Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of noncoding small RNAs that regulate gene expression by base pairing with target mRNAs at the 3'-terminal untranslated regions (3'-UTRs), leading to mRNA cleavage or translational repression. Single-nucleotide polymorphisms (SNPs) located at miRNA-binding sites (miRNA-binding SNPs) are likely to affect the expression of the miRNA target and may contribute to the susceptibility of humans to common diseases. We herein performed a genome-wide analysis of SNPs located in the miRNA-binding sites of the 3'-UTR of various human genes. We found that miRNA-binding SNPs are negatively selected in respect to SNP distribution between the miRNA-binding 'seed' sequence and the entire 3'-UTR sequence. Furthermore, we comprehensively defined the expression of each miRNA-binding SNP in cancers versus normal tissues through mining EST databases. Interestingly, we found that some miRNA-binding SNPs exhibit significant different allele frequencies between the human cancer EST libraries and the dbSNP database. More importantly, using human cancer specimens against the dbSNP database for case-control association studies, we found that twelve miRNA-binding SNPs indeed display an aberrant allele frequency in human cancers. Hence, SNPs located in miRNA-binding sites affect miRNA target expression and function, and are potentially associated with 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 | 0.002 |
| 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) | 0.002 | 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".