Higher Expressions of SHH and AR Are Associated with a Positive Receptor Status and Have Impact on Survival in a Cohort of Croatian Breast Cancer Patients
Bibliographic record
Abstract
Breast cancers (BC) are usually classified into four molecular subtypes according to the expression of estrogen (ER), progesterone (PR), and human epidermal growth factor 2 (HER2) receptors and proliferation marker Ki-67. Despite available anti-hormonal therapies and due to the inherent propensity of some subtypes to develop metastasis, there is a permanent need to discover new prognostic and predictive biomarkers, as well as therapeutic targets for BC. In this study, we used immunohistochemical staining to determine the expression of androgen receptor (AR) and sonic hedgehog protein (SHH), the main ligand of the Hedgehog-GLI (HH-GLI) signaling pathway, in 185 archival primary BC tissue samples and correlated it with clinicopathological characteristics, molecular subtypes, receptors statuses, and survival in a cohort of Croatian BC patients. Results showed that higher SHH and AR expressions were associated with positive receptor status, but increased SHH expression had a negative impact on survival in receptor-negative BCs. On the contrary, higher AR expression was mostly protective. However, multivariate analysis showed that only higher AR expression could be considered as an independent prognostic biomarker for poorer overall survival in triple-negative breast cancer patients (TNBC) (HR 10.9, 95% CI 1.43–83.67; p = 0.021), what could be Croatian population-related. SHH could be a potential target for treating TNBCs and HER2-enriched BCs, in cases where HH-GLI signaling is canonical (SHH-dependent).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".