Identification of circulating microRNAs during the liver neoplastic process in a murine model of hereditary tyrosinemia type 1
Bibliographic record
Abstract
Hereditary tyrosinemia type 1 (HT1) is a severe inborn error of metabolism, impacting the tyrosine catabolic pathway with a high incidence of hepatocellular carcinoma (HCC). Using a HT1 murine model, we investigated the changes in profiles of circulating and hepatic miRNAs. The aim was to determine if plasma miRNAs could be used as non-invasive markers of liver damage in HT1 progression. Plasma and liver miRNAome was determined by deep sequencing after HT1 phenotype was induced. Sequencing analysis revealed deregulation of several miRNAs including let-7/miR-98 family, miR-21 and miR-148a, during manifestation of liver pathology. Three miRNAs (miR-98, miR-200b, miR-409) presenting the highest plasmatic variations among miRNAs found in both plasma and liver and with >1000 reads in at least one plasma sample, were further validated by RT-qPCR. Two of these miRNAs have protein targets involved in HT1 and significant changes in their circulating levels are detectable prior an increase in protein expression of alpha-fetoprotein, the current biomarker for HCC diagnosis. Future assessment of these miRNAs in HT1 patients and their association with liver neoplastic lesions might designate these molecules as potential biomarkers for monitoring HT1 damage progression, improving diagnosis for early HCC detection and the design of novel therapeutic targets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".