Lack of expression of c-KIT in ovarian cancers is associated with poor prognosis
Bibliographic record
Abstract
The c-KIT protooncogene encodes a tyrosine kinase receptor, KIT, that is expressed in many normal and cancerous tissues. In this study, we have examined the expression of c-KIT and its ligand, stem cell factor (SCF), in human epithelial ovarian tumors, in normal ovaries and in cultured ovarian surface epithelium (OSE). Cultured cells, normal tissues and tumors were analyzed by Northern and Western blot analyses, reverse transcription-polymerase chain reaction and immunohistochemistry. Normal OSE expressed SCF, but not c-KIT; however, epithelial invaginations and inclusion cysts often expressed KIT protein. Of 15 benign ovarian tumors and tumors of low malignant potential, 87% expressed c-KIT, and 92% of these co-expressed SCF, suggesting the possibility of autocrine growth regulation. Of 35 malignant ovarian cancers, 71% expressed c-KIT (92% co-expressed SCF), with a trend for decreased c-KIT expression in advanced stage disease. Of 34 patients with malignant tumors for whom follow-up information was available (median follow-up time of 24 months), 9 had tumors that did not express c-KIT, 8 (89%) of whom have died and the remaining 1 has recurrent disease. Of the 25 patients with tumors expressing c-KIT, 56% are still alive. Eight of the patients have no evidence of disease and all had KIT-expressing tumors. Statistical analysis indicated that patients whose tumors did not express c-KIT had a significantly shorter (p < 0.05) disease-free survival time than patients who had KIT-expressing tumors. Our results suggest that c-KIT may play a role in early ovarian tumorigenesis, and that loss of c-KIT expression is associated with poor prognosis.
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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".