EXPLORING MICRORNA EXPRESSION PATTERNS IN EARLY DETECTION AND PROGRESSION OF HEPATOCELLULAR CARCINOMA- SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with most cases diagnosed at an advanced stage due to limitations in current diagnostic tools. Traditional biomarkers such as alpha-fetoprotein lack the sensitivity and specificity needed for early detection. MicroRNAs (miRNAs) have emerged as promising molecular biomarkers, but evidence remains fragmented and inconsistent, highlighting the need for a systematic synthesis of current findings. Objective: This systematic review aims to evaluate the diagnostic and prognostic value of specific microRNAs in the early detection and monitoring of hepatocellular carcinoma. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed across PubMed, Scopus, Web of Science, and Cochrane Library for studies published between January 2019 and July 2024. Inclusion criteria encompassed observational and case-control studies assessing miRNA expression in HCC patients. Studies were screened independently by two reviewers, and data were extracted using a standardized form. Risk of bias was assessed using the Newcastle-Ottawa Scale. Due to heterogeneity in methodologies, a qualitative synthesis was performed. Results: Eight studies involving 2,342 participants were included. Key miRNAs identified with strong diagnostic or prognostic relevance included miR-21, miR-122, miR-125b, miR-224, and exosomal miR-500a-3p. Reported sensitivities ranged from 72% to 85%, with area under the curve (AUC) values up to 0.91. Many miRNAs correlated significantly with tumor stage, recurrence, and survival outcomes. Risk of bias was generally low to moderate across included studies. Conclusion: MicroRNAs show substantial potential as non-invasive biomarkers for the early diagnosis and monitoring of HCC. However, variability in detection methods and limited population diversity call for further large-scale, standardized studies to confirm their clinical applicability.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".