Genetic variations of microRNAs in human cancer and their effects on the expression of miRNAs
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the posttranscriptional level to lead to mRNA degradation or repressed protein production. The expression of miRNA is deregulated in many types of cancers. To determine whether genetic alterations in miRNA genes are associated with cancers, we have systematically screened sequence variations in several hundred human miRNAs from >100 human tumor tissues and 20 cancer cell lines. We identified 8 new single-nucleotide polymorphisms (SNPs) and 14 novel mutations (or very rare SNPs) that specifically present in human cancers. These mutations/SNPs are distributed in the regions of pri-, pre- and even mature miRNAs, respectively. Importantly, whereas most of the mutations did not exert detectable effects on miRNA function, a G --> A mutation at 19 nt downstream of miRNA let-7e led to a significant reduction of its expression in vivo, indicating that miRNA mutation could contribute to tumorigenesis. These data suggest that further screening for genetic variations in miRNA genes from a wide variety of human cancers should increase the discovery and identification of molecular diagnostic and therapeutic targets and complement the mutation analysis of consensus coding sequences in human cancers.
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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 | 0.001 |
| 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".