STAT3: An Emerging Therapeutic Target for Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a major global health problem and its treatment options have been limited. Signal transducer and activator of transcription 3 (STAT3) is a transcription factor important for various cellular processes. Overexpression and constitutive activation of STAT3 have been frequently found in HCC and associated with poor prognosis. Ample evidence has shown that STAT3 plays pivotal roles in the initiation, progression, metastasis and immune suppression of HCC. Thus, STAT3 has attracted attention as a novel therapeutic target in HCC. Clinical trials have investigated STAT3-targeted therapeutics either as monotherapy or in combination with chemotherapeutic agents, immune checkpoint inhibitors and alternative targeted drugs. Some of these studies have yielded encouraging results. Particularly, napabucasin-a cancer stemness inhibitor targeting STAT3-driven gene transcription-has stood out with its promising clinical efficacy and safety profile. Nonetheless, clinical investigations of STAT3-targeted therapies in HCC are limited and more efforts are strongly urged to evaluate their clinical performance in HCC. Here, we provide a comprehensive review of the roles of STAT3 in HCC and follow by comprehensive analysis of STAT3 targeted strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".