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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".