Epigenetic regulation of hepatocellular carcinoma progression: MicroRNAs as therapeutic, diagnostic and prognostic factors
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC), a significant challenge for public healthcare systems in developed Western countries including the USA, Canada, and the UK, is influenced by different risk factors including hepatitis virus infections, alcoholism, and smoking. The disruption in the balance of microRNAs (miRNAs) plays a vital function in tumorigenesis, given their function as regulators in numerous signaling networks. These miRNAs, which are mature and active in the cytoplasm, work by reducing the expression of target genes through their impact on mRNAs. MiRNAs are particularly significant in HCC as they regulate key aspects of the tumor, like proliferation and invasion. Additionally, during treatment phases such as chemotherapy and radiotherapy, the levels of miRNAs are key determinants. Pre-clinical experiments have demonstrated that altered miRNA expression contributes to HCC development, metastasis, drug resistance, and radio-resistance, highlighting related molecular pathways and processes like MMPs, EMT, apoptosis, and autophagy. Furthermore, the regulatory role of miRNAs in HCC extends beyond their immediate function, as they are also influenced by other epigenetic factors like lncRNAs and circular RNAs (circRNAs), as discussed in recent reviews. Applying these discoveries in predicting the prognosis of HCC could mark a significant advancement in the therapy of this disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".