Microarray Expression Profiling of microRNAs Reveals Potential Biomarkers for Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) remains a major health problem for delayed diagnosis, inefficient surveillance and poor prognosis. Recent studies have indicated that non-coding RNAs contribute to the development of new strategies for diagnosis and treatment of HCC. In the present study, we employed 18 pairs of HCC and matched non-tumor tissues for the identification of differentially expressed microRNAs (miRNAs) in HCC, among which 7 paired specimens were selected randomly for microarray detection. Totally, twenty-three miRNAs were screened out to have statistically significant differences with the threshold of P < 0.01 and fold-change ≥ 2.0 or ≤ 0.5 using miRNA microarray. In the validation stage, two miRNAs exhibited higher expression levels in the HCC tissues compared with those in the matched non-tumor tissues, whereas the expression levels of ten miRNAs were lower in the HCC tissues than those in the matched non-tumor tissues. In further analysis, eight miRNAs, including miR-4270, miR-125b-5p, miR-199a-3p, miR-10a-5p, miR-424-5p, miR-195-5p, miR-106b-5p and miR-3651, were retained, when another constraint about the signal intensity of microarray probes was established. Among these miRNAs, our study was the first to show the higher expression level of miR-3651 and the lower expression level of miR-4270 in HCC. The areas under the receiver-operating-characteristic curve values of miR-3651 and miR-4270 were 0.730 and 0.967, respectively, indicating their potential diagnostic values. Our results may help provide the context for expanded interpretations of miRNA studies involved in the progression of liver disease, potentially serving as a diagnostic tool of HCC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".