Over‐expression of <i>S100A2</i> in pancreatic cancer correlates with progression and poor prognosis
Bibliographic record
Abstract
Controversy exists regarding the clinical significance of S100A2 in the progression of tumours. In pancreatic cancer, little is known about the role of S100A2. The aim of this study was to clarify the clinical significance of S100A2 expression in pancreatic carcinogenesis. We microdissected invasive ductal carcinoma (IDC) cells from 22 lesions, pancreatic intraepithelial neoplasia (PanIN) cells from five lesions, intraductal papillary mucinous neoplasm (IPMN) cells from 38 lesions, pancreatitis-affected epithelial (PAE) cells from 16 lesions, and normal ductal cells from 18 normal pancreatic tissues. S100A2 expression in 14 pancreatic cancer cell lines, microdissected cells and formalin-fixed paraffin-embedded (FFPE) samples was examined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Microdissection analyses revealed that IDC cells expressed higher levels of S100A2 than did IPMN, PAE or normal cells (all comparisons, p < 0.007). Cell lines from metastatic sites expressed higher levels of S100A2 than those from primary sites. PanIN cells expressed higher levels of S100A2 than normal cells (p = 0.018). IDC cells associated with poorly differentiated adenocarcinoma expressed higher levels of S100A2 than did IDC cells without poorly differentiated adenocarcinoma (p = 0.006). Analyses of FFPE samples revealed that levels of S100A2 were higher in samples from patients who survived < 1000 days after surgery than in those from patients who survived > 1000 days (p = 0.043). Immunohistochemical analysis was consistent with qRT-PCR. S100A2 may be a marker of tumour progression or prognosis in pancreatic carcinogenesis and pancreatic 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".