Protein Expression Profiling of Endometrial Malignancies Reveals a New Tumor Marker: Chaperonin 10
Bibliographic record
Abstract
Endometrial carcinoma is a common malignancy in women, being exceeded in incidence only by that of breast, lung, and colorectal cancers. At present, no serum tumor markers are available for the monitoring of endometrial carcinoma patients, and patients with recurrent disease are detected only following the development of symptoms or abnormalities in imaging assessments. Similarly, no screening tools are available for endometrial carcinoma. Protein profiling by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has proven to be a sensitive and fast method of analysis for small proteins or peptides to yield specific biomarkers. In this study, a variety of normal and malignant endometrial tissue samples were fractionated and analyzed by SELDI-TOF MS (SELDI is a version of MALDI utilizing protein "chips"). A number of proteins displayed differential expression in malignant endometrial tissues. One of the prominent proteins fractionated by weak cation exchange chromatography and displaying enhanced expression in these malignant tissues was identified as chaperonin 10. The increased expression of chaperonin 10 in malignant endometrial tissues was further confirmed by parallel Western blot and immunohistochemistry analyses.
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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 | 0.000 | 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) | 0.000 | 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".