Abstract 142: The EGFR signaling modulates in mesenchymal stem cells the expression of microRNAs involved in the interaction with breast cancer cells
Bibliographic record
Abstract
Abstract Introduction. We previously demonstrated that the activation of the epidermal growth factor receptor (EGFR) in mesenchymal stem cells (MSCs) modulates the expression of a number of genes coding for secreted proteins that promote breast cancer cell progression. As different microRNAs (miRNAs) have been shown to be involved in the cross-talk between MSCs and tumor cells, we hypothesized that the EGFR might regulate in MSCs the expression of miRNAs that might affect breast cancer progression through paracrine circuits. To this aim, we analysed the whole small RNA transcriptome of MSCs stimulated or not with transforming growth factor α (TGFα), one of the main ligands of the EGFR. Materials and methods. Small RNA sequencing was performed using the SOLiD 5500xl System. The DAVID (Database for Annotation, Visualization and Integrated Discovery) database was used for pathway analysis. Exosomes containing miRNAs were isolated from conditioned media with the ExoQuick reagent. MiRNA expression levels in conditioned medium were analyzed by Real Time PCR. Anchorage-dependent growth assays and Boyden-chamber based colorimetric migration assays were used to assess the proliferation and the migration of breast cancer cells, respectively. Results. Following small RNA sequencing, we identified 36 miRNAs differentially expressed between untreated and TGFα-treated MSCs with a fold change (FC) <0.56 or FC ≥1.90 (Confidence Interval, CI, 95%) and a threshold of sequence counts≥150 in at least one sample. In particular, 18 miRNAs resulted up-regulated with FC≥1.90 and 18 miRNAs down-regulated with FC<0.56. Pathways analysis of the target genes of the differentially expressed miRNAs revealed a significant enrichment in different KEGG pathways involved in cancer development and progression, including TGFβ signaling, focal adhesion, Rap1 signaling, Hippo signaling, mTOR and RAS signaling. To study miRNAs involved in the TGFα-mediated interaction between MSCs and breast cancer cells, we collected conditioned media from MSCs and analysed the expression levels of selected miRNAs. Real Time PCR analysis revealed the presence of several miRNAs in the conditioned medium from MSCs, including miR-23c, miR379-3p and miR-432-5p. The biological activity of the identified miRNAs was explored in a panel of breast cancer cell lines. MiR-23c was found to regulate the proliferation and migration of triple negative MDA-MB-468 and MDA-MB-231 breast cancer cells. Conclusion. Collectively, our data suggest that the EGFR signaling regulate in MSCs a wide number of miRNAs that might be involved in breast cancer progression, providing novel information on the mechanisms that regulate the MSC-tumor cross-talk. Citation Format: Marianna Gallo, Marianeve Carotenuto, Cristin Roma, Francesca Bergantino, Pasqualino de Antonellis, Nicola Normanno, Antonella De Luca. The EGFR signaling modulates in mesenchymal stem cells the expression of microRNAs involved in the interaction with breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 142.
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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 | 0.000 |
| 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".