Abstract 117: The role of microRNAs in mammary tumorigenesis
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) are small non-coding regulatory RNA molecules that act post-transcriptionally on mRNA to regulate the expression of most cellular genes. Many miRNAs are aberrantly expressed in tumor tissue, acting either as tumor suppressors or oncogenes, and their expression has been implicated in the development of breast cancer. Using a transgenic mouse model of mammary tumorigenesis where transgenic overexpression of IGF-IR drives mammary tumor development (MTB-IGFIR transgenic mice), a miRNA array was performed to study the expression level of various miRNAs in mammary tumors compared to wild-type mammary tissue. The miRNA array revealed that 43 miRNAs were significantly elevated by a minimum of 2-fold in tumor tissue, while 45 miRNAs were significantly downregulated by at least 2-fold in tumor tissue when compared to normal mammary tissue. The expression differences of miRNAs (miR-24, miR-31, miR-183, miR-200a*, miR-200b, miR-200c, miR-210, miR-378 and miR-574-3p) were confirmed in tumor and normal mammary tissues using qRT-PCR. These miRNAs were chosen as they displayed some of the largest expression differences between the mammary tumors and normal mammary tissue, and several have been implicated in tumor progression and metastasis. In particular, miR-200c and miR-31 levels are inversely related to metastasis, whereas miR-183 may be involved in reducing cellular migratory properties. Next, the expression of these miRNAs was examined in a tumor cell line (RM11A) generated from a MTB-IGFIR mammary tumor. We found that miR-31, miR-183, miR-200c, miR-210, and miR-378 were expressed in the RM11A cells. We then used synthetic miRNA precursors that mimic the mature form of miRNAs and inhibitors to overexpress and knockdown, respectively, the levels of these miRNAs in RM11A cells. Overexpression of these five miRNAs was optimized using qRT-PCR with TaqMan primers to increase expression levels between 30 and 400 fold, as this magnitude of change was observed in the miRNA array. To determine the impact of miRNAs on mammary tumorigenesis, assays for proliferation (Ki-67 immunofluorescence), migration (scratch wound and Boyden chamber), and cell survival (JC-1 fluorescence) will be performed. To date we have observed that when compared to negative controls, overexpression of miR-210 significantly increased proliferation by approximately 1.5- fold, while the overexpression of miR-31, miR-183, and miR-378 did not significantly affect proliferation. Functional assays performed in the RM11A cell line will be repeated in a second mammary tumour cell line (4T1) to compare and contrast the importance of these miRNAs in another cell line. Discovering the differential expression of miRNAs and their effects on proliferation, migration, and survival in the RM11A and 4T1 cells will enhance our understanding of the function of miRNAs in human breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 117. doi:10.1158/1538-7445.AM2011-117
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".