a previous transcriptomic screen of male breast cancer (MBC) and female breast cancer (FBC) occurrences, we observed that Stanniocalcin 2 (STC2) was overexpressed in the former. The aim of this study was to confirm the expression of STC2 in MBC and to investigate whether this had an impact on patient prognosis. Following an earlier transcriptomic screen, STC2 gene expression was confirmed by RT-qPCR in matched MBC and FBC samples as well as in tumour-associated fibroblasts derived from each gender. Subsequently, STC2 protein expression was examined immunohistochemically in tissue microarrays containing 477 MBC cases. Cumulative survival probabilities were calculated using the Kaplan-Meier method and multivariate survival analysis was performed using the Cox hazard model. Gender-specific STC2 gene expression showed a 5.6-fold upregulation of STC2 transcripts in MBC, also supported by data deposited in Oncomine™. STC2 protein expression was a positive prognostic factor for disease-free survival (DFS; Log-rank; total p = 0.035, HR = 0.49; tumour cells p = 0.017, HR = 0.44; stroma p = 0.030, HR = 0.48) but had no significant impact on overall survival (Log-rank; total p = 0.23, HR = 0.71; tumour cells p = 0.069, HR = 0.59; stroma p = 0.650, HR = 0.87). Importantly, multivariate analysis adjusted for patient age at diagnosis, node staging, tumour size, ER, and PR status revealed that total STC2 expression as well as expression in tumour cells was an independent prognostic factor for DFS (Cox regression; p = 0.018, HR = 0.983; p = 0.015, HR = 0.984, respectively). In conclusion, STC2 expression is abundant in MBC where it is an independent prognostic factor for DFS.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".