ArticleGenes2022
Structural Analysis of microRNAs in Myeloid Cancer Reveals Consensus Motifs.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- miR-16-5p enhances sensitivity to RG7388 through targetingCancer drug resistance (Alhambra, Calif.) · 2023Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs that function in post-transcriptional gene silencing and mRNA regulation. Although the number of nucleotides of miRNAs ranges from 17 to 27, they are mostly made up of 22 nucleotides. The expression of miRNAs changes significantly in cancer, causing protein alterations in cancer cells by preventing some genes from being translated into proteins. In this research, a structural analysis of 587 miRNAs that are differentially expressed in myeloid cancer was carried out. Length distribution studies revealed a mean and median of 22 nucleotides, with an average of 21.69 and a variance of 1.65. We performed nucleotide analysis for each position where Uracil was the most observed nucleotide and Adenine the least observed one with 27.8% and 22.6%, respectively. There was a higher frequency of Adenine at the beginning of the sequences when compared to Uracil, which was more frequent at the end of miRNA sequences. The purine content of each implicated miRNA was also assessed. A novel motif analysis script was written to detect the most frequent 3-7 nucleotide (3-7n) long motifs in the miRNA dataset. We detected CUG (42%) as the most frequent 3n motif, CUGC (15%) as a 4n motif, AGUGC (6%) as a 5n motif, AAGUGC (4%) as a 6n motif, and UUUAGAG (4%) as a 7n motif. Thus, in the second part of our study, we further characterized the motifs by analyzing whether these motifs align at certain consensus sequences in our miRNA dataset, whether certain motifs target the same genes, and whether these motifs are conserved within other species. This thorough structural study of miRNA sequences provides a novel strategy to study the implications of miRNAs in health and disease. A better understanding of miRNA structure is crucial to developing therapeutic settings.
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Registered trials
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