Expression and Processing of the Neuroendocrine Protein Secretogranin II in Benign and Malignant Pheochromocytomas
Bibliographic record
Abstract
The aim of the present study was to compare the expression levels of secretogranin II (SgII), prohormone convertases (PC)1 and PC2, and the proteolytic processing of SgII in benign versus malignant pheochromocytomas. Quantitative (Q)-PCR experiments indicated that SgII, PC1, and PC2 mRNAs were overexpressed in pheochromocytoma compared to non-tumoral chromaffin cells (P<0.001) and in benign compared to malignant tumors (P<0.01). Western blot analysis using a human SgII antiserum revealed the occurrence of a 97-kDa band corresponding to the expected size of SgII, with significantly higher quantities in benign than in malignant tumors (P<0.05). Using antisera directed against sequential regions of SgII (N-terminal, secretoneurin [SN], EM66, internal, and C-terminal sequences), we observed distinct processing profiles between benign and malignant pheochromocytomas. In contrast, using PC1 and PC2 antisera no differences between the two types of tumors were found. RIA measurement showed that EM66 median values between benign and malignant chromaffin cell tumors were significantly different (128.5 vs. 6.3 ng/mg protein, respectively; P<0.001). Taken together, these results indicate that, in pheochromocytoma, malignancy is associated with reduced PC1, PC2, and SgII mRNA expression and decreased levels of processing products of SgII, in line with the low concentrations of EM66 that occur in malignant tumors. These data support the notion that SgII-processing products, such as EM66, could represent prognostic markers of pheochromocytomas.
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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) | 0.000 | 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".