MALDI imaging mass spectrometry in ovarian cancer for tracking, identifying, and validating biomarkers.
Bibliographic record
Abstract
BACKGROUND: Among biomarkers, cancer-antigen 125 (CA-125) is the most studied. We propose an analytical tool to track ovarian carcinoma biomarkers, that is, the MALDI mass spectrometry imaging. MATERIAL/METHODS: Ovarian carcinomas and benign ovaries were directly analyzed by MALDI-TOF-MS. After automatic profiling and mass spectrometry imaging analyses, hierarchical clustering based on principal component analysis in nonsupervised mode was carried out. On the same samples, preparations were performed to investigate peptides, then proteins, followed by high mass proteins, in an automatic profiling to specific signatures for diagnosis. Using tissue bottom-up strategy on tissue digestion, and mass spectrometry imaging after by shotgun sequencing by nalano-LC-IT-MS in MS/MS mode from washing samples from on tissue digested peptides, several biomarkers were found. RESULTS: A list of specific biomarkers from the ovarian carcinoma regions was obtained and classified as proteins associated with cell proliferation, involved in immune response modulation, signaling to the cytoskeleton, and tumor progression. These specific biomarkers were then validated by immunocytochemistry using Tag-mass technology, cell biology, Western blot, and by PCR (using SKOV-3 ovarian epithelial cancer cells). A link between the immune regulation (innate immunity, tolerance) and virus cause is also discussed. CONCLUSIONS: From the biomarkers identified, proteins involved in immune response modulation and cell proliferation have been pointed out in this study. Two new markers have been identified using such a strategy, that is, fragment C-terminal of the PSME1 (Reg-Alpha) and mucin-9.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".