Altered patterns of transcription of the septin gene, <i>SEPT9</i>, in ovarian tumorigenesis
Bibliographic record
Abstract
Ovarian carcinoma represents the most lethal gynaecological malignancy. A variety of morphological subtypes are recognised (e.g. serous, mucinous, endometrioid), which may be benign, borderline or malignant. While their relationship is controversial, knowledge of the molecular mechanisms of ovarian tumorigenesis may help resolve this issue and perhaps identify early markers of disease. Perturbed patterns of expression of the SEPT9 gene on chromosome 17q25.3 have been implicated in a variety of tumour types including both breast and ovarian neoplasia. In preliminary studies, we showed that SEPT9 mRNA was upregulated in a bank of ovarian tumours, which included benign, borderline and malignant tumours, and reported increased levels of one splice variant, SEPT9_v4*. We now describe a comprehensive analysis of SEPT9 expression specifically in serous and mucinous ovarian tumours (benign, borderline and malignant), using cDNA microarray, semi- and quantitative RTPCR of microdissected archival tumour material. Our data show consistent and specific overexpression of both SEPT9_v1 and SEPT9_v4* transcripts in the epithelial component of ovarian tumours. These transcripts show highest levels of expression in serous and mucinous borderline tumours. SEPT9_v1 is also upregulated in both serous and mucinous carcinomas. Interestingly, highest levels of expression are observed in serous borderline and low-grade tumours rather than high-grade in keeping with a model of progression of benign, borderline and low-grade serous tumours.
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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) | 0.001 | 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 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".