Isoform-Specific Upregulation of Palladin in Human and Murine Pancreas Tumors
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDA) is a lethal disease with a characteristic pattern of early metastasis, which is driving a search for biomarkers that can be used to detect the cancer at an early stage. Recently, the actin-associated protein palladin was identified as a candidate biomarker when it was shown that palladin is mutated in a rare inherited form of PDA, and overexpressed in many sporadic pancreas tumors and premalignant precursors. In this study, we analyzed the expression of palladin isoforms in murine and human PDA and explored palladin's potential use in diagnosing PDA. We performed immunohistochemistry and immunoblot analyses on patient samples and tumor-derived cells using an isoform-selective monoclonal antibody and a pan-palladin polyclonal antibody. Immunoblot and real-time quantitative reverse transcription-PCR were used to quantify palladin mRNA levels in human samples. We show that there are two major palladin isoforms expressed in pancreas: 65 and 85-90 kDa. The 65 kDa isoform is expressed in both normal and neoplastic ductal epithelial cells. The 85-90 kDa palladin isoform is highly overexpressed in tumor-associated fibroblasts (TAFs) in both primary and metastatic tumors compared to normal pancreas, in samples obtained from either human patients or genetically engineered mice. In tumor-derived cultured cells, expression of palladin isoforms follows cell-type specific patterns, with the 85-90 kDa isoform in TAFs, and the 65 kDa isoform predominating in normal and neoplastic epithelial cells. These results suggest that upregulation of 85-90 kDa palladin isoform may play a role in the establishment of the TAF phenotype, and thus in the formation of a desmoplastic tumor microenvironment. Thus, palladin may have a potential use in the early diagnosis of PDA and may have much broader significance in understanding metastatic behavior.
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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".