Differential Expression Profiling and Functional Analysis of microRNAs through Stage I-III Papillary Thyroid Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To elucidate the mechanisms undergoing the pathogenesis of PTC, this study try to find stage specific microRNAs (miRNAs) using microarray chip in stage I, II and III papillary thyroid carcinoma (PTC) tissues as well predict miRNAs binding target genes and their molecular functions. METHODS: PTC specimens of stage I, II, and III and their paired adjacent non-tumor tissue (one patient for each stage) were collected. The expressions of miRNAs were examined using miRNA microarray chip. The most significant changed miRNAs from microarray were verified by using quantitative RT-PCR. The Potential miRNAs regulating target genes and their preliminary biological functions were forecasted with variety function prediction software. RESULTS: Ten miRNAs exhibited sequential up regulation expression profiles and five miRNAs performed sequential down regulation throughout stage I to III (p<0.05). After normalization, Fifteen miRNAs showed significant different compared to adjacent non-tumor tissues (p<0.05). Among of them, the most significant up regulation and down regulation miRNAs were miR-146b-5p and miR-335, respectively. Both of them were verified with qRT-PCR. 34 target genes for miR-146-5p and 36 target genes for miR-335 was predicted. CONCLUSION: MicroRNA profile assay successfully detected a branch of differential expression miRNAs between PTC and normal tissue. Some of them also showed stage specific. Biological function analysis showed that target genes were involved in five aspects including cell proliferation, differentiation, apoptosis, cycle, and signaling transduction pathway, suggesting the regulatory role of abnormal expression of critical miRNAs in the pathogenesis of PTC.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".