FKBP10/FKBP65 expression in high-grade ovarian serous carcinoma and its association with patient outcome
Bibliographic record
Abstract
The frequent loss of chromosome 17 in epithelial ovarian carcinomas (EOC), particularly high-grade serous carcinomas (HGSC), has been attributed to the disruption of TP53 (at 17p13.1) and other chromosome 17 genes suspected to play a role in tumour suppressor pathways. In a transcriptome analysis of HGSC, we showed underexpression of a number of chromosome 17 genes, which included FKBP10 (at 17q21.1) and collagen I α 1 (COL1A1; at 17q21.33). FKBP10 codes for the immunophilin FKBP65 and is suspected to act as a chaperone for COL1A1. We have investigated FKBP10 (gene) and FKBP65 (protein) expression in HGSC samples and EOC cell lines that differ in their tumourigenic potential. COL1A1 expression was also investigated given the purported function of FKBP65. RT-PCR analysis verified underexpression of FKBP10 and COL1A1 in HGSCs (n=14) and six tumourigenic EOC cell lines, relative to normal ovarian surface epithelial cells and a non-tumourigenic EOC cell line. Immunohistochemistry analyses of 196 HGSC samples using tissue microarrays revealed variable staining intensities in the epithelial tumour component where only 7.8% and 1.0% of samples stained intensely for FKBP65 and COL1A1, respectively. Variable staining intensities were also observed for the stromal component where 23.6% and 24.1% stained intensely for FKBP65 and COL1A1, respectively. There was no significant correlation of staining intensity of either protein with disease stage. Staining of FKBP65 was clearly visible in normal epithelial cells of the ovarian surface and fallopian tube. There was a significant correlation between absence of FKBP65 staining in the epithelial cell component of the tumour and prolonged overall survival (p<0.001). Our results suggest that underexpression of FKBP65 protein is characteristic of HGSCs and that this expression profile may be linked to molecular pathways associated with an unfavourable outcome in 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 | 0.001 |
| 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".