Search for Cancer Markers from Endometrial Tissues Using Differentially Labeled Tags iTRAQ and cICAT with Multidimensional Liquid Chromatography and Tandem Mass Spectrometry
Bibliographic record
Abstract
A total of nine potential markers for endometrial cancer (EmCa) have been discovered and identified from endometrial tissue homogenates using a combination of differentially labeled tags, iTRAQ and cICAT, with multidimensional liquid chromatography and tandem mass spectrometry. The tissues were snap frozen in liquid nitrogen within 15-20 min after devitalization. Samples for proteomic analysis were treated with protease inhibitors before processing. Marker proteins that were overexpressed in EmCa are chaperonin 10, pyruvate kinase M1 or M2 isozyme, calgizzarin, heterogeneous nuclear ribonucleoprotein D0, macrophage migratory inhibitory factor, and polymeric immunoglobulin receptor precursor; those that were underexpressed are alpha-1-antitrypsin precursor, creatine kinase B, and transgelin. The chaperonin 10 result confirms our earlier observation of overexpression in EmCa tissues using surface-enhanced laser desorption/ionization mass spectrometry, verified by Western analysis and immunohistochemistry [Yang, E. C. C. et al. J. Proteome Res. 2004, 3, 636-643]. Pyruvate kinase was observed to be overexpressed using both iTRAQ and cICAT labeling. All nine markers have been found to be associated with various forms of cancer. A panel of these plus other markers may confer sufficient selectivity for diagnosing and screening of EmCa. The use of cICAT led to identification of a higher proportion of lower-abundance signaling proteins; conversely, iTRAQ resulted in a higher percentage of the more abundant ribosomal proteins and transcription factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".