Keratin 19: a key role player in the invasion of human hepatocellular carcinomas
Bibliographic record
Abstract
OBJECTIVE: Keratin (K)19, a biliary/hepatic progenitor cell (HPC) marker, is expressed in a subset of hepatocellular carcinomas (HCC) with poor prognosis. The underlying mechanisms driving this phenotype of K19-positive HCC remain elusive. DESIGN: Clinicopathological value of K19 was compared with EpCAM, and α-fetoprotein, in a Caucasian cohort of 242 consecutive patients (167 surgical specimens, 75 needle biopsies) with different underlying aetiologies. Using microarrays and microRNA profiling the molecular phenotype of K19-positive HCCs was identified. Clinical primary HCC samples were submitted to in vitro invasion assays and to side population analysis. HCC cell lines were transfected with synthetic siRNAs against KRT19 and submitted to invasion and cytotoxicity assays. RESULTS: In the cohort of surgical specimens, K19 expression showed the strongest correlation with increased tumour size (p<0.01), decreased tumour differentiation (p<0.001), metastasis (p<0.05) and microvascular invasion (p<0.001). The prognostic value of K19 was also confirmed in a set of 75 needle biopsies. Profiling showed that K19-positive HCCs highly express invasion-related/metastasis-related markers (eg, VASP, TACSTD2, LAMB1, LAMC2, PDGFRA), biliary/HPC markers (eg, CD133, GSTP1, NOTCH2, JAG1) and members of the miRNA family 200 (eg, miR-141, miR-200c). In vitro, primary human K19-positive tumour cells showed increased invasiveness, and reside in the chemoresistant side population. Functionally, K19/KRT19 knockdown results in reduced invasion, loss of invadopodia formation and decreased resistance to doxorubicin, 5-fluorouracil and sorafenib. CONCLUSIONS: Giving the distinct invasive properties, the different molecular profile and the poor prognostic outcome, K19-positive HCCs should be considered as a seperate entity of HCCs.
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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 | 0.001 |
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".