ST6GAL1, COX-2 AND HB-EGF MRNA EXPRESSION IN BREAST CANCER SAMPLES FROM KASHAN, IRAN
Bibliographic record
Abstract
Background : ST6Gal1, Cox-2 and HB-EGF genes are involved in different tumors and their enhanced expressions often correlate with poor prognosis. In this study we assay the expressions of these genes by reverse transcriptase-PCR in 54 breast cancer samples. Methods : Tissue samples were either formalin-fixed for histopathological examination or frozen for reverse transcriptase-PCR. Image program was used for the densitometry of the image of the gels and the expression of different genes was normalized with beta actin expression. The student's t-test and correlation matrix were used for data analyses. Results : We observed significantly higher expressions of ST6Gal1 (P= 0.040), Cox- 2 (P= 0.001) and HB-EGF (P= 0.009) in the tumor region compared to the margin samples. A significant correlation was found between HB-EGF and Cox-2 expression (P= 0.001). There was a positive correlation between total score, tumor size, histology grade and nuclear grade but there was a reverse correlation between age and tumor size, histology grade and total score. Conclusion : Expressions of ST6Gal1, Cox-2 and HB-EGF in breast tumor samples in this and a number of other studies emphasize their role as important markers in breast cancer . The use of medications to inhibit either their individual expressions or the possible inhibition of all three genes may improve patient survival and prevent metastasis.
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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 | 0.001 | 0.001 |
| 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".