Long noncoding RNAs in liver cancer: what we know in 2014
Bibliographic record
Abstract
INTRODUCTION: Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with an estimated over half a million new cases diagnosed annually. Due to the difficulty in early diagnosis and lack of effective treatment options, HCC is currently ranked as the second highest neoplastic-related mortality in the world, with an extremely low 5-year survival rate of between 6 and 11%. Long noncoding RNAs (lncRNAs), are genes lacking protein coding ability, have recently emerged as pivotal participants in biological processes, often dysregulated in a range of cancers, including HCC. AREAS COVERED: In this review, we highlight the recent findings of lncRNAs in HCC pathogenesis, with particular attention on epigenetic events. In silico analysis was utilized to emphasize intrinsic linkages within the ncRNA families associated with hepatocarcinogenesis. EXPERT OPINION: While our understanding of lncRNAs in the onset and progression of HCC is still in its infancy, there is no doubt that understanding the activities of ncRNAs will certainly secure strong biomarkers and improve treatment options for HCC patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".