A strategy for high‐resolution protein identification in surface‐enhanced laser desorption/ionization mass spectrometry: Calgranulin A and chaperonin 10 as protein markers for endometrial carcinoma
Bibliographic record
Abstract
Surface-enhanced laser desorption/ionization-mass spectrometry (SELDI-MS) has conventionally been practiced on linear time of flight (TOF) which has low mass accuracy and resolution. Here we demonstrate in an examination of both malignant and nonmalignant endometrial tissue homogenates that high mass accuracy and resolution in the MS stage are crucial. Using a commercially available quadrupole/TOF (QqTOF), we were able to resolve two potential cancer markers, subsequently identified off-line as chaperonin 10 and calgranulin A, that differ by 8 Da in mass. Two off-line protein identification protocols were developed: the first was based on size-exclusion chromatography (SEC), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), protein extraction, trypsin digestion, and matrix-assisted laser desorption/ionization-tandem MS (MALDI-MS/MS); the second on SEC and shotgun nano-liquid chromatography (nanoLC)-MS/MS. Analyses on a cohort of 44 endometrial homogenates showed 22 out of 23 nonmalignant samples had nondetectable to very low abundance of chaperonin 10 and calgranulin A; 17 of the 21 malignant samples had detectable to abundant levels of both proteins. Immunohistochemical staining of a tissue microarray of 32 samples showed that approximately half of malignant endometrial tissues exhibited positive staining for calgranulin A in the malignant epithelium, while 9 out of 10 benign tissues exhibited negative epithelial staining. In addition, macrophages/granulocytes in malignant as well as nonmalignant tissues showed positive staining. No immunostaining occurred in stroma or myometrium. Calgranulin A, in combination with chaperonin 10 and other proteins, may eventually constitute a panel of markers to permit diagnosis and screening of endometrial cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".