Increasing EMMPRIN and matriptase expression in hepatocellular carcinoma: tissue microarray analysis of immunohistochemical scores with clinicopathological parameters
Bibliographic record
Abstract
AIMS: To examine the expression of extracellular matrix metalloprotease inducer (EMMPRIN) and matriptase in hepatocellular carcinoma (HCC) and to correlate this with tumour progression. METHODS AND RESULTS: Immunohistochemical analysis of EMMPRIN and matriptase was performed on tissue microarrays of 122 cases of HCC with various histological grades and/or clinical parameters. The expression of EMMPRIN and matriptase was undetectable in normal liver parenchyma of all eight control cases. However, among the 122 HCC cases, EMMPRIN and matriptase immunoreactivity was seen on the cell membrane and in the cytoplasm. The average immunostaining scores of EMMPRIN were 88 for grade I HCC, 195 for grade II HCC and 293 for grade III HCC. Of 85 HCC cases in 122 with detailed clinical TNM stages, the average immunostaining scores of EMMPRIN were 75 for stage T1, 177 for stage T2, 260 for stage T3 and 313 for stage T4 cases of HCC. In addition, the average immunostaining scores of matriptase were 84 for grade I HCC, 187 for grade II HCC, 302 for grade III HCC, and 72 for stage T1, 181 for stage T2, 224 for stage T3 and 284 for stage T4 cases of HCC. More advanced M and N stages of HCC were associated with higher intensity, greater percentages of tumour staining and immunostaining scores of EMMPRIN and matriptase. Higher EMMPRIN and matriptase immunostaining scores in HCCs also correlated significantly with tumour grading and TNM stages. CONCLUSIONS: Our findings demonstrate for the first time that EMMPRIN and matriptase are overexpressed in HCC. These may be novel biomarkers for the diagnosis and treatment of HCC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".