Mining the malignant ascites proteome for pancreatic cancer biomarkers
Bibliographic record
Abstract
Pancreatic cancer (PC) is one of the most lethal malignancies and disease-specific biomarkers are desperately needed for better diagnosis, prognosis, monitoring treatment efficacy and for accelerating the development of novel targeted therapeutics. Being an advanced stage manifestation and a proximal fluid in contact with cancer tissues, the ascitic fluid presents itself as a promising rich source of biomarkers. Herein, we present a comprehensive proteomic analysis of pancreatic ascitic fluid. To fractionate the complex ascites proteome, we adopted a multi-dimensional chromatographic approach that included size-exclusion, ion-exchange and lectin-affinity chromatographic techniques. Our detailed proteomic analysis with high-resolution Orbitrap(®) mass spectrometer resulted in the identification of 816 proteins. We followed rigorous filtering criteria that consisted of PC-specific information obtained from three publicly available databases (Oncomine, Protein Atlas and Unigene) to segregate 20 putative biomarker candidates for future validation. Since these proteins are of membranous and extra-cellular origin, most are glycosylated, and many of them are over-expressed in cancer tissues, the probability of these proteins entering the peripheral blood circulation is high. Their validation as serological PC biomarkers in the future is highly warranted.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".