Alkaline Phosphatase ALPPL-2 Is a Novel Pancreatic Carcinoma-Associated Protein
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a very low median survival rate. The lack of early sensitive diagnostic markers is one of the main causes of PDAC-associated lethality. Therefore, to identify novel pancreatic cancer biomarkers that can facilitate early diagnosis and also help in the development of effective therapeutics, we developed RNA aptamers targeting pancreatic cancer by Cell-systematic evolution of ligands by exponential enrichment (SELEX) approach. Using a selection strategy that could generate aptamers for 2 pancreatic cancer cell lines in one selection scheme, we identified an aptamer SQ-2 that could recognize pancreatic cancer cells with high specificity. Next, by applying 2 alternative approaches: (i) aptamer-based target pull-down and (ii) genome-wide microarray-based identification of differentially expressed mRNAs in aptamer-positive and -negative cells, we identified alkaline phosphatase placental-like 2 (ALPPL-2), an oncofetal protein, as the target of SQ-2. ALPPL-2 was found to be ectopically expressed in many pancreatic cancer cell lines at both mRNA and protein levels. RNA interference-mediated ALPPL-2 knockdown identified novel tumor-associated functions of this protein in pancreatic cancer cell growth and invasion. In addition, the aptamer-mediated identification of ALPPL-2 on the cell surface and cell secretions of pancreatic cancer cells supports its potential use in the serum- and membrane-based diagnosis of PDAC.
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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 | 0.000 |
| 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 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".