Genomic alterations detected by comparative genomic hybridization in ovarian endometriomas
Bibliographic record
Abstract
Endometriosis is a complex and multifactorial disease. Chromosomal imbalance screening in endometriotic tissue can be used to detect hot-spot regions in the search for a possible genetic marker for endometriosis. The objective of the present study was to detect chromosomal imbalances by comparative genomic hybridization (CGH) in ectopic tissue samples from ovarian endometriomas and eutopic tissue from the same patients. We evaluated 10 ovarian endometriotic tissues and 10 eutopic endometrial tissues by metaphase CGH. CGH was prepared with normal and test DNA enzymatically digested, ligated to adaptors and amplified by PCR. A second PCR was performed for DNA labeling. Equal amounts of both normal and test-labeled DNA were hybridized in human normal metaphases. The Isis FISH Imaging System V 5.0 software was used for chromosome analysis. In both eutopic and ectopic groups, 4/10 samples presented chromosomal alterations, mainly chromosomal gains. CGH identified 11q12.3-q13.1, 17p11.1-p12, 17q25.3-qter, and 19p as critical regions. Genomic imbalances in 11q, 17p, 17q, and 19p were detected in normal eutopic and/or ectopic endometrium from women with ovarian endometriosis. These regions contain genes such as POLR2G, MXRA7 and UBA52 involved in biological processes that may lead to the establishment and maintenance of endometriotic implants. This genomic imbalance may affect genes in which dysregulation impacts both eutopic and ectopic endometrium.
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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 | 0.002 | 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".