Signatures derived from increase in SHARPIN gene copy number are associated with poor prognosis in patients with breast cancer
Bibliographic record
Abstract
We report three signatures produced from SHARPIN gene copy number increase (GCN-Increase) and their effects on patients with breast cancer (BC). In the Metabric dataset (n = 2059, cBioPortal), SHARPIN GCN-Increase occurs preferentially or mutual exclusively with mutations in TP53 , PIK3CA , and CDH1 . These genomic alterations constitute a signature (SigMut) that significantly correlates with reductions in overall survival (OS) in BC patients (n = 1980; p = 1.081e − 6). Additionally, SHARPIN GCN-Increase is associated with 4220 differentially expressed genes (DEGs). These DEGs are enriched in activation of the pathways regulating cell cycle progression, RNA transport, ribosome biosynthesis, DNA replication, and in downregulation of the pathways related to extracellular matrix. These DEGs are thus likely to facilitate the proliferation and metastasis of BC cells. Additionally, through forward (FWD) and backward (BWD) stepwise variate selections among the top 160 downregulated and top 200 upregulated DEGs using the Cox regression model, a 6-gene (SigFWD) and a 50-gene (SigBWD) signature were derived. Both signatures robustly associate with decreases in OS in BC patients within the Curtis (n = 1980; p = 6.16e − 11 for SigFWD; p = 1.06e − 10, for SigBWD) and TCGA cohort (n = 817; p = 4.53e − 4 for SigFWD and p = 0.00525 for SigBWD). After adjusting for known clinical factors, SigMut (HR 1.21, p = 0.0297), SigBWD (HR 1.25, p = 0.0263), and likely SigFWD (HR 1.17, p = 0.062) remain independent risk factors of BC deaths. Furthermore, the proportion of patients positive for these signatures is significantly increased in ER −, Her2-enriched, basal-like, and claudin-low BCs compared to ER + and luminal BCs. Collectively, these SHARPIN GCN-Increase-derived signatures may have clinical applications in management of patients with BC.
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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 | 0.001 |
| 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) | 0.003 | 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".