Updates on the hepatocyte growth factor/c-Met axis in hepatocellular carcinoma and its therapeutic implications
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common cancer and is the second leading cause of cancer death. Since the diagnosis of HCC is difficult, in many cases patients with HCC are diagnosed advanced stage of development. Hepatocyte growth factor (HGF)/c-mesenchymal-epithelial transition receptor (c-Met) axis is a key signaling pathway in HCC, either via canonical or non-canonical pathways. Available treatments against HCC based upon HGF/c-Met inhibition can increase patient lifespan, but do not reach the expected therapeutic benefits. In HCC, c-Met monomers can bind other receptor monomers, activating several noncanonical signaling pathways, leading to increased cell proliferation, invasion, motility, and drug resistance. All of these processes are enhanced by the tumor microenvironment, with stromal cells contributing to boost tumor progression through oxidative stress, angiogenesis, lymphangiogenesis, inflammation, and fibrosis. Novel treatments against HCC are being explored to modulate other targets such as microRNAs, methyltransferases, and acetyltransferases, which are all involved in the regulation of gene expression in cancer. This review compiles basic knowledge regarding signaling pathways in HCC, and compounds already used or showing potential to be used in clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".