Amplification of cycline D1, c-myc and EGFR oncogenes in tumour samples of breast cancer patients
Bibliographic record
Abstract
Background: Breast cancer is the most common form of cancer in women. It arises from multiple genetic changes in oncogenes and tumor suppressor genes. Among so far studied oncogenes relatively few, including epdermal growth factor receptor (EGfR), cyclinD1 (CCNDİ)and c-myc, have been found to play an important role in progression of this type of human malignancy. The aim of this study was to examine the prognostic potential of CCND1, c-myc and EGFR amplification and their possible cooperation in breast carcinogenesis. Methods: Copy number analyses of CCND1 and c-myc genes were done by TaqMan based quantitative real time PCR. Amplification status of EGFR was determined by differential Pcr. Results: Amplification of CCND1, c-myc and EGFR oncogene has been found in 20.4%, 26.5% and 26.5% of breast cancer cases, respectively. Analysis showed that amplification of CCND1 oncogene was significantly associated with the stage II of disease while amplification of EGFR gene was significantly associated with overexpression of HER-2/neu. Tumour stage and expression of HER-2/neu appeared to be significant predictors of patient's outcome. Stage I patients lived significantly longer then stage III patients (p=0.04) while patients with HER-2/neu overexpression had worse prognoses and lived significantly shorter (p=0.001). Finally, survival of patients who underwent hormone therapy only was significantly longer (p=0.001) then survival of the rest of patients. Conclusions: Amplification of CCND1 or EGFR oncogene is associated with the progression of breast cancer and bad prognosis. No co-ordination in amplification of CCND1, c-myc and EGFR oncogenes were established in this cohort of breast cancer patients.
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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 | 0.000 |
| 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) | 0.001 | 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".