A Bioinformatic Pipeline Places STAT5A as a miR-650 Target in Poorly Differentiated Aggressive Breast Cancer.
Eric López-HuertaEzequiel M Fuentes-PananaPublished in: International journal of molecular sciences (2020)
Breast cancer (BRCA) is a leading cause of mortality among women. Tumors often acquire aggressive features through genomic aberrations affecting cellular programs, e.g., the epithelial to mesenchymal transition (EMT). EMT facilitates metastasis leading to poor prognosis. We previously observed a correlation between an amplification of miR-650 (Amp-650) and EMT features in BRCA samples isolated from Mexican patients. In this study, we explored the cBioportal database aiming to extend that observation and better understand the importance of Amp-650 for BRCA aggressiveness. We found that Amp-650 is more frequent in aggressive molecular subtypes of BRCA, as well as in high grade poorly differentiated tumors, which we confirmed in an external miRNA expression database. We performed differential expression analysis on samples harboring Amp-650, taking advantage of gene target prediction tools and tumor suppressor gene databases to mine several hundreds of differentially underexpressed genes. We observed STAT5A as a likely putative target gene for miR-650 in aggressive poorly differentiated BRCA. Samples with both Amp-650 and low expression of STAT5A had less overall survival than samples with either or none of the alterations. No target gene has been described for miR-650 in BRCA, thus, this bioinformatic study provides valuable information that should be corroborated experimentally.
Keyphrases
- cervical cancer screening
- poor prognosis
- long non coding rna
- cell proliferation
- genome wide identification
- protein kinase
- breast cancer risk
- copy number
- genome wide
- high grade
- epithelial mesenchymal transition
- long noncoding rna
- public health
- transcription factor
- ejection fraction
- newly diagnosed
- dna methylation
- prognostic factors
- young adults
- machine learning
- coronary artery disease
- big data
- risk factors
- deep learning
- childhood cancer
- pregnancy outcomes
- patient reported outcomes