Abstract 1458: The precursor miR-138/2, but not miR-138/1, targets p53 mRNA and contributes to the acquisition of melanoma metastatic phenotype: Are the miRNAs precursors important to direct mature miRNA to mRNA targets
Bibliographic record
Abstract
Abstract MiRNAs regulate pathways associated with differentiation, proliferation, apoptosis, among others, miRNAs missregulation may contribute to malignant transformation. Although melanoma is one of the rarest dermatological cancers, it is responsible for the greatest number of skin cancer-related deaths. In that context, our aim is to identify miRNAs that could be involved with melanoma progression. Using a cellular murine model of melanoma development, we identified the miR-138 as an interesting target of studying. In this model, four cell lines (melan-a, 4C, 4C11- and 4C11+) mimics the distinct steps of human melanoma genesis. miR-138 is codified from two different regions on the genome and they were named according to their origin. miR-138/1 is coded from chromosome 9 in mouse (corresponding to 3, in humans) while miR138/2 is coded from chromosome 8 in mouse (equivalent to 16, in humans). Despite the mature sequence of these two miRNAs being exactly the same, the flanking regions that form the precursors are distinct. miR-138 coded from chromosome 8 but not from chromosome 9 promotes cells to acquire a more aggressive phenotype. When we transfected the tumorigenic but non-metastatic cell line 4C11- with the genomic DNA corresponding to miR-138/2, it promotes the increase of proliferation, migration, colony formation and resistance to anoikis by this cell line compared to its untransfected counterpart 4C11-. In vivo assays showed that 4C11- miR-138/2 is able to form fast-growing tumors and pulmonary metastasis. None of these phenotypic changes was observed in 4C11- cells overexpressing the miR-138 coded from chromosome 9. We also validated p53 (rarely mutated in melanomas) as a target of miR-138 coded from chromosome 8, but not from chromosome 9. Therefore, we hypothesized that the sequence of precursor miRNA (pre-miR) could be involved with the direction of mature miRNA to the mRNA target. Moreover, EZH2, validated as a target of miR-138, is downregulated only in 4C11- cells overexpressing the miR-138/1, emphasizing even more the importance of the precursor sequence in the regulation of the mRNA target. miR-138 expression in melanocytic lesion were evaluated as well. We observed increased expression of miR-138 in primary and metastatic human melanoma samples related to benign nevi. Therefore, in this work we showed that miR-138 may contribute to melanoma genesis and is capable to regulate the expression of p53. Moreover, we suggest for the first time that the precursor sequence of miRNA can direct the mature miRNA to mRNA targets. Supported by CNPq and FAPESP Citation Format: Adriana Taveira Da Cruz, Aline Hunger, Genevieve Paré, Dulcie Lai, Fabiana Henriques Machado Melo, Bryan Eric Strauss, Victor Tron, Miriam Galvonas Jasiulionis. The precursor miR-138/2, but not miR-138/1, targets p53 mRNA and contributes to the acquisition of melanoma metastatic phenotype: Are the miRNAs precursors important to direct mature miRNA to mRNA targets. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1458. doi:10.1158/1538-7445.AM2014-1458
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".