AKAP3 correlates with triple negative status and disease free survival in breast cancer
Bibliographic record
Abstract
BACKGROUND: Cancer-testis antigens are among the new promising biomarkers, especially for targeted therapy. Aberrant and specific expression of these proteins has been reported in some tumor tissues. Also understanding their differential role in normal and cancer tissues may introduce them as new candidates for biomarker in cancer. METHODS: AKAP3 expression was investigated in 162 tumors, normal adjacent and normal tissues of the breast with Real-Time PCR. Also the correlation between the gene expression and clinico-pathologic features of the tumors and treatment regimen was evaluated. RESULTS: There was an association between lack of AKAP3 expression in tumor tissues and triple negative status (p=. 03). There was also a correlation between lack of this marker and tumor size (p = .01) and stage (p = .04). Lack of AKAP3 in normal adjacent tissues was associated with poor prognosis. Kaplan Meier plot demonstrated a remarkable better 5-year disease free survival in AKAP3 positive normal adjacent group. CONCLUSIONS: It was found that this relationship is originated from the difference in AKAP3 expression, not therapy distribution between two groups of patients. Thus, it may be a proper biomarker candidate for triple negative breast cancer patients. Also, testing AKAP3 in normal tissue of the patients may be used to predict the outcome of the treatment.
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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 | 0.000 | 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".